N = 95 university students  built new study habits over a period of 6 weeks with manipulated context stability. One group was instructed to constantly vary the context of their habit repetitions by changing rooms and times and the other group was instructed to keep the context of habit performance stable. In Study 2, N = 308 habits  from N = 218 users of a published habit building app were analyzed without manipulating but measuring context stability. We found the same pattern in both datasets: Context stability predicted more automaticity and higher habit repetition goal attainment. We also found that the effect of context stability on habit repetition goal attainment was partially mediated by automaticity in both datasets. These results show that context does not only act as a trigger for habit instigation but also has an ongoing effect on habit execution.In this paper, we investigate the effects of context stability on automaticity and goal attainment in intentional habit building. We used hierarchical growth curve modeling and multilevel mediation to test our hypotheses on two datasets. In Study 1,  You may have expected a quote by some famous person here, but you find a small demonstration of how something in the wrong context can draw attentional resources, cause surprise or even trigger mental friction.Marco Stojanovic-We cannot escape context. Everything that is, exists in a certain context. Be it a thought, a word, or a behavior. The human brain evolved to adapt to frequently encountered contexts by automatically activating behavioral responses that yielded beneficial results in the past . We ofteContext, frequency, and automaticity are central concepts in modern habit research. Classic, simplistic definitions of habit equaled frequency of behavior with habit . Why is In past habit research, context stability was inferred through instructions to keep it stable e.g., , multiplWhen investigating habit formation, automaticity is typically the dependent variable, which grows over time by habit repetition , 2021. Azeitgeber) is light exposure, which influences melatonin levels and thus the circadian rhythm  as well as on the interindividual level (Level 2). A context stability factor that is applied to subjects should thus influence their average automaticity scores (Level-2-effect) and daily fluctuation in context stability should influence automaticity within subjects at that point in time (Level-1-effect).H1a: Context stability increases automaticity on Level 1.H1b: Context stability increases automaticity on Level 2.So, it would be fair to assume that context stability should support behavioral automatization. Furthermore, we already have evidence that more automaticity leads to less motivational interference during habit performance , 2021. TH2a: Context stability increases HRGA on Level 1.H2b: Context stability increases HRGA on Level 2.We further hypothesize that the effect of context stability on HRGA is mediated by automaticity.H3: Automaticity mediates the effect of context stability on HRGA.H1a, H2a, and H3 are tested with two datasets, respectively, for better generalizability. Thus, the corresponding models will have a parameter for each dataset. The first dataset (Study 1) stems from a longitudinal diary study in which students built new study habits. In the first dataset, context stability was manipulated between subjects. With this context stability group factor, H1b and H2b are tested. The second dataset (Study 2) contains real user data from a habit building app . Other vN = 95 university students  participated in return for course credit. Participants with at least 12 habit repetitions were registered for an additional lottery for one of 10 online coupons worth 50 Euros each (~55 U.S. dollars). Participants who did not log at least one habit repetition were excluded from the data analysis. The participants were recruited in psychology lectures of two German universities and social media groups. 90% of the participants studied psychology and the remaining 10% something else.The data were collected with Qualtrics .1 The stContext stability was manipulated after the habit definition process by different instructions on where and when to perform the habit. Participants were instructed to do their habit at the same place (stable context) or to do their habit in different places such that at least the last five places would be different (variable context). Further, they were instructed to do their habit at the same time in the course of the day (stable context) or at different times in the course of the day such that at least the last five points in time in the course of the day would be different (variable context). Participants were reminded to keep their context stable/variable in this way in the daily habit repetition survey. All participants were instructed to perform their habit daily and answer the event sampling survey directly afterwards. Thereafter, participants answered items about constructs not relevant for this study.0 = does not apply at all to 10 = applies perfectly). HRGA was measured with one item  and answered on a 0–100% scale. Automaticity was measured with the Self-Report Behavioral Automaticity Index  according to After the pretest, the 6-week long event sampling phase started. The event sampling survey was accessible anytime via an individual link. Automaticity and context stability were measured on an 11-point scale  nested in persons (Level 2). We modeled growth curve models with multilevel regressions  using IBu0), random slopes (u1), and the covariance of the random intercepts and the random slopes ) were specified with an unstructured covariance structure at the start of the estimation process.The growth curves (Model 1–6) describe the change of automaticity and HRGA over time . Random intercepts  by conducting a multilevel mediation analysis with the MLmed Beta 2 IBM SPSS macro by Rockwood , plus the individual slope bp1,, which is the average slope for the effect of habit repetition  of the whole sample b10 plus the individual deviation from that slope up1, (3), times habit repetition plus habit repetition squared  with a fixed beta (b2), which adds a quadratic trend, plus errort,p. We found in preliminary analyses that habit pausing  had a negative influence on automaticity (both datasets) and potentially on HRGA (second dataset), which is why we also included habit pausing measured as days since the last habit repetition as a control variable in Model 1–6. For Model 2–6, bp0, and bp1, are the same as in Model 1.Model 1 (1) predicts automaticity at time t for person p with a random intercept In Model 2 (H1a), we added the Level 1 predictor context stability to Model 1, resulting in In Model 3 (H1b), we added the dummy coded group variable context stability group  as a Level 2 predictor to Model 1, resulting in t for person p with habit repetition , habit pausing, and automaticity plus errort,p.Model 4 predicts HRGA at time In Model 5 (H2a), we added the Level 1 predictor context stability to Model 4, resulting in In Model 6 (H2b), we added the group variable context stability group as a Level 2 predictor to Model 4, resulting in N = 2,482 habit repetitions of N = 95 participants with an average of M = 26.13 (SD = 12.20) logged habit repetitions per participant. Concerning pauses between habit repetitions , with n = 1,780 (74.6%) the majority of habit repetitions were done without pause. There was no association of average context stability and average habit pausing in either group, rstable = −0.11, p = 0.49, rvariable = −0.20, p = 0.18. See The dataset contained d = 1.33, t(−18.80) = 4.21, p < 0.001 , t(2285.45) = −1.26, p = 0.209. Neither age, b = −0.068 (SE = 0.039), t(92.57) = −1.76, p = 0.082, nor gender, b = 0.593 (SE = 0.514), t(92.54) = 1.15, p = 0.251, had an influence on automaticity.To model habit formation over time, automaticity was predicted with habit repetition as the time variable with random slopes and random intercepts. Then, habit repetition was squared and added as a predictor to model decreasing automaticity gains in higher habit repetition ranges. Finally, the time since the last habit repetition in days, habit pausing, was added as a control variable see , 2021 anb = 0.073 (SE = 0.013), t(2340.98) = 5.62, p < 0.001.To test H1a, the influence of Level 1 context stability on automaticity, context stability was added to the automaticity baseline model (Model 1) as a predictor, resulting in Model 2. As expected, context stability predicted automaticity, b = −0.411 (SE = 0.405), t(93.62) = −1.01, p = 0.313.To test H1b, the influence of the context stability manipulation on automaticity, context stability group was added to the automaticity baseline model (Model 1) as a dummy coded Level 2 predictor, resulting in Model 3. Contrary to our expectation, the group factor did not predict automaticity, b = 4.360 (SE = 0.346), t(1277.37) = 12.59, p < 0.001. Habit repetition, b = 0.058 (SE = 0.099), t(53.27) = 0.59, p = 0.561, and habit pausing, b = −0.370 (SE = 0.334), t(2349.01) = −1.11, p = 0.268, did not predict HRGA. Then, to test H2a, we added context stability (see b = 1.816 (SE = 0.232), t(2180.30) = 7.82, p < 0.001. Finally, to test H2b, we added the group factor context stability group as a Level 2 predictor to the HRGA baseline model (Model 4), resulting in Model 6. Contrary to our expectation, the group factor did not predict HRGA, b = 0.076 (SE = 4.333), t(87.22) = 0.02, p = 0.986.H2a-b aim at testing context stability as a predictor for HRGA in the habit formation process. First, we defined a HRGA baseline model with habit repetition, habit pausing, and automaticity , p < 0.001. We found no such effect on Level 2 (between-indirect effect) with averaged Level 1 data, b = 0.330 , p = 0.170. The CIs for the indirect effects were estimated using Monte Carlo simulations with 10.000 samples. See H3 aims at testing automaticity as a mediator of the effect of context stability on HRGA. We conducted a multilevel mediation analysis with the MLmed Beta 2 macro by Rockwood (downloaded from b = 0.178 (SE = 0.018), t(1175.73) = 10.03, p < 0.001, but not in the variable context group, b = −0.012 (SE = 0.019), t(1103.68) = −0.64, p = 0.524. In the stable context group, habit repetition squared predicts automaticity, b = −0.0011 (SE = 0.0003), t(1184.10) = −3.48, p < 0.001, which it did not in the variable context group, b = 0.0005 (SE = 0.0004), t(1101.51) = 1.06, p = 0.290. In the main analysis above with pooled groups, habit repetition squared was not significant (see b = 4.902 (SE = 0.322), t(1216.27) = 15.25, p < 0.001, but not in the variable context group, b = −0.351 (SE = 0.348), t(1141.80) = −1.01, p = 0.312.To investigate the unexpected disparity between context stability affecting automaticity and HRGA on Level 1 but not on Level 2, we fitted Model 2 (automaticity) and Model 5 (HRGA) separately to the stable and the variable context group. The results are summarized in b = 0.626 , p < 0.001, but not in the variable context group, b = 0.009 , p = 0.926. On Level 2 (between-indirect effect) with averaged Level 1 data, there was neither an effect in the stable context group, b = 0.414 , p = 0.617, nor in the variable context group, b = 0.543 , p = 0.457. See Further, we also repeated the multilevel mediation analysis for the stable and variable context group separately