Cardiac arrest survivors may experience hypoxic brain injury that results in cognitive impairments which frequently remain unrecognised. This may lead to limitations in daily activities and participation in society, a decreased quality of life for the patient, and a high strain for the caregiver. Publications about interventions directed at improving quality of life after survival of a cardiac arrest are scarce. Therefore, evidence about effective rehabilitation programmes for cardiac arrest survivors is urgently needed. This paper presents the design of the ALASCA  trial, a randomised, controlled clinical trial to evaluate the effects of a new early intervention service for survivors of a cardiac arrest and their caregivers.The study population comprises all people who survive two weeks after a cardiac arrest and are admitted to one of the participating hospitals in the Southern part of the Netherlands. In a two-group randomised, controlled clinical trial, half of the participants will receive an early intervention service.The early intervention service consists of several consultations with a specialised nurse for the patient and their caregiver during the first three months after the cardiac arrest. The intervention is directed at screening for cognitive problems, provision of informational, emotional and practical support, and stimulating self-management. If necessary, referral to specialised care can take place. Persons in the control group will receive the care as usual.The primary outcome measures are the extent of participation in society and quality of life of the patient one year after a cardiac arrest. Secondary outcome measures are the level of cognitive, emotional and cardiovascular impairment and daily functioning of the patient, as well as the strain for and quality of life of the caregiver. Participants and their caregivers will be followed for twelve months after the cardiac arrest.A process evaluation will be performed to gain insight into factors that might have contributed to the effectiveness of the intervention and to gather information about the feasibility of the programme. Furthermore, an economic evaluation will be carried out to determine the cost-effectiveness and cost-utility of the intervention.The results of this study will provide evidence on the effectiveness of this early intervention service, as well as the cost-effectiveness and its feasibility.Current Controlled Trials [ISRCTN74835019] The incidence of cardiac arrest is 1 – 2 per 1000 inhabitants a year but differs according to country and region . ResusciA cardiac arrest may lead to irreversible brain damage, called posthypoxic encephalopathy or hypoxic brain injury. Hypoxic brain injury often leads to cognitive or emotional impairments, with memory disorders and depressive symptoms being the most common complaints -15. As aUnfortunately, although the number of problems is substantial, a large percentage of these problems, especially the cognitive impairments, are not recognised or treated.Currently, there exist no specific follow-up programmes or nursing interventions for cardiac arrest survivors in the Netherlands. In the international literature, only a few programmes for cardiac arrest survivors were found. In a study by Cowan et al., psychosocial nursing therapy reduced two-year mortality in a group of cardiac arrest survivors . This inThe diffuse brain injury and cognitive loss after a cardiac arrest seems in many ways similar to that seen after traumatic brain injury. In a study on traumatic brain injury patients, a routine follow up service resulted in a reduction in social morbidity and severity of post concussion symptoms six months later . Given tThe primary goal of the present study is to evaluate the effectiveness of a new, early intervention service for survivors of a cardiac arrest and their caregivers. Secondly, a process evaluation will be performed to gain insight into factors that might have contributed to the effectiveness of the intervention and to gather information about the feasibility of the programme. Finally, an economic evaluation will determine the cost-effectiveness and cost-utility of the intervention.We anticipate that the prospective follow-up and support provided by a specialised nurse will reduce the negative consequences of a cardiac arrest and will result in a higher level of participation in society and quality of life of the cardiac arrest survivor. Furthermore, we expect that this intervention will be cost-effective.The study presented in this paper is a randomised, controlled clinical trial in which the effect of an early intervention service will be investigated. Participants will be followed over one year. During this year, three measurements will take place, namely two weeks, three months and twelve months after the cardiac arrest  results from research on traumatic brain injured patients with regard to their participation in society assessed using the Community Integration Questionnaire . The mean (sd) total score on the CIQ was 16.09 (sd = 4.20) . An assuBetween three and ten days after the cardiac arrest the patient and caregiver will be approached to participate in the study by their treating physician. Standard procedures concerning informed consent will be used. Both patients and caregivers sign an informed consent form if they decide to participate in the study. In case the patient is still unconscious or incapacitated at the moment that informed consent is needed, the caregiver will be asked to give provisional informed consent. When the patient regains consciousness, definitive informed consent will be sought.Participants will be randomly allocated to either the intervention group or the control group. The randomisation will take place after the baseline measurements (2 weeks after the cardiac arrest) and will be performed centrally by the project leader using a computerised block randomisation. The randomisation scheme includes prestratification on two variables, namely hospital site and location of the cardiac arrest . Two series of opaque numbered envelopes will be prepared for each hospital site , filled with a card indicating the allocated group. After registration, the envelope will be opened and the specialised nurse will be informed about the patients that are allocated to the intervention group.The intervention group will receive an early intervention service delivered by a specialised nurse in addition to usual health care available to all patients, whereas the control group will only receive usual health care. The intervention period will extend up to three months after the cardiac arrest. During this period a specialised nurse will provide one to six consultations for the patient together with his or her caregiver (if present). The number of consultations will be tailored for each individual participant and will depend on the severity of the problems and the needs and wished of the patient and their caregiver. The consultations will take place in the hospital or at the participants' home. In between the consultations the participants have the opportunity to contact the nurses by phone or email. During the intervention period the specialised nurse will screen for cognitive and emotional problems. Next to that, information will be provided by means of an information booklet and by discussing questions that the participants may have. Furthermore, emotional and practical support will be offered and self-management will be explained and promoted. A consultant in rehabilitation medicine supervises the specialised nurses. If necessary, referral to additional diagnostics or specialised rehabilitation care can take place.There will be no restrictions on possible co-interventions. Participants assigned to the intervention group who do not want treatment will still be approached for all the follow-up measurements.Baseline measurements will be administered two weeks (= T1) after the cardiac arrest, with further assessments at three (= T2) and twelve months (= T3). The measurements will be administered wherever the patient is at that time. A research assistant, who will be blinded to the group allocation, will perform the assessments.To check for the effectiveness of blinding, the research assistants will be asked after the last measurement to indicate whether they think the participant belonged to the intervention group or the control group.The following socio-demographical variables will be recorded: Age, gender, nationality, marital status, living condition, educational level, and work situation. The variables related to the resuscitation and medical situation of the patient that will be recorded are presented in Table The primary outcome measures of this trial are participation in society and quality of life of the cardiac arrest survivor. The secondary outcome measures are cognitive, emotional and cardiovascular impairment and daily functioning of the cardiac arrest survivor, as well as the strain, emotional functioning and quality of life of the caregiver. The instruments that will be used to measure these domains are presented in Table The Community Integration Questionnaire (CIQ) is a 15-item questionnaire that assesses the level of participation in society . The CIQThe Impact on Participation and Autonomy (IPA) is a 39-item questionnaire that focuses on two aspects of participation, namely perceived participation and the experience of problems .The EuroQol 6D (EQ-6D) is a generic quality of life measure consisting of 6 items  each with three answer possibilities . In addiThe Medical Outcomes Study 36-item Short Form Health Survey  is a 36-item generic quality of life questionnaire that measures several domains of perceived health .The Quality Of Life after Brain Injury questionnaire (QOLIBRI) is a brain injury specific quality of life measure. The QOLIBRI consists of 49 questions that measure health related quality of life within six domains  .The Cognitive Failures Questionnaire (CFQ) is a 25-item questionnaire on self-reported cognitive failures . Both thThe Cognitive Log (Cog-log) is a 10-item cognitive screening instrument . It measThe Adult Memory and Information Processing Battery (AMIPB) Task A is a measure for information processing speed . In thisThe Paragraph Recall (PR) is a test for immediate and delayed verbal memory . The subThe Trail Making Test (TMT) measures scanning, visuomotor tracking, divided attention and cognitive flexibility . The tesDuring the Verbal Fluency Test (VFT), the subject is asked to name as many words from one semantic category, in this case animals, in one minute .The Hospital Anxiety and Depression Scale (HADS) is a commonly used questionnaire, which was designed to detect the presence of mild degrees of mood disorders in non-psychiatric hospital outpatients . The HADThe Impact of Event Scale (IES) measures psychological reactions that can take place after a traumatic event. The IES has 15 items and explores the level of posttraumatic stress by asking for intrusive and avoidance symptoms ,43.The New York Heart Association Classification (NYHA-classification) is a functional classification system that divides cardiac patients into four classes depending on their limitation in physical activities .The Fatigue Severity Scale (FSS) is a 9-item questionnaire that measures experienced severity of fatigue symptoms in daily activities .The Barthel Index (BI) is a widely used instrument that measures basic  activities of daily living (ADL). The instrument consists of 10 items and measures to what extent a person can perform basic ADL activities independently .The Frenchay Activities Index (FAI) is an accepted measure for instrumental ADL that reports on fifteen daily life activities . CompareThe Caregiver Strain Index (CSI) is a 13-item, dichotomous scale that has to be filled out by a partner, caregiver or significant other . The CSIThe intervention will be evaluated based on information from the specialised nurses and the participants. First, the intervention process will be evaluated based on information provided by the specialised nurse. He/she will record the following items: the actual number of consultations , the direct time related to each consultation, the indirect time related to each consultation , the provision of information per consultation, the use of the screening tools, the kind of problems presented, number of referrals, completion of the intervention according to protocol and, in case of non-compliance, reason for non-compliance. Second, after completion of the intervention the participant and his/her caregiver will be asked to fill out a questionnaire to evaluate the intervention.Data concerning costs will be gathered prospectively using monthly cost-diaries . All theDescriptive techniques will be used to present data concerning the participants, number of dropouts, losses-to-follow up and the scores on the outcome measures.To determine whether the patients participating in this trial are representative for the population of cardiac arrest