with the same specifications as in the main analysis. On Level 1 (within-indirect effect), automaticity mediated the influence of context stability on HRGA in the stable context group, As to our knowledge, this study is the first in which context stability was actively manipulated to then track its influence on the development of new habits. As expected, we found that habit building worked better in stable contexts as indicated by higher automaticity scores. However, as the auxiliary analyses show, context stability only seems to predict automaticity on higher levels past a certain threshold: In the stable context group, higher context stability on Level 1 predicted higher automaticity, but it had no effect in the variable context group. This effect, rooted in the stable context group, was strong enough to spill over into the pooled analysis with both groups, which explains why context stability on Level 1 could become a significant predictor for automaticity in the main analysis, while the Level 2 context stability group factor—which resembles a considerable difference of average Level 1 context stability—would have no such effect. In generally stable contexts, it matters how stable it is precisely in a given day and small deviations will tax automaticity. In generally variable contexts with new times in the flow of the day and new places, there is no association with the behavior and thus there seems to be no association of automaticity and context.On the other hand, it needs to be noted that even with the imposed friction of varying contexts, participants of the variable context group were able to build considerable degrees of automaticity by repetition. One needs to take into account however, that participants knew they were in a controlled study with instructions to perform their defined habits in certain ways and had course credit on the line for participation. We could not control if and how participants reminded themselves to do their habits. less total habit repetitions. This potentially confounding deliberation factor should be considered when interpreting the reported results which include data from the variable context group.Another source of potential systematic error variance is the instruction for participants of the variable context group to deliberately switch contexts before their study performance. This instruction triggers planning of the study behavior that lies before them as conscious decisions on the time and place for each repetition need to be made before each execution, which could have created an intentional focus on the task at hand, potentially resulting in increased situational intention and deliberation concerning the target behavior. As Furthermore, we found that when habit repetitions were performed in stable contexts, HRGA was higher. Put differently, habits—respectively the behaviors participants set out to make a habit at the beginning of the study—were quit more often before completion when performed in varying contexts. The auxiliary analyses provide a more precise picture again. Analogously to automaticity, context stability on Level 1 predicted HRGA in the stable context group, but not in the variable context group, again resulting in a weaker spillover effect in the main analysis with pooled data. We found this effect to be partially mediated by automaticity. Again, the auxiliary analyses revealed that this mediation can only be found in the stable context group. However, the automaticity-HRGA path is significant for both groups. Irrespective of how automaticity is obtained, it seems to increase HRGA. Automaticity is associated with less want conflicts  and less motivational interference  during hWith study 2, we want to stress test our hypotheses with real user data of a habit building app that was developed and published as a product of earlier research .N = 2,368 habit repetitions (Level 1) of N = 218 users, who defined and tracked a total of N = 308 habits (Level 2) from 17.09.18–04.02.21. Habits with less than two habit repetitions as well as habits from the first author had previously been removed from the dataset. The data stems mainly from participants from Germany (25.3%), India (21.4%) and the United States(13.4%). The rest of the data was logged from countries all over the world with each contributing less than 4% of the total. 48.6% of users were female. 50% of the users were in the age group of 18–24 years, 31.8% were in the age group of 25–34 years, 12.8% were in the age group of 35–44 years, and 5.4% were in the age group of 45–54 years.Here, we analyzed user data from an app we published in the AppStore  long-term goal they want to achieve with the habit. When defining the estimated (3) duration, users were not constrained to a certain time as in Study 1, but the app recommended a range of 3–60 min. Users were informed that they should stop their habit only after having reached their habit action goal (Step 6) and not after the defined duration. Defining a duration was supposed to help the users planning their habit and integrating it more easily in the flow of their day. Excluding 20 outlier habits with over 120 min as a duration goal, the average defined duration was durationM = 23.72 (durationSD = 23.39) with a median of durationMdn = 15. Then, users defined the (4) context for their habit by specifying a time in the course of the day  and a physical environment . Then, users defined the (5) habit action  by specifying the action they would perform during the habit . Users were not restricted to study habits as in Study 1. Next, users defined a (6) habit action goal , which would render a current habit repetition completed after achieving . After that, users were asked to define a short version of their habit, which they should perform instead of the normal habit if they were under time pressure or feel they would not be able to perform their full habit for any other reason. The so-called (7) emergency habit action and the (8) emergency habit action goal were defined analogously to steps 5 and 6 but with the instruction to make it considerably shorter and be able to perform it anywhere if possible. Two hundred forty-eight (10.5%) of the 2,368 analyzed habit repetitions were marked as emergency habit repetitions. As emergency habit repetitions were used sparingly and are similar to the normal version of the habit, they were treated as normal habit repetitions in data analysis. In the (9) frequency-step, users chose the weekdays on which they wanted to perform the habit and were thus not instructed to perform their habit daily as in Study 1. However, the app recommended setting the goal of performing the habit daily, which was done in 258 (83.8%) of the 308 analyzed habits. As a second part of this step, the users chose a time on which they wanted to be reminded of their new habit on the chosen weekdays. Finally, the app showed the users a summary of the habit and adaptions could be made for each step before saving the new habit.The habit definition process of the AppStore app “Grow - Habit Builder” was similar to the one of Study 1 in the stable context condition with some additions and usability modifications. In a first step, the users gave their habit a (1) 0 = strongly disagree to 10 = strongly agree) with 10 partially adapted items from the SRHIHaving finished the habit definition process, users could log data for their habit after each habit repetition. The event sampling process was the same as in Study 1, however with an adaption for efficiency and usability. The app tracks several scales, which are not all relevant to this article. In order to keep break-off rates low, which rise with questionnaire length in mobile surveys , we adapn = 166; 76%). We modeled the user data with Level 1 and Level 2 as in Study 1 and then performed exactly the same calculations.We fitted the same models to this dataset that we also fitted to the dataset of Study 1 apart from Model 3 (H1b) and 6 (H2b), as they contain the group variables for context stability which do not exist in the dataset of Study 2. Here, habit repetitions (Level 1) are nested in habits (Level 2), which are nested in persons (Level 3). However, most users in the analyzed dataset only logged data for one habit  logged habit repetitions per habit. Most of the habits were related to studying , physical exercise  and mental focusing practices . Concerning habit pausing, with n = 1,328 (64.5%) the majority of habit repetitions were done without pause, n = 315 (15.3%) were done with a pause of 1 day, n = 137 (6.7%) were done with a pause of 2 days, and n = 280 (13.6%) were done with a pause of 3 days or more. Over all N = 2,368 habit repetitions, mean HRGA was M = 83.41% (SD = 28.20) with a median of Mdn = 100%. Over n = 1,720 habit repetitions (context stability was not measured after the first three repetitions), context stability was M = 80.88% (SD = 25.18) with a median of Mdn = 92%. There was no association of average context stability and average habit pausing, r = 0.08, p = 0.26.The dataset contained Retesting H1a, we performed the same analyses as in Study 1. First, we tested the automaticity baseline model representing habit formation over time with this second dataset. Then, we added context stability as a predictor to retest H1a.b = −0.0014 (SE = 0.0001), t(1983.24) = −13.46, p < 0.001. Furthermore, we found that the random slope was redundant, which is why we removed it from the model , t(1714.00) = 8.88, p < 0.001.To retest H1a, the influence of context stability on automaticity, context stability was added to the automaticity baseline model (Model 1) as a Level 1 predictor, resulting in Model 2. As expected, context stability predicted automaticity, b = 1.820 (SE = 0.209), t(1634.65) = 8.73, p < 0.001. Then, we added context stability (see b = 0.210 (SE = 0.020), t(1672.42) = 10.70, p < 0.001.Retesting H2a, we performed the same analyses as in Study 1. First, we tested the HRGA baseline model with habit repetition and automaticity as predictors with this second dataset , p < 0.001, as well as on Level 2 (between-indirect effect) with averaged Level 1 data, b = 0.087 , p < 0.01. The CIs for the indirect effects were estimated using Monte Carlo simulations with 10,000 samples. See Retesting H3, we performed the same analyses as in Study 1. As expected, automaticity mediated the influence of context stability on HRGA. We found this effect on Level 1 (within-indirect effect), In Study 2, we found corroborating empirical evidence for H1a, H2a and H3. As expected, stable habit contexts predicted higher automaticity and higher HRGA. Further, we could replicate the mediation of the effect of context stability on HRGA via automaticity. In Study 2 however, we also found both direct effects (context stability on automaticity and automaticity on HRGA) and the total effect as well as indirect effect (context stability on HRGA) on Level 1 and on Level 2. The expected context stability effects emerged even in a fuzzy, natural, real-life setting with people from all over the world with a balanced gender ratio, who intentionally build new habits of many different kinds with minimal, standardized, and automatic guidance by the app software in a self-regulated manner without direct incentives like course credit. Thus, Study 2 provides an indication for generalizability of the context effects in habit building we proposed in our