survivors, the baseline characteristics between compliant and non-compliant participants, as well as dropouts and losses-to-follow up will be compared.Before examining the effectiveness of the intervention, the comparability of the two groups will be checked. Baseline characteristics will be compared using independent sample T-test  or Mann Whitney U-test , in case of a continuous variable. In case of a dichotomous variable a chi-square test will be used.The data from the evaluation study will be analysed according to the intention-to-treatment principle. If either T1 or T2 data are missing the 'last-observation-carried-forward' principle will be used.Multiple regression analysis, adjusted for possible differences in baseline characteristics or baseline scores, will be applied to examine differences between the intervention and the control group on the primary outcome measures at T3 (one year after the cardiac arrest). If there is a difference between the groups, the effect size will be calculated. Subgroup analysis will be performed for potential effect modifiers.To assess whether protocol deviations or care provided outside the intervention have caused biases, the results of the intention-to-treatment analysis will be compared to the on-treatment analysis.The outcomes of the process evaluation will be reported using descriptive techniques.To determine the cost-effectiveness of the intervention a statistical analysis of costs will be performed. The total costs are calculated by adding up direct health care costs, direct non-health care costs and indirect costs. Health care costs are estimated using the Dutch guideline for cost analysis in health care research . The FriA multi-way sensitivity analysis will be performed to gain insight into the generalisability of the economic evaluation.A cost-utility analysis will relate the difference in between the intervention and care-as-usual to changes in utility. This will result in costs per quality-adjusted-life-years (QALY's).The main goal of the ALASCA trial is to evaluate a new early intervention service for cardiac arrest survivors and their caregivers. This is a relevant topic because evidence on effective rehabilitation programmes for cardiac arrest survivors is urgently needed. During the design of this study many choices and selections were made, and four of the more important ones will be discussed.First, the intervention was designed especially for this study. There are very few studies describing programmes for people with hypoxic brain injury due to a cardiac arrest. Consequently, we chose to model the intervention upon evidence of effective interventions for people who had traumatic brain injury, as there are many similarities between hypoxic and traumatic brain injuries . This inSecond, the selection of our primary outcome measures, participation in society and quality of life, may differ from common primary outcome measures in cardiology research. However, this is a common approach in rehabilitation medicine, where maximisation of participation in society is one of the main goals . We expeThird, it may need some clarification why three quality of life measures will be administered. The EQ-6D and the SF-36 were chosen because they measure generic quality of life and can be used in the cost-effectiveness analyses. Next to that, these questionnaires have been used in many other patient groups, which makes it possible to compare the quality of life of cardiac arrest survivors to that of other patient groups. The third quality of life measure is the QOLIBRI, which is a brain injury specific questionnaire. This questionnaire was included because it may have a higher sensitivity to change in case of hypoxic brain injury than both general quality of life measures.Fourth, we explicitly chose to include the strain for and quality of life of the caregiver in the secondary outcome measures. Our impression is that being the partner of a cardiac arrest survivor can imply a heavy burden. These caregivers are at risk for developing stress and emotional problems and this definitely needs more attention. Therefore, the intervention is directed at both the patient and their caregiver, who is expected to benefit from the early intervention service as well.To conclude, this paper describes the design of a randomised, controlled clinical trial that will investigate the effectiveness of a new early intervention service for cardiac arrest survivors and their caregivers. The inclusion of the participants started in April 2007 and will continue until April 2010. The results of this study will provide evidence on the effectiveness of this early intervention service, as well as the cost-effectiveness and its feasibility. This will give insight into the question whether it would be useful to implement this early intervention service in the Dutch health care system.The author(s) declare that they have no competing interests.VRMP is the main researcher and is responsible for writing the protocol. JAV and WGMB originated the idea for the study. JAV is project-leader of the study. JAV and CMvH are the supervisors of VRMP. DTW is the promotor of VRMP.JAV, CMvH, WGMB, APMG, SCAMB, MCFTMdK and DTW participated in the design of the study and research protocol. All authors read and corrected draft versions of the manuscript and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria  at the same time and the cost of these methods allows for the use of more values to help discover the underlying biological mechanisms.As several methods assume different null hypotheses, we propose to reformulate the main question that biologists seek to answer. To determine which genesets are associated with expression values that differ between two experiments, we focused on three ad hoc criteria: expression levels, the direction of individual gene expression changes (up or down regulation), and correlations between genes. We introduce the FAERI methodology, tailored from a two-way ANOVA to examine these criteria. The significance of the results was evaluated according to the self-contained null hypothesis, using label sampling or by inferring the null distribution from normally distributed random data. Evaluations performed on simulated data revealed that FAERI outperforms currently available methods for each type of set tested. We then applied the FAERI method to analyze three real-world datasets on hypoxia response. FAERI was able to detect more genesets than other methodologies, and the genesets selected were coherent with