hypotheses.The goal of this paper was to test how context stability affects the development of new, beneficial habits. Both studies provide evidence for the hypotheses that context stability improves automatization and enhances habit performance as indicated by higher HRGA. With the manipulation of context stability by having participants constantly switch places and times in Study 1, it could be shown that these effects vanished if context stability only fluctuates on a generally very low level. Only when participants were instructed to keep their contexts stable (same place and same time), which was the case for the stable context group in Study 1 and all app users of Study 2, the context effects emerged. When people performed their habits in more stable contexts, they reported higher automaticity scores and attained higher degrees of their set habit repetition goals. The effect of context stability on HRGA was partially mediated by automaticity in both studies.Context seems to be more than just a cue for habit instigation. Here, we observed a positive lingering effect of performing one’s habit in the “correct” context, meaning in the context in which the habit behavior has been encoded in previous repetitions.Analogous to the distinction We know that one should keep the context stable when building new habits. We defined context as a combination of the physical environment and the time in the flow of the day and measured the reported stability which was perceived by the participants. But what are the defining factors that have to be perceived to trigger the perception of context? Is daylight as a zeitgeber more important than the preceding action of just having finished lunch? Is more information derived from visual information about the physical environment than from other variables like temperature? Does the impact of such context information vary systematically over persons, is it idiosyncratic or habit-dependent? There is evidence that different features of context such as the physical location, time of day, mood, and the presence of particular other people can be more or less important for guiding habitual behaviors of different kinds , albeit with physical location having a dominating effect . The resWhen intentionally building good habits with specifically defined goals, motivation and intention are necessary in the beginning to build automaticity. To better automize a beneficial habit, make sure to keep the context stable. Here are two important remarks on this.First, make sure to define context precisely. As we saw in Study 1, it can already heavily disrupt perceived context stability if one just defines a very general context such as “at home after school.” Defining the exact place and the preceding action in the flow of the day seem to be viable heuristics to significantly increase context stability and thus improve habit development and goal attainment.Secondly, consider the inherent natural variability of the chosen context. For example, a café might vary regularly in how crowded it is, which table one gets on a given day, what background music is playing and whether the adjacent table is occupied by a loudly laughing young couple or a stressed but silent PhD student. The kitchen table at home might have a lower contextual variance and would thus bear less potential to disrupt habit execution. Superficially, both contexts—“at my kitchen table” and “at my favorite café”—seem to be equivalently suitable for habit building, but the difference in their context variance makes one superior to the other. This reasoning can analogously be applied to the time dimension of context. The context variance might be systematically lower in the morning and the evening, but vary more heavily in the middle of the day.Even though the data from two distinct datasets converged into the same, expected pattern, one should be careful before generalizing the results of this paper to other domains with different conditions. Both datasets consist of mainly young people who intended to build a useful habit. In Study 1, most participants were extrinsically incentivized by course credit and a lottery for an online coupon. Study 2 was constrained to iPhone users. All analyzed habits were defined in a similar way following the structured habit definition process. Hence, all habits share certain attributes that other habits might not have and which might influence the applicability of the presented results. For example, all habits were planned out in advance, should by definition only be performed once a day and were connected with individual long-term goals. Habits of other archetypes, such as smartphone checking habits that get triggered about every 12 min , or mentIn this paper, we quantified the influence of context stability in the process of habit acquisition. Keeping the context stable supports behavioral automatization, i.e., habit development. The presented evidence suggests that context does not only act as a trigger to initiate habits, but has a lingering effect with context stability being conductive to a smooth habit execution with higher goal attainment. Having a clear specification of time and space of a habit that is to be built seems to be a good starting point to ensure context stability.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethik-Kommission der Universität Bielefeld. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.MS developed the idea and study design, created the iPhone application, conducted the data acquisition, performed the statistical analyses, and wrote the manuscript. AG and SF contributed to the final version of the manuscript and provided critical feedback, which helped shaping the theoretical foundation of the paper. SF supervised the project. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We memorize our daily life experiences, which are often multisensory in nature, by segmenting them into distinct event models, in accordance with perceived contextual or situational changes. However, very little is known about how multisensory boundaries affect segmentation, as most studies have focused on unisensory  segmentation. In three experiments, we investigated the effect of multisensory boundaries on segmentation in memory and perception. In Experiment 1, participants encoded lists of pictures while audio and visual contexts changed synchronously or asynchronously. After each list, we tested recognition and temporal associative memory for pictures that were encoded in the same audio-visual context or that crossed a synchronous or an asynchronous multisensory change. We found no effect of multisensory synchrony for recognition memory: synchronous and asynchronous changes similarly impaired recognition for pictures encoded at those changes, compared to pictures encoded further away from those changes. Multisensory synchrony did affect temporal associative memory, which was worse for pictures encoded at synchronous than at asynchronous changes. Follow up experiments showed that this effect was not due to the higher dimensionality of multisensory over unisensory contexts (Experiment 2), nor that it was due to the temporal unpredictability of contextual changes inherent to Experiment 1 (Experiment 3). We argue that participants formed situational expectations through multisensory synchronicity, such that synchronous multisensory changes deviated more strongly from those expectations than asynchronous changes. We discuss our findings in light of supportive and conflicting findings of uni- and multi-sensory segmentation. We temporally segment our everyday experiences into distinct episodic events in memory   effect of multisensory boundaries in previous studies was obtained from recognition memory, rather than associative memory. The enhanced recognition of items encoded at contextual boundaries (as opposed to those away from boundaries) may come with the trade-off of impaired temporal binding between items crossing a boundary in associative memory  or out of synchrony .We initially recruited 34 participants in the age range of 18–40 years from the academic environment of Maastricht University. Participants were recruited via social media platforms and were required to have at-home access to a computer or laptop, headphones, Internet access to download and install the experiment software and a quiet place without distractions (see below for further details). Participants who could not or were not willing to install the experiment software were excluded from participation in the study. Of the recruited sample, 24 participants  successfully installed and completed the experiment . All participants provided informed consent before participating in the experiment and were monetarily compensated. The study was approved by the ethical committee of the Faculty of Psychology and Neuroscience of Maastricht University.Due to national regulations in response to the COVID-19 pandemic during 2020–2021 in The Netherlands, we designed the experiment so that it could be completed at home. We programmed the experiment in Psychopy Peirce, , which hParticipants saw 12 lists of 36 visual items, which were randomly selected for each participant from a publicly available image set  without repetition. The audioscapes were generated from the soundtracks of publicly available online ambience videos and were chosen to be perceptually different from one another in terms of spectral distribution Fig. B and perAfter each list, item recognition and temporal order memory were tested in two separate tasks. In the recognition task, participants were presented with items that were either shown during the preceding list or not and had to indicate whether they thought they saw it during the preceding list (Old judgment) or not (New judgment). Items that were drawn from the preceding list were taken directly after a contextual change (boundary item) or three items away from a contextual change, provided that it did not overlap with a following contextual change  or from opposite sides of a contextual change (Across pair), with the number of items spanning between those of the pair being equal for Within and Across trials. Further, Across trials included pairs that were drawn across synchronous or asynchronous audiovisual context changes. Each trial ended upon button response, with a maximum response window of 6 s.In the temporal order memory test, participants saw pairs of items that were drawn from the preceding list and had to indicate which item of the pair was presented first. The items of a temporal order pair were presented on the left and right side of the center of the screen. The item that was presented first during the encoding phase was shown on the left in half of the trials, and shown on the right in the other half of the trials. The order of trials with left or right-sided presentation of the first encoded item was randomized across lists and participants. Pairs were either drawn from the same audiovisual context  and calculated statistical effects using a one-way repeated measures ANOVA with Context as within-subject factors. The same ANOVA model was used for analysis of the response times .p < 0.05). We report partial eta-squared, All ANOVA models included a full-factorial interaction term, using Type III sum of squares. Post hoc paired comparisons were conducted to parse significant main or interaction effects  compared to