current knowledge of cellular response to hypoxia. Moreover, the genesets selected by FAERI were confirmed when the analysis was repeated on two additional related datasets.The expression values of genesets are associated with several biological effects. The underlying mathematical structure of the genesets allows for analysis of data from several genes at the same time. Focusing on expression levels, the direction of the expression changes, and correlations, we showed that two-step data reduction allowed us to significantly improve the performance of geneset analysis using a modified two-way ANOVA procedure, and to detect genesets that current methods fail to detect. The major issue when studying datasets with many tests and few replicates in general and microarray datasets in particular is the decreased power of the analysis. Indeed, to reduce the risk of detecting false positives due to the lack of a sufficient number of replicates, statistical tests allow for higher threshold levels as the number of replicates decreases. This implies an unavoidable increase of the number of false negatives and, thereby, a decrease in power.To compensate for this lack of information about expression data generated by microarrays, some authors have tried to work at the probe level, breaking down probesets into smaller components. Nonetheless, most researchers prefer to work at the probeset level where data can be analyzed in different layers  .Among these methods, the analysis of genesets with regard to known or observed biological criteria holds high potential for fundamental as well as clinical research. For example, members of the same metabolic pathway can be studied simultaneously to assess modifications of the pathway under certain conditions, such as the action of a drug, or to diagnose a pathology, etc. . This la- group discovery: empirical data is used to identify new criteria to discover new genesets. This task uses several different approaches such as data clustering -9 and th- geneset analysis: previously defined genesets are used to guide empirical data analysis, to determine whether the expression values of known genesets are correlated with the conditions compared in the experiments -17.0): "Which known genesets are associated with different expression profiles under the two conditions compared?". We discuss here the ability of current methods to provide an answer to this question.The research presented here focuses on statistical geneset analyses. Our initial motivation was to answer the seemingly trivial question  may be expressed at various levels, over- or under-expressed or correlated (coordinated response of member genes).This main question Q0, we present the FAERI methodology , an ANOVA-2-like procedure tailored to detect genesets with differing expression profiles, regardless of the nature of the individual responses.To provide an answer to question Q0. The methods discussed below were selected to provide an overview of the main strategies followed by the authors, or due to their pioneering approach.Several geneset analysis methods have been published recently. Each available method provides an answer to question QOver-representation analysis methods are not considered in this paper, as they focus on the top list obtained by individual analysis and aim to identify only those groups of genes that are represented by the most differentially expressed probesets. This approach thus facilitates the interpretation of individual analyses, by giving annotation clues about biological meaning. However, such methods require the ad hoc selection of an individual statistic, and the definition of an arbitrary threshold for the detection of individual genes, whatever the individual statistic used. Thus, over-representation analysis does not allow for the detection of genesets associated with subtle individual changes according to the individual statistic chosen -21. For Table comp  methods can be classified based on their competitive or self-contained null hypothesis. Competitive methods sort the groups focusing on question Qmp Table  23-28].-28.comp lf Table . The sigThe GSEA methodology and its derivatives make use of a hybrid strategy providing results that are harder to interpret. The null model used to develop the methods relies on the competitive approach, but is later evaluated by label sampling, thus mixing both strategies ,25,27.The second methodological difference between the methods concerns definition of the geneset score evaluated. Methods using two steps first compute an individual statistic that is then used in a second step to compute a geneset statistic -28,33. C0. Therefore, we propose to reformulate the hypothesis used with the available methods with respect to the biological properties of the sets , data used (4th column), statistic used (5th column) and significance evaluation procedure (6th column). The other columns provide additional information.Table 0 includes all of the other questions  related to the nature of the groups tested. Three criteria must be taken into account to describe the groups: expression levels (a), the direction of the expression change (b) and correlations between the group members (c).Question Qt or SAMGS d statistics characterize expression differences, regardless of the individual expression levels [(a) It is important to study expression levels as highly expressed probesets may mask differences observed for probesets with lower expression levels. Two-step methods such as the SAMGS are not influenced by this criterion. For example, the Student n levels . Moreovet [2d) and expression change variability (2τ) [(b) Individual expression values may reveal up or down regulation of the genes depending on the conditions tested. One way to take the direction of expression changes into account and deliver results with no dependence on this criterion is to avoid studying mixed groups with a global null effect (50% over and 50% under expressed). Two-step methods may be adapted to use the absolute value of a unidirectional individual statistic (Student t ), or itst ). Alternt . Among gity (τ2) ,31.0 takes both possibilities into account. Two-step methods based on a sum or a mean, like the SAMGS, do not need further adaptation since they explicitly rely on the sum of individual pieces of information [uC, see Table (c) Genes may be associated with correlated or variable expression changes. Question Qormation ,23,28,300.It appears that only two-step methods, as well as the SAMGS, may simultaneously