asynchronous boundary items  = 7.21, P = 0.013, F = 9.99, P = 0.004, F = 0.10, P = 0.76).Performance on the recognition task was relatively high across the three boundary types, with recognition accuracy around 0.9 and false alarm rates around 0.1  = 1.92, P = 0.18).The asynchronous boundary trials included two types of unisensory contextual changes: audio but no visual changes and visual but no audio changes. To explore whether these two asynchronous boundaries affected recognition performance differently, we conducted a post hoc paired samples T test and found no significant effect  = 1.60, P = 0.21), with comparable average response times for each of the three boundary items . Post hoc comparison between the two asynchronous boundary items yielded no significant difference  = 1.92, P = 0.18).Figure F = 8.97, P < 0.001, F = 2.68, P = 0.013, F = 12.11, P = 0.002, F = 3.61, P = 0.07, F = 1.86, P = 0.19).Figure F = 0.02, P = 0.98), with comparable average response times for each of the three boundary conditions . Post hoc comparison between the two asynchronous boundary types yielded no significant difference  = 0.29, P = 0.60).Figure We observed two main findings. First, we found that recognition memory was worse for items that were presented at multisensory boundaries compared to non-boundary items, regardless of multisensory synchrony. This finding suggests that synchronous boundaries affected recognition memory in a similar way as asynchronous boundaries. The superior performance for non-boundary items contrasts findings from previous movie segmentation studies  multisensory boundaries affected memory performance more than unisensory boundaries. Importantly, in contrast to Experiment 1, unisensory boundaries in Experiment 2 constituted contextual changes in one sensory context while the other context was entirely absent. The outcome of this experiment could further elucidate the results of Experiment 1. Finding a larger temporal memory impairment for multisensory as opposed to unisensory boundaries would indicate that the effect of a boundary on memory increases with increasing perceptual dimensionality of the contextual changes. This finding would then suggest that the results of Experiment 1 arose from increased perceptual dimensionality of the synchronous multisensory boundaries. However, finding that uni- and multi-sensory boundaries similarly affected temporal order memory would indicate that perceptual dimensionality per se cannot explain the results of Experiment 1.We recruited 20 new participants  from the same academic environment and using the same procedures. All participants gave informed consent before participating in the experiment and were monetarily compensated. The study was approved by the ethical committee of the Faculty of Psychology and Neuroscience of Maastricht University.A, playing the soundscapes without any frame color), visual only  or audio-visual . Second, in each context condition, a context changed after every six pictures. Further, in the audio-visual context, the frame changed color simultaneously with a change in soundscape. Figure We used a similar design as Experiment 1, with the following changes. First, pictures were presented in one of three contexts: audio only  = 19.57, P < 0.001, F = 11.42, P < 0.001, F = 4.41, P = 0.019, F = 10.40, P = 0.005, F = 6.97, P = 0.017, F = 19.63, P < 0.001, Encoding responses of one participant were not properly logged, leaving 19 datasets for the analysis of encoding responses. Figure minus A: F = 3.26, P = 0.088, minus AV: F = 7.48, P = 0.014, minus AV: F = 1.06, P = 0.32). Thus, audio and audiovisual boundaries led to slower boundary encoding times than did visual boundaries.The significant Position × Context interaction effect suggested that the slower encoding time associated to boundary items differed between contexts. To assess whether this was the case, we used post hoc comparisons to compare the slower boundary response times relative to the non-boundary response times between the various uni- and multi-sensory contexts. The relatively slower boundary response times (compared to non-boundary response times) for visual context was marginally significantly smaller than for auditory context  = 0.34, P = 0.99), suggesting that difficulty of non-boundary judgments was comparable between the uni- and multi-sensory contexts.Could this difference in response slowing be related to increased difficulty in rating audio contexts (uni- or multi-sensory) over visual contexts? To address this issue, we conducted a post hoc one-way repeated measures ANOVA on the non-boundary encoding times of the three sensory contexts. We found no significant effect  = 4.46, P = 0.048, d’ = 3.2 ± 0.14) compared to boundary items . We found no significant main effect for Context (P = 0.15) or the Position × Context interaction term (P > 0.9).As in Experiment 1, recognition accuracy was again relatively high, with accuracy around 0.9 for both boundary and non-boundary positions in the auditory, visual and audio-visual contexts. False alarm rates were below 0.1 across all conditions  = 9.35, P = 0.006, P = 0.45) or the Position × Context interaction (P = 0.42). These findings suggest better performance for middle items compared to boundary items, in line with the recognition accuracy results.A two-way repeated measures ANOVA for recognition response times revealed a significant effect for Position  = 20.59, P < 0.001, F = 0.75, P = 0.48) or the Event × Context interaction term  = 0.66, P = 0.53). Post hoc comparisons showed that hit rates for Within trials were significantly higher than those for the Across trials of all three sensory contexts  as within-subject factor. The main effect of Context was not significant (F < 1). To mimick the asynchronous condition of Experiment 1, we pooled the Across trials of the two unisensory conditions of Experiment 2 and compared it to the Across trials of the multisensory condition (one-way repeated measures ANOVA). We again found no significant effect (F < 1), suggesting that uni- and multi-sensory boundaries in Experiment 2 similarly affected temporal order accuracy.Experiment 1 showed that temporal order accuracy differed for item pairs crossing the synchronous and asynchronous boundaries. To test for a similar effect in Experiment 2, we first conducted a one-way repeated measures ANOVA of the F = 8.37, P = 0.009, Within trials (2445.1 ± 0.13 ms) than Across trials (2618.4 ± 0.12 ms). There was no significant effect for Context (P = 0.17) or the Event × Context interaction (P = 0.12).Finally, a repeated measures ANOVA of the temporal order memory response times showed a significant effect for Event  affecting perceptual boundary processing. That is, synchronous multisensory contextual changes were processed in a similar way as unisensory contextual changes. This finding is in agreement with a previous multisensory segmentation study of movie clips  from the same academic environment. Recruitment and exclusion criteria were the same as for Experiment 1. All participants gave informed consent before participating in the experiment and were monetarily compensated. The study was approved by the ethical committee of the Faculty of Psychology and Neuroscience of Maastricht University.th image (regular condition), or occurred irregularly after three, six or nine objects (irregular condition). Figure The experiment instructions and procedure were the same as in Experiment 2, including the overt encoding responses, the recognition and the temporal order memory tasks, but with the following exceptions. In Experiment 3, we only used the unisensory contextual conditions. Participants completed six visual-only and six audio-only context lists. In each unisensory condition, the contextual changes either occurred consistently and regularly after the 6F = 40.28, P < 0.001, F = 9.27, P = 0.011, F = 7.66, P = 0.018, F = 7.98, P = 0.017, F = 4.22, P = 0.065, P > 0.9, first-order interaction effect Ps > 0.6, second-order interaction effect P = 0.23). Thus, the slower encoding times for boundary items did not differ between regular and irregular contextual changes.Encoding response times were incomplete or missing for eight participants, possibly due to these participants accidentally pressing the wrong keys during encoding. To assess the effect of regularity of the contextual changes on encoding response times, we compared response times between the boundary and (the pooled) non-boundary items. A repeated measures ANOVA with Position , Context  and Regularity as within-subject factors yielded a significant main effect of Position  using a similar repeated measures ANOVA model yielded no significant main effects (Ps > 0.31) or interaction effects (Ps > 0.40). Average ± SE response times across all conditions was 1084.17 ± 45.67 ms, comparable to that of the previous experiments.F = 14.48, P = 0.0013, Figure Across trial data was pooled across the audio and visual modalities and analysed using a one-way repeated measures ANOVA with Regularity as within-subject factor. We found no significant effect (F < 1).To assess whether irregular unisensory boundaries affected temporal order memory differently than regular boundaries, Analysis of responses times (correct responses only) using a similar repeated measures ANOVA model yielded no significant main (Ps > 0.14) or interaction effect (Ps > 0.26). Average ± SE response times across all conditions was 2477.14 ± 183.59 ms, which was also comparable to the previous experiments.We found no evidence that temporal irregularity of boundary occurrence affected recognition or temporal memory, which suggests that temporal irregularity did not modulate boundary processing in terms of event model updating Zacks,  or encodOur findings can be summarized as follows. Synchronous multisensory contextual changes during encoding interrupted temporal associative memory processing more than asynchronous multisensory changes. This effect could not be explained by higher sensory dimensionality of multisensory over unisensory contexts, nor by the temporal irregularity of boundary occurrence during encoding. Instead, we argue that the synchronous multisensory boundaries constituted a stronger deviation from multisensory expectations than asynchronous boundaries. Further, the effect of multisensory synchronicity was not found in recognition memory, similarly to a previous multisensory segmentation study, in which asynchronous audio and visual tracks of movie clips did not alter memory performance with respect to synchronous movie clips  when they think a meaningful event has ended or a new one has started (Huff et al., A possible limitation of our study is that participants completed the task outside of a controlled laboratory. We aimed to control technical aspects of the study by using freely available and well-tested software that has been verified to run on all major operating systems without major differences in software performance (Bridges et al., In conclusion, we found that the synchronicity of multisensory contextual boundaries affected temporal order memory. Further, neither multisensory dimensionality nor the temporal regularity of boundary occurrence affected temporal or recognition memory. Our findings provide further insight into how contextual changes affect the organization of perceptual and mnemonic processing of our experiences.


We found associations between single gene mutations and ethnicity, with IDH1 mutations being more common in non‐Hispanics  and WT1 mutations more common in Hispanics . We also found an emerging trend towards adverse risk cytogenetics in Hispanic patients (p = 0.1796), as well as high risk fusions such as MLL‐r . There was no difference in overall survival (OS) between Hispanic and non‐Hispanics patients. When examining only newly diagnosed patients (n = 105), there was improved OS in Hispanics  by univariate analysis and equivalent OS by multivariate analysis (hazard ratio = 1.52 [95% CI = 0.74–3.15]). Hispanics with a driver mutation not class‐defining had improved survival compared to non‐Hispanics. Our study demonstrates significant genetic differences between Floridian Hispanics and non‐Hispanics, but no difference in OS in patients treated at an academic medical center.Hispanic patients have been reported to have an increased incidence of AML and possibly inferior outcomes compared to non‐Hispanics. We conducted a retrospective study of 225 AML patients (58 Hispanic and 167 non‐Hispanic) at two academic medical centers in Florida. Disease characteristics, cytogenetics, mutation profiles, and clinical outcomes were assessed. Hispanic patients were younger at presentation than non‐Hispanics ( AML survival is mitigated by a number of known risk factors, including inherent disease pathobiology, clinical characteristics, and age and sociodemographic factors, leading to a 5‐year median overall survival for adult patients ranging from 5%–70%. Increasingly, outcome in myeloid malignancies, and even disease definition, has been linked to discrete molecular mutations.Ethnic and racial disparities, attributable to both biologic and non‐biologic factors, have been described in several cancers, including AML. Compared to whites, for example, blacks have worse overall survival likely due to census tract disadvantage, affluence, and segregation  and to certain biologic characteristics such as karyotype and molecular mutations. The Hispanic population comprises the largest minority group in the United States, currently representing nearly 20% of the population (26% in Florida), and it is expected to approach 30% by 2060. Cancer is the leading cause of death among Hispanics in the US , who appear to develop distinct subtypes of leukemia: previous publications have revealed an increased incidence rate of B‐acute lymphoblastic leukemia (B‐ALL) and of acute promyelocytic leukemia (APL) , which hHispanics also may have an increased incidence rate of AML as a whole, possibly linked with inferior outcomes, compared to their non‐Hispanic counterparts . This fi22.1Newly presenting patients diagnosed with AML per the 2016 World Health Organization at two cancer centers ] and the H. Lee Moffitt Cancer Center [MCC]) between the years of 2014 and 2017 (MCC 2014–2015 only) were included in this study, which was approved by Institutional Review Boards at both centers. Minimum age for inclusion was 18 years, and all newly presenting patients underwent next generation sequencing (NGS) using established 11‐ or 51‐gene myeloid panels performed by Genoptix Laboratories (now NeoGenomics Laboratories). Mutational profiling using an amplicon‐based targeted NGS platform was completed on an Illumina MiSeq instrument. All newly presenting AML patients had NGS performed as standard of care and were included in the analysis; no patients were excluded. Cytogenetic risk as defined per European LeukemiaNet criteria. Newly diagnosed patients were treated with less‐intensive and intensive chemotherapy regimens, with less‐intensive defined as hypomethylating agent‐based therapy, and more intensive as cytarabine‐ and anthracycline‐based regimens. Patients who had relapsed or refractory disease were also treated with less‐intensive and intensive therapies. Ethnicity (Hispanic or non‐Hispanic) was self‐identified by patients, as was race.2.2Patient demographics and disease characteristics were summarized using descriptive statistics. The association between ethnicity and study covariates was examined using chi‐square test. For continuous variables, Wilcoxon signed‐rank test was used to compare groups. Overall survival analysis was measured from date of diagnosis until death due to any cause. The Kaplan–Meier method was used to derive survival curve estimates across groups, and the log‐rank test was used to comparisons survival curves. Univariable Cox proportional hazard regression model was performed to estimate hazard ratio (HR) and corresponding 95% confidence interval. Statistical significance was determined at a two‐sided alpha level of <0.05. False discovery rate method was used for multiplicity adjustment. Mutation plot was displayed along with key study covariates. Statistical analyses were carried out using SAS Software version 9.4  and R Software version 4.1.1.33.1Among 225 treated AML patients with targeted NGS performed, 58 (26%) were Hispanic, and 167 (74%) were non‐Hispanics Table p = 0.0762). The cytogenetic risk score in all patients was favorable in 7.6%, intermediate in 63.6%, adverse in 25.3%, and missing in 3.5%. The cytogenetic risk score in Hispanics was favorable (8.6%), intermediate (58.6%), and adverse (32.8%), and in non‐Hispanics it was favorable (7.2%), intermediate (65.3%), adverse (22.8%), and missing (4.8%). There was no difference in cytogenetic risk score between Hispanic and non‐Hispanic patients (p = 0.1796), although there was an emerging trend to adverse cytogenetic risk in Hispanics.At time of NGS sample collection, 105 patients (47%) had newly diagnosed disease, 101 patients (45%) had relapsed/refractory disease, and 19 patients (8%) were in complete remission  had a detectable mutation on the targeted NGS panel, 73 (32%) did not (Table p = 0.0182) . On the other hand, WT1 mutations were more common in Hispanic patients compared to non‐Hispanics , with an adjusted p‐value of 0.421.Focusing on mutation differences between Hispanics and non‐Hispanics, for discrete mutations, rates were similar between Hispanics and non‐Hispanics. IDH1 mutations, but not IDH2, were significantly more common in non‐Hispanic versus Hispanic patients  Table . Howeverp = 0.284]) by univariate analysis .The total number of mutations in Hispanics appeared to be less, with a mean number of 1, compared to a mean number of 1.17 for non‐Hispanics . Adverse risk fusions MLL/inv(3)/t were seen in 4.4% of patients and, although rare, were more common in Hispanic patients compared to non‐Hispanics , and this remained significant after multiplicity adjustment (p = 0.024).We also examined for associations between ethnicity and mutation class/molecular subgroups . The mos3.33.3.1p = 0.443). There was no difference in OS between Hispanics and non‐Hispanics , and 3‐year OS for Hispanic patients was 63.0% versus 39.1% for non‐Hispanic patients (p = 0.735). The univariate HR for survival for non‐Hispanics was 1.43 . In multivariate analysis considering age, sex, ECOG, cytogenetic risk, and institution, the HR for survival for non‐Hispanics was 1.15 . Sixteen Hispanic patients (27.6%) underwent allogeneic hematopoietic stem cell translation, compared to 36 non‐Hispanic patients .With a median follow‐up of 24.4 months , 125 patients were still alive (55.6%): 35 Hispanic patients (60.3%) and 90 non‐Hispanic patients (53.9%). There was no difference in follow‐up between the two groups . In multivariate analysis accounting for age, sex, ECOG, and treating institution, this difference in survival persisted, but was no longer significant .In patients who were newly diagnosed (105 patients), OS was significantly better for Hispanic patients compared to non‐Hispanics, at a median of 44.7 months versus 14 months    and 83.1% versus 34.3% at 3 years (p = 0.072) . The two IDH mutations occurring in Hispanic patients were both IDH2 R140. When we examined splicing factor mutations as a group , while the frequency was not different by ethnicity, OS was significantly better in non‐Hispanic patients (p = 0.010)  /inv16), and TP53/aneuploidy , and non‐Hispanic patients were more likely to have a favorable risk fusion/mutation . The strongest association was between Hispanic ethnicity and adverse risk AML fusions, which remained statistically significant after multiplicity adjustment  of these patients were in complete remission after initially receiving treatment at outside centers. These patients were included in analysis of mutations since pre‐leukemic mutations often persist in remission. However, driver mutations may have been missed in these patients and decreased the rate of detection of NPM1 and FLT3 mutations, for example.p = 0.128). When we examined only newly diagnosed patients, Hispanic patients had a significant and lasting improvement in OS (p = 0.026). These observations are despite Hispanic patients appearing to be enriched for prognostically adverse cytogenetic abnormalities, particularly high‐risk fusions such as mixed lineage leukemia re‐arrangement (MLL‐r). When we performed multivariate analysis accounting for age, sex, ECOG, cytogenetic risk, and treating institution, this difference in OS was no longer observed, likely due to Hispanics being younger at time of AML diagnosis than non‐Hispanics. Whether this age difference at AML diagnosis is due to the demographics of Florida versus differences in risk factors or disease biology remains to be discovered. Hispanic patients were also more likely to have de novo rather than secondary AML. At the minimum, and contrary to prior reports, our study suggests that Hispanic patients have equivalent age‐adjusted OS compared to non‐Hispanics, particularly if they are treated at an academic medical center. This may be due to the equalizing of socioeconomic disparities that can contribute to more advanced presentation, inadequate follow‐up, and lack of availability of allogeneic stem cell transplantation. There was no difference in the rate of allogeneic transplantation in different ethnic groups on our study.Interestingly, Hispanic patients had improved OS compared to non‐Hispanics, although the curves converged over time at around 4 years .We sought to further assess survival differences in Hispanics and non‐Hispanics with AML by examining distinct, class‐defining molecular subgroups. In the entire cohort, patients with TP53/aneuploidy, regardless of ethnicity, had the poorest outcomes, although there was a tail on the curve with 10%–15% long‐term survival. Interestingly, Hispanic patients in the driver not‐class defining category had improved OS relative to non‐Hispanics. One possible explanation for this difference is that non‐Hispanic patients were enriched for mutations involved in cellular metabolism, suggesting that the heterogeneous driver not‐class defining category could be sub‐divided by the presence or absence of mutations in IDH1/2. Hispanic patients did not have inferior OS in any molecular subgroup. However, when we looked at specific mutation classes, Hispanic patients with RAS pathway or RNA splicing factor mutations had inferior OS compared to non‐Hispanics .Supporting InformationClick here for additional data file.