provide an answer to the three criteria considered and thus evaluate more completely question QSoftware packages developed thus far are not always able to fine-tune the appropriate combination of steps with regards to the criteria. Moreover, it is not systematic to take the direction of the answer into account or to rely on partial genesets  ,28,30,33Global methods, on the other hand, ignore the possible bias associated with expression level. The SAMGS procedure involves a preliminary step of variance stabilization based on the SAM individual analysis method, which requires that the expression values of all probesets be estimated ,36-38.Competitive procedures aim to identify groups with bigger differences and ignore groups associated with the phenotype but with a moderate answer compared to the others. The self-contained null hypothesis thus provides a more appropriate answer, where each set is evaluated according to its possible distribution range -28,33.0 as completely as possible. Several methodologies may be selected to answer complementary questions. Gentleman et al. previously suggested to separately study sub-groups defined by the direction of expression changes and to define core sets of genes common to several genesets . As a perspective of the GlobalAncova methodology, Mansmann and Meister proposed to study the interaction between the effect of the condition and group composition, to take the variability of the individual responses into account. The hypothesis associated with this test complements the unidirectional null hypothesis, but also provides a partial answer to question Q0 since it ignores unidirectional correlated groups (no interaction).There are several possible strategies to evaluate question Q0 based on a single self-contained test. To optimally analyze expression data, our model relies on a global strategy because two-step methods suffer from a loss of information (correlation) caused by the substitution of data with individual statistics. We introduce the FAERI method that was tailored from the 2-factor ANOVA procedure. Our aim was to group the information associated with individual differences into one single statistic taking direction, correlation and expression level into account and to combine the advantages of the GlobalTest (groups with a variable answer), GlobalAncova  and SAMGS (independence with expression level) models whilst avoiding their respective limitations.The work reported below proposes an alternative strategy to answer question QBosco et al. (2006) described the E-MEXP-445 dataset, available in the ArrayExpress repository. The experiment compares samples extracted from human monocytes. RNA was hybridized on 6 Affymetrix HGU-133a microchips, among which 3 normoxia samples and 3 samples from cultures grown under hypoxic conditions .Vengellur et al. (2005) studied cellular response to hypoxia on human Hep3b hepatocytes cell lines, considering 3 additional treatments known to mimic hypoxic conditions: cobalt chloride (100 μM), nickel chloride (100 μM), and DFO (100 μM). mRNA was extracted separately from two biological replicates, and cRNA was hybridized on Affymetrix GeneChip HG-U95Av1 microchips. We used part of this dataset to compare samples grown under normoxic and hypoxic conditions, and did not use the other samples (used by the authors to compare hypoxia with molecules that mimic hypoxic conditions). This dataset is available on GEO (GSE-1056) .Kim et al. (2006) described the GSE-4086 dataset, available on GEO. The authors studied cellular response to hypoxia on human B lymphocytes (P493-6 cell line). Two biological replicates were used to extract mRNA from separate cultures. cRNA was hybridized on Affymetrix GeneChip HG-U133A microchips .In the work reported here, the three hypoxia datasets were preprocessed using GCRMA to study the effect of hypoxia and compare the ability of each geneset analysis method to detect common pathways from several datasets featuring a limited number of replicates.Xiao et al. (2008) report a study of osteoporosis using microarrays hybridized with 10 low and 10 high BMD samples . B-lymphocyte mRNAs were isolated from 70 ml of blood, and cRNA was hybridized on Affymetrix GeneChip HG-U133A microchips. This dataset is available both on GEO GSE-7429) and ArrayExpress (E-GEOD-7429) [9 and ArrAs several genes are associated with multiple probesets, a bias could be introduced during the analysis at the geneset-level. In the work reported below, we used only one probeset for each gene studied on the microchip, by selecting the probeset associated with the largest variability, to avoid giving too much weight to a gene due to the number of associated probesets. As a result of this selection, the words "probesets" and "genes" are synonyms in this text. The multiplicity of the probesets is not considered in this publication.t test [The analysis of variance or ANOVA relies on the study of the variability of the data with respect to one or more criteria to determine if that(those) criterion(a) is(are) responsible for the values observed. The simplest model, relying on a single criterion is equivalent to the Student t test . When anThe model that we chose to test the validity of this approach states that the expression data observed can be explained by dependence of the intensities measured on the probesets, the condition and the interdependence between those two parameters, since each probeset can potentially be associated with a different response under each of the tested conditions.The mathematical representation of this model is given by Equation 1.i is the contribution of factor a, bj is the contribution of factor b, abij is the contribution of the interaction of factors a and b and E(ij)k is the residual contribution.Where μ is the general mean, aThe comparison between the different criteria relies on the ratio of the mean squares associated with each criterion. The statistic computed for each criterion is evaluated by Fisher's F distribution using the appropriate number of degrees of freedom (equations 2 to 4).x is the F score associated with factor x, MSx is the mean square value associated with factor x, nx is the number of levels for factor x.Where FIt is important to note that when the effect of the interaction is significant, no decisions can be made for the other criteria. The study of the effect associated with the condition is close to the GlobalAncova method ,44. The Geneset analysis of differential expression is influenced by the direction of