The association between Glasgow Prognostic Score (GPS) and the modified Glasgow Prognostic Score (mGPS) and clinical outcomes in patients receiving immune checkpoint inhibitors (ICIs) remains controversial. Thus, this meta‐analysis aimed to examine the prognostic performance of GPS and mGPS in patients treated with ICIs.Eligible studies were retrieved from searches of EMBASE, PubMed, Web of Science, and Cochrane Library until July 2021. The hazard ratio (HR) and 95% confidence intervals (CIs) were pooled by using fixed‐effect or random‐effects model to evaluate the influence of GPS/mGPS on overall survival (OS) and progression‐free survival (PFS).p < 0.001) and PFS (p < 0.001). Subgroup analyses showed no significant association between mGPS score of 1 and OS in patients with non‐small cell lung cancer (NSCLC), while this score was significantly associated with poor PFS in patients with NSCLC and head and neck squamous cell carcinoma. Higher GPS (score of 1 or 2) were associated with poor clinical outcomes . Subgroup analysis showed high GPS levels were linked to worse OS in patients with NSCLC and gastric cancer, but not for PFS in these patients. Regarding test time point, GPS was related to worse OS and PFS in pre‐treatment GPS group, but not in post‐treatment GPS group.A total of 1164 patients were included. Overall, mGPS score of 2 and 1 were related to inferior OS (GPS and mGPS showed great potential to predict survival in patients treated with ICIs. Large and perspective trial are warranted to further validate these findings. In the present meta‐analysis, we pooled the data of 963 individuals from 11 studies on mGPS, and 505 patients from 4 GPS articles to assess the prognostic roles of mGPS and GPS among various cancer patients treated with ICIs. We found that higher mGPS and GPS were significantly related to inferior OS and PFS, indicating that these biomarkers may be as a clinical tool dichotomizing patients into responders and non‐responders to ICIs, and then assist clinicians to make decision regarding ordering of therapies. The keywords were as follows: “immune check point inhibitor*”, “neoplasm”, “Glasgow prognostic score or modified Glasgow prognostic score” or “GPS” and “mGPS”, “CTLA‐4”, “ipilimumab”, “PD‐1”, “PD‐L1”, “nivolumab”, “avelumab,” “durvalumab”, “atezolizumab”, “pembrolizumab”, “immune checkpoint inhibitor”, “immunotherapy”, “prognosis”, “prognostic”, and “survival”.2.2Studies that met the following requirements were included: (1) studies including patients with tumors receiving ICIs; (2) reporting data regarding the hazard ratio (HR) and 95% confidence interval (CI) for overall survival (OS) or progression‐free survival (PFS). The following exclusion criteria were applied: (1) Letters, conference abstracts, editorials, case reports, and reviews; (2) insufficient information to calculate HRs and 95% Cis; (3) Studies were not in English.2.3The following information was extracted independently by two reviewers: first author, publication year, country, study design, sample size, treatment, the score of GPS or mGPS, HRs for OS and PFS, and 95% CIs. We assess the quality of included studies with the Quality Assessment of Newcastle‐Ottawa Scale (NOS), which includes three aspects: selection, comparability, and outcome assessment. Included studies assigned with scores higher than 5 were high‐quality studies. Disparities were addressed by discussion.2.4I2 statistic. I2 > 50% was represented significant heterogeneity, and then a random effect model was used. Otherwise, a fixed‐effect model was applied. The sources of heterogeneity were examined by sensitivity and subgroup analysis. Funnel plots and Egger's test were utilized to identify publication bias. Statistical significance was defined as p < 0.05 for all statistical tests with STATA 16.0 (Stata Corp.).Pooled HR were calculated based on HRs with their 95% CIs. Heterogeneity of the included studies was evaluated by Higgins 33.1A total of 98 relevant hits were initially obtained from selected databases. There were 58 records remained after removing duplicates. Of these, 35 articles were discarded by titles and abstracts review due to reviews, conference abstracts, irrelevant studies, letters, or case reports. The full texts of the remaining 23 articles were reviewed. Finally, 14Six studies focused on patients with NSCLC, three studies on gastric cancer, two articles on head and neck squamous cell carcinoma, two studies on renal cell carcinoma, and two studies on urothelial carcinoma and salivary gland carcinoma, respectively. Most studies are conducted in Japan, and all included studies are retrospective. The types of ICIs involved in our studies only comprised PD‐1/PD‐L1 inhibitors  except for one study, which included ICI as monotherapy or in combination with anti‐ CTLA‐4 and anti‐VEGF treatments.Regarding testing time of GPS/mGPS, most studies assessed pre‐treatment mGPS, and only one study evaluated post‐treatment mGPS. Similarly, only two studies evaluated post‐treatment GPS. Besides, the majority of patients in our study WERE treated with ICIs as first‐or further‐line settings.3.2p < 0.0001), with significant heterogeneity (I2 = 55.6%). Moreover, mGPS score of 1 from seven studies were found to be correlated to worse OS , with moderate heterogeneity (I2 = 41.7%) , we treated Ogura 2021p < 0.001) and of 1  were related to inferior PFS, with moderate heterogeneity  . For PFS, the funnel plots of mGPS score of 2 or 1 were symmetrical, indicating no potential publication bias .Sensitivity analyses of mGPS for the OS and PFS were conducted to determine whether an individual study had an effect on the results. There was no significant influence on results, regardless of mGPS score of 2 or 1, suggesting that these results were robust. The funnel plots involving mGPS for OS indicated that obvious publication bias existed, regardless of mGPS score of 2 or 1 , with significant heterogeneity (I2 = 66.8%) , and 2.30 .Both Kasahara and Namikawa investigated the effect of GPS on OS and PFS before and after ICIs, therefore we considered Kasahara2019) Figure . Unlike p = 0.036), with moderate heterogeneity (I2 = 25.2%). Subgroup analysis on the basis of cancer types revealed that there was no statistically significant association between high GPS levels and inferior PFS in patients with NSCLC and gastric cancer, with pooled HRs of 1.63 , and 1.36 . With regard to test time point, like OS, high GPS was related to worse PFS in pre‐treatment GPS group, but not for post‐treatment GPS group with pooled HRs of 1.67 , and 1.23  .Sensitivity analyses revealed that no individual study could substantially affect the combined HRs and its 95% Cis for the OS. Nevertheless, for the PFS, pooled results were significantly influenced when Kasahara cohort 24In the present meta‐analysis, we pooled the data of 963 individuals from 11 studies on mGPS, and 505 patients from four GPS articles to assess the prognostic roles of mGPS and GPS among various cancer patients treated with ICIs. We found that higher mGPS and GPS were significantly related to inferior OS and PFS, indicating that these biomarkers may be as a clinical tool for dichotomizing patients into responders and non‐responders to ICIs, and then assisting clinicians to make decision regarding the ordering of therapies. To our best knowledge, this is the first comprehensive analysis to evaluate the association between mGPS/GPS and survival outcomes in patients treated with ICIs.+ tumor‐infiltrating lymphocytes, have been reported to be predictive of the efficacy of ICIs, these markers have yet to be established due to no conclusive results across tumors.The advent of ICIs has reshaped the management of many tumor types, especially for those cancers by a lack of treatment options. Nevertheless, a great number of tumor patients fail to respond to ICIs, creating an urgent need to identify prognostic biomarkers capable of predicting prognosis in these patients.+ T‐cells, which is of importance in antitumor immune response to ICIs.The GPS/mGPS, which comprise CRP and albumin, are composite biomarkers of systemic inflammation and nutritional status. The critical role of systemic inflammation in the development and progression of tumor has been widely accepted. A great number of studies reported that hematological parameters which can capture inflammatory conditions, such as neutrophils, lymphocytes, platelets, and CRP, are related to prognosis in cancer patients.Although the prognostic roles of mGPS/GPS in patients treated with ICIs have started to be recognized and investigated in recent years, there are open questions about the standardized use of mGPS/GPS. First, mGPS/GPS should be hierarchic as these is characterized by ordinal categorical variables. GPS/mGPS of 0, 1, and 2 should be considered as low, moderate and high risk. In our study, we pooled HRs based on mGPS equal to 1 or 2, referring to mGPS of 0 for differentiating between the different levels. Regarding GPS, although we cannot conduct this like mGPS due to limited studies, we still demonstrated that higher GPS was associated with poor prognosis. Second, the treatment history may affect the mGPS/GPS. Since CRP and albumin are dynamic biomarkers that can be affected by many factors, including underlying diseases, treatment approaches (resection or radiotherapy), and testing conditions.Several limitations should be acknowledged. First, all of the included studies were retrospective, whose results might be influenced by selection bias and reporting bias. The GPS/mGPS need to be demonstrated in perspective studies. Second, the majority of the recruited patients from eligible studies are Japanese, while only three studies were performed in Western countries. Third, although subgroup analysis based on types of cancers has been conducted, relevant studies were insufficient in specific cancer types, which need to cautiously generalize these results in these patients. Forth, comprehensive subgroup analyses according to other confounders were not done due to the small sample size, which may bias our results. Fifth, the line of ICIs varies according to different tumor types. Therefore, previous treatments may have affected CRP and albumin levels at the initiation of immunotherapy.5Our study showed that higher mGPS/GPS were associated with poor prognosis in patients undergoing ICIs. Types of cancer, testing time may affect the prognostic performance of mGPS/GPS. These results demonstrated that the mGPS/GPS as a convenient and cost‐effective score derived from blood measured routinely may help clinicians to properly use ICIs to treat patients with cancer.Yongchao Zhang analyzed the data and wrote the first draft. Shanshan Chen and Hualei Chen analyzed the data. WeiLi designed the study, proof read and revised the submission. All authors discussed the results and approved the final manuscript.This study is funded by Beijing Talents Project and Capital's Funds for Health Improvement and Research (grant number 2020–2475 2175).The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Figure S1Figure S2Figure S3Figure S4Figure S5Figure S6Click here for additional data file.


Primary gastric T‐cell lymphoma (PG‐TCL) is a rare hematological malignancy with few data reported. The objective of this study is to investigate the epidemiology, clinical characteristics, and survivals of PG‐TCL.Totally, 164 patients with PG‐TCL from 1975 to 2016 extracted from the Surveillance, Epidemiology, and End Results Program (SEER) database were analyzed. Kaplan–Meier method was applied to plot overall survival (OS) and cancer‐specific survival (CSS). The prognostic factors of OS and CSS were explored by Cox proportional hazard regression. Nomograms were constructed to predict survival possibilities.The age‐adjusted incidence rate of PG‐TCL was 0.0091 per 100,000 person‐years and increased with age. The median age at onset was 65 years old with male predominance. The major histological type was peripheral T‐cell lymphoma, NOS (63.4%). The 1‐, 2‐, and 5‐year OS were 45.5%, 34.7%, and 23.5%, respectively while the 1‐, 2‐, and 5‐year CSS were 47.4%, 37.3%, and 29.6%, respectively. Multivariate Cox analysis demonstrated that age at diagnosis, use of chemotherapy, and radiotherapy were the independent prognostic factors for OS. Chemotherapy combined with radiotherapy could significantly improve patients' OS compared with chemotherapy alone. Moreover, age at diagnosis and use of chemotherapy were also the independent prognostic factors for CSS. Nomograms for PG‐TCL were developed to predict 1‐, 2‐, and 5‐year OS possibilities. The predictability of nomograms was verified by high concordance index and good agreement with the predicted value in calibration plots.PG‐TCL is a rare neoplasm with low incidence. Patients with PG‐TCL generally exhibited poor prognosis. Use of chemotherapy plus radiotherapy was associated with favorable OS. This is the first and largest population‐based study about primary gastric T‐cell lymphoma(PG‐TCL). Patients with PG‐TCL generally exhibited poor prognosis. Use of Chemotherapy plus radiotherapy was associated with favorable survival. Researchers can utilize this database to investigate incidence, clinical characteristics, survival, and therapy of rare cancers.22.1The demographic characteristics, treatment information, and survival of patients diagnosed as PG‐TCL between 1975 and 2016 came from the SEER 18 registry database . The SEER 18 database  was available for extracting data of disease incidence which were registered in and after 2000. The International Classification of Diseases for Oncology, Third Edition 3 (ICD‐O‐3) histology codes 9700/3, 9701/3, 9702/3, 9705/3, 9708/3, 9709/3, 9714/3, 9716/3, 9717/3, 9719/3, 9724/3 and 9725/3, along with the primary site of stomach were used to identify as PG‐TCL. The primary site was categorized into four subgroups according to tumor location: upper third of stomach , mid and low of stomach , overlapping stomach (C16.8) and stomach NOS (C16.9). Other histological types of lymphoma with stomach as the primary site or non‐gastric T‐cell lymphoma were regarded as comparative cohorts of PG‐TCL.Baseline characteristics collected from SEER 18 database included age, sex, race, type of disease histology, Ann Arbor stage, lesion location, treatment , cause‐specific death classification, survival months, and vital status. All the SEER data were obtained from the public database.2.2t‐test or Wilcoxon test for continuous variables whereas χ2 test for categorical variables. The log‐rank test was used to compare OS or cancer‐specific survival (CSS) between different groups and Kaplan–Meier method was applied to plot the survival curve. CSS was defined as the time from diagnosis to death due to PG‐TCL or the last follow‐up. The Cox hazard proportional regression were performed to identify independent prognostic factors of OS or CSS. Variable exhibiting P < 0.1 in the univariate analysis was considered as a candidate to perform the subsequent multivariate analysis. Moreover, a nomogram model was established to predict the 1‐, 2‐, and 5‐year OS probabilities for PG‐TCL patients. Concordance index (C‐index) and calibration plot were utilized to assess the accuracy of nomogram model. Statistical analyses were conducted by SPSS 25.0 and R package 3.6.2. All P‐values were bilateral and P < 0.05 was considered as a statistical difference.The difference of baseline characteristics was explored by Student's 33.1P < 0.0001, Figure The SEER database demonstrated that the age‐adjusted incidence rate (AIR) of PG‐TCL from 2000 to 2018 [age adjusted to the 2000 US Standard Population (Census P25‐1130)] was 0.0091 per 100,000 person‐years . The annual AIR of PG‐TCL was shown in Figure 3.2The current study identified 164 patients with PG‐TCL from SEER database. The demographics and treatment information of PG‐TCL were shown in Table Subgroup analysis was conducted according to the histological types , which was demonstrated in Table 3.3P = 0.936) or CSS  was observed among different subtypes of PG‐TCL patients.With a median follow‐up of 7 (0–223) months, 130 (79.3%) deaths were recorded, and 72 cases died of PG‐TCL. The Kaplan–Meier curves of OS and CSS for total PG‐TCL patents were shown in Figure Univariate analysis showed that younger age at diagnosis as well as the use of chemotherapy and radiotherapy were obviously associated with better OS Table  and CSS Moreover, we also investigated the impact of different treatment modalities on OS and CSS. Univariate and multivariable analyses were performed in 106 patients who received treatment. Compared with patients receiving chemotherapy, chemotherapy plus radiotherapy could significantly improve PG‐TCL patients' OS Table . However3.4Furthermore, a predictive nomogram model was constructed to predict OS for PG‐TCL based on independent risk factors of the Cox regression analysis, including age at diagnosis, radiotherapy, and chemotherapy. Figure 3.5n = 164) and other histological subtypes of PGL  from SEER database. The distribution and percentage of different subtypes of PGL were shown in Figure P < 0.001; median CSS: 9.0 months vs 78.0 months, P < 0.001; Figure Then, we investigated the difference of clinical characteristics and prognosis between patients with PG‐TCL n = 16 and othe3.6P < 0.001, Figure P < 0.001, Figure P = 0.015, Figure P = 0.197, Figure As PTCL‐NOS and ALCL‐ALK+ were two major histological types of PG‐TCL, we further compared the clinical features and survival of patients between primary gastric PTCL‐NOS/ALCL‐ALK+ and non‐gastric PTCL‐NOS/ALCL‐ALK+. Primary gastric ALCL‐ALK+ patients exhibited older age of onset Table , inferio4Given the rarity of PG‐TCL, clinical characteristics and prognosis have not been comprehensively studied yet. Large‐scale databases, such as SEER program, provided us the epidemiology and clinical features of PG‐TCL. To the best of our knowledge, this is the largest study about PG‐TCL to date. There were several observations obtained from the current study.First, we reported the low age‐adjusted incidence rate of PG‐TCL with 0.0091 per 100,000 person‐years among US population for the first time. The incidence rate increased with age and males were more susceptible to PG‐TCL. In addition, this histological subtype was also rare among different types of PGL. In SEER database, PG‐TCL represented 0.97% of total PGL cases. In a previous study by Koch P, et al, PG‐TCLs occurred 1.3% (5/398) of total localized PGL (Ann Arbor Stages I and II) cases in Germany population,Secondly, the current study demonstrated that PG‐TCL mainly occurred in the elderly and was more prevalent in males. The most common histological type of PG‐TCL in our study was PTCL‐NOS, followed by ALCL‐ALK+. These findings were consistent with previous reports from other institutions.Thirdly, independent survival factors of PG‐TCL patients were identified in the current study by log‐rank tests and Cox proportional hazards regressions. Age at diagnosis was the independent demographic factor and had an adverse impact on OS, which could be partly accounted for poorer performance status, more comorbidities, and higher incidence of toxicities of chemotherapy. On the other hand, the treatment modality provided to patients varied considerably in our cohort. Chemotherapy was still the first‐line treatment regime of PG‐TCL. More than half of the patients received chemotherapy. Multivariate regression analyses further indicated that chemotherapy was significantly associated with improved OS. There has been no consensus on standard chemotherapy regime for PG‐TCL yet. The chemotherapy regime CHOP, which consists of cyclophosphamide, doxorubicin, vincristine, and prednisolone, was most commonly used in Park et al.’s cohort.Fourthly, as concerns statistical methodology, CSS was included as one of the endpoints of our study as it could directly reflect the influence of the disease on long‐term survival of patients and exhibit a more “dynamic” estimate of the risk of death over time than traditional survival rate. This study identified age at diagnosis and chemotherapy as the independent prognostic factors of CSS while radiotherapy might improve CSS of patients with PG‐TCL. Besides, from the CSS plot Figure , high riThere were some limitations to the present study. First of all, data selection bias may be inevitable due to the retrospective manner of the study. Next, in addition to the prognostic factors included in the current study, other factors, such as genetic mutation, performance status, chemotherapy regimens, specific dosage of radiation, different pathological types (ALCL‐ALK−), etc. also have an impact on survival. But the detailed information was unavailable to us from SEER database. Additionally, whether the patients did not receive therapy, or the treatment information was unknown could not be discriminated based on the data extracted from SEER database. The insufficient information limited us for further investigating the effects of these important components on prognosis. Moreover, there has been no consensus about the definition of PGL so far. In the strict sense, only patients with Ann Arbor Stages I and II would be included in our study according to the original definition of PGL.Taken together, this is the largest and first population‐based retrospective study about PG‐TCL, which consisted of 164 patients from SEER database and indicated distinct clinical characteristics and inferior prognosis of PG‐TCL with low incidence in US population. Our study suggested that chemotherapy was the main treatment option for PG‐TCL, and the addition of radiotherapy could improve patients' survivals, which could improve clinicians' knowledge about the treatment of PG‐TCL. The predictable nomogram facilitated hematologists to estimate prognosis and establish personal follow‐up strategy.Minyue Zhang and Fei Xiao contributed the conception of the study and wrote the paper. Meisi Lin, Minyue Zhang, and Mengping Chen collected and analyzed the data. Jian Hou designed the study and Honghui Huang contributed to the revision of the manuscript. All authors approved the final manuscript.This work was supported by Science and Technology Development Foundation of Shanghai Pudong New Area Health and Family Planning Commission (PW2015E‐1).No conflicts of interest.The patient information is not contained in the SEER program. Institutional review board approval and informed consent from the patients were not required.Tables S1‐S2Click here for additional data file.Figure S1Click here for additional data file.