the expression changes of the members and by possible interactions between the probesets and the conditions ,31,44-46t is unchanged. Note that this procedure renders data closer to the conditions of application of the ANOVA 2 .Probesets belonging to the same geneset can be expressed at various levels. From a biological point of view, a weakly expressed geneset might have a bigger impact on physiology than a strongly expressed one. We propose to standardize the data so that, in principle, all the probesets have the same potential for response under two conditions compared, based on a classical reduction of data producing Z values. The standardization is computed for each probeset using Equation 5 by pooling the data associated with both conditions and computing the observed mean and variance. The resulting reduced data is distributed around a mean value of 0 with a variance of 1. The information about the difference between the conditions is retained. At the end of this operation, the result of an individual analysis based on the Student ij is the expression value associated with the ith probeset in the jth microchip, Mpool, i and Spool, i are the means and standard deviations computed for probeset i from all experiments (step 1). This procedure is repeated for each probeset in the dataset.Where X0, with no dependence on the direction of differential expression, two approaches can be proposed: up- and down-regulated probesets may be studied separately or simultaneously by computing a cumulative value for the absolute difference. The first solution consists of defining "activated" or "repressed" genesets to conduct a proper analysis. This approach relies on current knowledge that is incomplete, but is useful for diagnosis purposes to determine whether a patient has a pathology by analyzing signature genesets. The second solution is addressed by several methods by defining an individual statistic independent of the direction  [Several probesets may be activated or repressed at the same time, but may also be associated with a variable expression change, depending on the biological context studied and the definition of the genesets. To provide a full answer to question Qatistic) ,37,47,48Using a multivariate procedure, we propose to study the absolute response of the genes by directional reduction of the data. This operation involves multiplying the expression values related to down-regulated genes by -1 (Equation 6). The down-regulated genes are identified empirically from the expression data studied.i = MiB - MiA, the difference in means between conditions A and B for the ith probeset, Zij = Z-score associated with the ith probeset in the jth microchip, and ZDij the directionally reduced Z-score associated with the ith probeset in the jth microchip.Where Dt test when using the median as the estimator [It may be interesting to consider the median instead of the mean to evaluate the direction of differential expression. However, the performances tests we conducted previously in individual expression analyses show a reduction in the performance of the Student stimator  on biolot statistic remains unchanged.After this 2-step reduction of the data, FAERI evaluates an F statistic as is done in the ANOVA-2 procedure (Equation 1). The FAERI F* statistic associated with the condition effect is representative of the differences found based on biological reasoning. Each gene provides information on its differential expression, independently of the direction of that difference, and the absolute value of the individual Due to the dependence on geneset size introduced by directional reduction, the significance of the results cannot be evaluated using Fisher's theoretical distribution, which is used by the ANOVA procedure. We will refer to the statistic computed using the FAERI methodology as F*. We tested two solutions to determine the null distribution of the FAERI F* statistic, relying either on permutations or the use of random data.The permutation strategy associated with a self-contained null hypothesis relies on label sampling ,29. Give0 values. Under the null hypothesis, the F* statistic computed on reduced test data should follow the same distribution as the F*0 statistic computed from reduced random data. Conversely, if the expression values of the geneset differ between two conditions, the F* statistic distribution leads to higher values than the F*0 values computed from random data.Due to the Z value reduction step, a second strategy may be used to evaluate the null distribution of the FAERI F* statistic. Indeed, as the null hypothesis states that there are no differences between the two conditions compared and as we postulate that individual expression values follow a normal distribution, Z standardization provides the possibility to establish the null distribution using random data from a normal distribution, and then apply the same 2-step reduction to compute the reference F*This possibility to compute a reference distribution based on random data frees the FAERI method from the limited number of available permutations. Moreover, for each experimental strategy (number of replicates) and each geneset size, the null distribution can be computed once and stored for future analysis.As presented in the introduction, we propose to take the level of individual expression into account, using a first-step reduction of the data. Figure In the second step of FAERI, we used a directional reduction to obtain a cumulated value of absolute expression changes in the genesets. We chose to use an empirical determination of the direction, easier to assess from the data than from prior incomplete knowledge. As the sign of the reduced values is not a feature of the biological mechanisms involved within genesets, the description of the mechanisms involved should be associated with individual analysis of the member genes based on raw data.Figure We tested two procedures to assess the significance of the F* statistic. Figure Mansmann and Meister recommend assessing the significance from permutations instead of the Fisher distribution when analyzing microarray data with a multivariate procedure. Their GlobalAncova procedure evaluates the significance of the test using the label-sampling strategy instead of the classic Fisher distribution . The GloSeveral authors have stressed the need to compare the performances of geneset analysis methods with regard to the correlation between geneset members ,29,50. TWe reproduced the simulation proposed by Ackerman