The combination of bortezomib, thalidomide, and dexamethasone (VTd) is a standard of care for transplant‐eligible patients with newly diagnosed multiple myeloma (NDMM). Although approved labeling for VTd includes an escalating thalidomide dose up to 200 mg daily (VTd‐label), a lower fixed dose of thalidomide  has become commonplace in clinical practice. To date, no clinical trials comparing VTd‐mod with VTd‐label have been performed. Here, we compared outcomes for VTd‐mod with VTd‐label using a matching‐adjusted indirect comparison.VTd‐mod data were from NCT02541383  and NCT00531453 (phase II); VTd‐label data were from NCT00461747 . To adjust for heterogeneity, baseline characteristics from VTd‐label were weighted to match VTd‐mod. Outcomes included overall survival (OS), progression‐free survival (PFS), postinduction and posttransplant responses, and safety.VTd‐mod was noninferior to VTd‐label for OS, postinduction overall response rate (ORR), and very good partial response or better (≥VGPR). VTd‐mod was significantly better than VTd‐label for PFS, posttransplant ORR, and ≥VGPR. VTd‐mod was noninferior to VTd‐label for safety outcomes, and inferior to VTd‐label for postinduction and posttransplant complete response or better.Our analysis supports the continued use of VTd‐mod in clinical practice in transplant‐eligible NDMM patients. Part 2 of CASSIOPEIA is ongoing, with re‐randomization of patients achieving a partial response or better at Day 100 posttransplant to observation or daratumumab maintenance  every 8 weeks until disease progression or for a maximum of 2 years. In the randomized NCT00531453 trial, NDMM patients received VTd‐mod composed of bortezomib , dexamethasone (40 mg Days 1‐4 and 9‐12), and thalidomide (100 mg Days 1‐21) for four 21‐day cycles, followed by ASCT [The VTd‐mod group consisted of pooled data from NCT02541383  and NCT00531453 (a phase II study) , 7. CASSFor the CASSIOPEIA (VTd‐mod) and PETHEMA/GEM (VTd‐label) trials, individual patient‐level data (IPD) were obtained from the sponsor and were validated with their respective clinical study reports. For the NCT00531453 study, only aggregate data were available, with all available baseline variables and outcomes extracted from primary publications; IPD were reconstructed from Kaplan‐Meier survival curves using a validated method .2.2Both naïve comparisons (without any adjustments) and MAICs (unanchored indirect comparison) were conducted; the efficacy and safety populations from CASSIOPEIA were used for comparison of efficacy and safety endpoints in their respective MAICs. For the MAIC, individual patients in the VTd‐label group were weighted based on baseline characteristics to match VTd‐mod. Prognostic factors considered for matching were selected in collaboration with clinical experts, and included all commonly available baseline patient demographic and clinical characteristics in both studies: age, sex, Eastern Cooperative Oncology Group (ECOG) performance status, myeloma type, International Staging System (ISS), creatinine clearance, hemoglobin level, and platelet count. Cytogenetic risk was not reported for NCT00531453; therefore, it was not possible to consider for matching.2.3Efficacy outcomes included overall survival (OS), progression‐free survival (PFS), and postinduction and posttransplant responses: overall response rate (ORR), complete response or better (≥CR), and very good partial response or better (≥VGPR). Safety outcomes included treatment discontinuation due to any grade adverse events (AEs), Grade 3 or 4 thrombosis, and Grade 3 or 4 peripheral neuropathy.P‐values for HRs and Kaplan‐Meier curves. Absolute rate difference and odds ratios (ORs), calculated with two‐sided 95% CIs, were used to determine treatment response and safety outcomes; P‐values for ORs were determined using the two‐sided Fisher's exact test. Based on recent oncology studies, results that did not achieve statistical significance (5%) were interpreted with the use of noninferiority margins [Comparison of VTd‐mod with VTd‐label was performed stepwise via a naïve, unadjusted, indirect comparison followed by MAIC. Baseline characteristics from patients in the VTd‐label arm from the PETHEMA/GEM study were utilized to weight IPD in the VTd‐label group and then matched with those in the pooled VTd‐mod group. Hazard ratios (HRs) with two‐sided 95% confidence intervals (CIs) for time to event outcomes (OS and PFS) were determined using a weighted Cox regression analysis model that was fitted to the treatment arm; a log‐rank test was used to determine  margins , 12. Non margins .3Median duration of follow‐up was 35.9 months for VTd‐label, 33.3 months for NCT00531453 (VTd‐mod), and 18.8 months for CASSIOPEIA (VTd‐mod).3.1Baseline characteristics used for matching the VTd‐mod  and VTd‐label  groups in the efficacy analyses are shown in Table  3.2P = .072) and on the MAIC   and the MAIC   Figure . Signifi) Figure .3.3Table P = .065) and ≥VGPR   or ≥VGPR   and posttransplant   and 130 VTd‐label patients before and after weighting were presented for MAIC safety analysis. Characteristics were similar after weighting (Table  P = .678) or MAIC  (Table  P = .481) and 2.0% for VTd‐mod versus 0.9% for VTd‐label  for MAIC. Rates of Grade 3 or 4 peripheral neuropathy were 6.6% versus 5.4%  and 6.6% versus 4.2% .MAIC analysis of safety outcomes revealed VTd‐mod was noninferior to VTd‐label. No statistically significant differences between VTd‐mod and VTd‐label were found for the rate of discontinuation due to AEs for either the naïve comparison , whereas response in PETHEMA/GEM was investigator assessed using criteria from the European Society for Blood and Marrow Transplantation , 7, 8. TDifferences in study design, such as follow‐up times, routes of treatment administration, and maintenance regimens, may also have introduced bias toward the VTd‐label group, specifically regarding long‐term survival. The median follow‐up time in PETHEMA/GEM for VTd‐label was longer (35.2 months) , 9 than In terms of safety, the lack of superiority of VTd‐mod compared with VTd‐label was also unexpected, but reporting rules for PETHEMA/GEM may not have been as rigorous as those for CASSIOPEIA, thereby underestimating some AEs. As VTd‐mod is often used in clinical practice to reduce AEs, such as peripheral neuropathy, and thereby prevent early treatment discontinuations, analysis confirming reduced toxicity would be beneficial for clinical and reimbursement decision‐making. Although this MAIC analysis cannot confirm a lower rate of safety events based on included endpoints, it can confirm that VTd‐mod is noninferior to VTd‐label, supporting the use in NDMM patients.Because our MAIC analysis was a cross‐trial comparison, statistical adjustments compensating for unreported or unobserved confounding factors, referred to as residual confounding, could not be performed and may introduce bias. Residual confounding may result in omission of variables critical to balancing patient groups . The inc5Based on this MAIC analysis, VTd‐mod was shown to be noninferior to VTd‐label for OS, postinduction ≥VGPR, and ORR. This analysis also demonstrated the superiority of VTd‐mod for PFS, and posttransplant ≥VGPR and ORR. For response postinduction and posttransplant, VTd‐mod was inferior to VTd‐label for ≥CR. Although VTd‐mod patients received a lower thalidomide dose compared with VTd‐label patients, safety outcomes, even for Grade 3 or 4 events, were non‐inferior but did not reach the level of superiority. Taken together, our analysis supports the continued use of VTd‐mod in clinical practice in transplant‐eligible NDMM patients. These findings may also provide useful insight for clinical and reimbursement decision‐making regarding the relative efficacy and safety of the different treatment regimens.PS, M‐VM, AA, TF, CH, and PM contributed to the accrual and treatment of patients, data acquisition, and interpretation and analysis of data from the CASSIOPEIA study. TK, SC, JH, and AL contributed to the study concept, designed the MAIC analysis, and interpreted the data. MH and TV performed the MAIC analyses. All authors participated in manuscript preparation and revisions, and all authors read and approved the final manuscript.PS: Honoraria from Amgen, BMS, Celgene, Janssen, Karyopharm; Takeda; research funding from Amgen, Celgene, Janssen, Karyopharm, SkylineDx, Takeda. M‐VM: advisory committee for AbbVie, Amgen, Celgene, Genentech, GSK, Janssen, Mundipharma EDO, PharmaMar, Roche Laboratories, Inc, Takeda; membership on an entity's board of directors for AbbVie, Amgen, Celgene, EDO, GSK, Janssen, PharmaMar, Takeda; honoraria from Adaptive, Amgen, Celgene, Janssen, Takeda; speakers bureau for Amgen, Celgene, Janssen, Takeda; data and monitoring committee for Amgen and Jansen. AA: Advisory committee for Amgen, Celgene, Janssen, Sanofi, Takeda; consultancy for Sanofi; honoraria from Amgen, Celgene, Janssen; research funding from Celgene, Janssen, Sanofi. TF: Membership on an entity's board of directors or advisory committees for Amgen, Celgene, Janssen, Karyopharm, Oncopeptides, Roche, Sanofi, Takeda; speakers bureau for Celgene, Janssen, Takeda. CH: Consultancy for Celgene; honoraria from AbbVie, Amgen, Celgene, Janssen. MH and TV: Employment from Ingress‐Health. TK, SC, JH, and AL: Employment from Janssen. PM: Consultancy for AbbVie, Amgen, Celgene, Janssen, Takeda; honoraria from AbbVie, Amgen, Celgene, Janssen, Takeda.NCT00531453 (phase II), CASSIOPEIA study (NCT02541383), and PETHEMA/GEM study (NCT00461747).https://www.janssen.com/clinical-trials/transparency. As noted on this site, requests for access to the study data can be submitted through Yale Open Data Access (YODA) Project site at http://yoda.yale.edu.The data sharing policy of Janssen Pharmaceutical Companies of Johnson & Johnson is available at