et al. to assess the performances of ANOVA-2, FAERI and several geneset analysis methods currently available, to obtain a comprehensive understanding of the performances with regards to the mathematical structure of the data. Each set was simulated 100 times and Table Comparison of the results illustrates the effect of correlations between members of the sets. For each method tested, the presence of intra-set correlations reduces the performance of the analysis. For the simplest simulated sets , ANOVA-2 provides the best results, followed by FAERI  and GlobalTest, as evaluated using a gamma distribution or permutations (set 5).Moreover, the ANOVA-2 and FAERI procedures are both associated with higher performance compared to all other methods based on the simplest simulation scenario . Indeed, FAERI detects set 1 in 62% of the cases, with a threshold of 0.001 and no false positives (set 3: 0%). At this threshold, FAERI is able to detect roughly 5 times more genesets than the best performing method currently available . As a comparison, SAMGS and GlobalTest require a threshold that is 100 times higher to detect only 60% of set 1. Comparison of sets 4 and 5 highlights the method's abilities to detect genesets where half of the members are not differentially expressed. In this case, FAERI detects 43% of the genesets, compared to 15% when applying GlobalTest with a gamma distribution. The ANOVA-2 and FAERI procedures thus appear to be the most appropriate to analyze unidirectional genesets.The lower part of Table To ease interpretation of method's performances with regard to the mathematical structure of the genesets analyzed, Table Table • ANOVA-2 is the most appropriate method for unidirectional groups;• GlobalTest, which uses a statistic representative of response variability, is suited for the study of both uni- and bidirectional non correlated genesets;• SAMGS, using an individual statistic, appears to be less appropriate for the analysis of correlated genesets, since this information is lost during the procedure;• FAERI, developed to detect sets independently of their mathematical structure, is appropriate for each type of set tested;• ANOVA-2 and FAERI provide an appropriate analysis of sets with correlated members, respectively using unidirectional and bidirectional definition of sets;• Methods making use of a two-step strategy provide lower performance levels (GSA and GSEA).Analysis of a genuine biological dataset offers the opportunity to qualitatively evaluate the results and provide a biological validation of geneset analysis methods through comparison of the results with current biological knowledge. We chose to use three datasets related to response under hypoxic conditions. The definition of the genesets relies on the MSIGDB databank (version 2.5), published by the authors of the GSEA methodology . MSIGDB Table Table This list can be split into 3 categories. First, genesets concerning several metabolic pathways suggest metabolic adaptations occur as a cellular response to oxygen deprivation. Second, several genesets address signaling pathways and are known to be involved in the hypoxic stress response. Genesets of the third category are related to pathologies, mostly involving cancers, for which a hypoxic environment has been observed -54. ThesThese observations should be followed-up by a characterization of the genesets detected by the other methods. Table As Table Setting a significance threshold of 1% for the p-values, the ranking of the methods detecting more genesets common to the two experiments is the following: FAERI.permutations (6 common genesets for at least 14 genesets detected), ANOVA-2 (5/35), GlobalTest.gamma (1/8) and GSA.mean (1/14). The other methods do not intersect. When the significance threshold is set to 5%, the ranking of the methods is the following: FAERI.permutations (34/42), GlobalTest.gamma (20/45), FAERI.null (6/18), ANOVA-2 (15/51), GlobalTest.asymptotic (4/26) and GSA.mean (1/8). The other methods do not intersect. These results suggest that the bidirectional genesets, detected by FAERI and GlobalTest, are an important part of the genesets simultaneously involved in both datasets, compared with the ANOVA-2 and GSA.mean procedures.To quantify the abilities of the methods to provide the same result from different datasets, we propose to compute Pearson's coefficients of correlation on the ranks associated with the genesets analyzed, by pairwise comparison of the three datasets, for each method and each category of genesets defined , and functional class scoring (FCS). This last category of methods relies either on 2-step (post-hoc) or global strategies (using raw data).Functional Analysis: Evaluation of Response Intensities). FAERI is a global methodology tailored from a 2-factor ANOVA procedure by a 2-step reduction of the data, and is evaluated with respect to the self-contained null-hypothesis (using label sampling or random data).In this paper, we address this question by considering the biological properties of genesets with regard to the underlying mathematical properties of the associated expression values. Focusing on expression levels, the direction of regulation and potential correlations between geneset members, we developed the FAERI methodology (0 ("Which known genesets are associated with different expression profiles under the two conditions compared?").Evaluations performed on random data reveal the ability of each method to detect sets simulated according to 8 scenarios, relying on mathematical properties. The ANOVA-2 procedure performs best when analyzing unidirectional genesets and FAERI performs similarly. Both methods outperform each currently available method. When the definition of the sets involves members regulated in both directions, ANOVA-2 performance drops, as expected, and our proposed FAERI methodology outperforms all tested methods. Both methods provide an appropriate solution for the analysis of correlated data . FAERI thus constitutes an improvement over all tested methods, and turns out to be the optimal method for testing question QA real-world example of analysis is reported for three datasets on cellular response to hypoxia. The results obtained from analysis of the E-MEXP-445 dataset illustrate that FAERI  is able to detect relevant results when a strict cut-off is used, compared to the other methodologies. The genesets detected by FAERI are related to three categories, respectively metabolic perturbations, hypoxia signaling/response, and hypoxia-related pathologies. The results of other global methods confirm these results, and post-hoc methods fail to detect any significant genesets. The results provided by FAERI from analysis of the two additional datasets reveal its ability to detect the same sets using several related datasets. Focusing on the top list obtained with several datasets, we showed that FAERI not only detects more sets, but also presents a larger intersection of the results.We proposed to score this last assessment on the whole list of sets by computing the Pearson correlation coefficient on the ranked list of genesets, for each method, using pairwise comparisons between the three datasets. Among all methods tested, FAERI provides the best correlated results between related datasets, regardless of the source of geneset definition used (each category of the MSIGDB databank was used for this purpose). FAERI thus outperforms all methods tested for its ability to attribute similar scores to each geneset from several datasets.Several recommendations emerge from this work. First, we suggest that differential expression analyses of microarray data be studied first by performing a geneset analysis with FAERI, and then that the results be further characterized using either an individual analysis or mathematical characterization of the sets detected. This approach eases interpretation of the results with regard to the correlation and direction of probeset-specific expression changes encountered for each geneset. Alternatively, genesets can be first classified with regard to the mathematical properties of the geneset expression values, and then the data can be analyzed using distinct methodologies .As a perspective for future work, we suggest to adapt FAERI so it would be able to test unequal sample datasets. Several adaptations to the ANOVA-2 procedure have been suggested to this end. To best adapt FAERI for this purpose, performance evaluations are required. Another interesting development would be to develop robust variants of FAERI based on the study of the impact of extreme values.An additional comment may be formulated about the empirical definition of the direction from the difference in means, used in the second step of the FAERI procedure. Indeed, genes slightly differing in one direction may empirically seem to be involved in the opposite direction, as sampling a small number of replicates can lead to a smaller or larger distribution than the population. Thus, the directional reduction step may lead to an incorrect definition of the sign of the expression change with regards to the biological structure of the data. An alternative would be to characterize first the direction associated with each gene based on current knowledge of the biological processes. Similarly, another alternative adaptation of the procedure would be to determine which genes are associated with an expression change, and to use the directional reduction step only on those genes. However, the use of prior knowledge on the expression and directional changes for each gene would be associated with important biases: incorrect prior information would reduce the significance of the geneset analysis, and this bias is more probable for less-studied genesets/biological mechanisms. Thus, the evaluation of the null distribution with realistic background noise would be geneset specific, depending on the number of errors in the annotation. In the future, we also plan to develop a non-parametric equivalent to FAERI, by applying a rank-based multivariate analysis procedure after the 2-step reduction of data. At least three options may be considered for further developments: ranking expression values at the level of the probesets, at the level of the genesets, or at the level of the whole dataset.The evaluation of significance may also lead to several adaptations. First, it would be interesting to add a third significance evaluation strategy based on simultaneous label sampling, where permutations would be performed without breaking the association with the cel file. Such a null distribution would provide an appropriate p-value for the evaluation of highly correlated genesets. However, the small number of available permutations would not allow for discrimination between genesets when the number of replicates is small. Both approaches introduce a bias during the p-value evaluation procedure: (i) the independent model assumes that the genes are not correlated, under-evaluating the p-value of co-occurring events; and (ii) the correlated model accordingly over-evaluates the p-value when the correlation between genes is not perfect. As the correlation pattern between genes is complex, the most appropriate p-value evaluation procedure should be somewhere in between the independent and correlated permutation models, with a geneset-specific balance between these two models.Another interesting perspective for evaluation of the p-value would be to improve the model used to generate the null distribution from random data. By studying several datasets first using individual and geneset analyses, it would be possible to characterize the correlation between genes in several situations. Then the network of associations could be used to count the average number of connections, to catch patterns of association, etc. Using such a strategy, the evaluation of the null hypothesis would consist in generating random networks with similar patterns of association and similar distributions of connectivity, and to use these patterns using multivariate random data built from the simulated correlation matrices. As an extension of this model, it would also be possible to include a supplemental parameter during simulation of the null distribution: the correlation pattern between genes may differ between the two groups.Performance evaluation of geneset analysis methods is not a trivial task, as each method is based on distinct null hypotheses. Previously published methods have been validated following several simulation schemes (using a limited number of scenarios) or running analyses on specific biological datasets (featuring more than 10 replicates for each tested condition). Nevertheless, those validations usually focused on the method developed by the authors, compared with only one method , if any, and a limited number of genesets. Conversely, publication of individual analysis methods requires extensive validations on real-world, simulated and spike-in datasets. The validations reported here reproduce the simulation scheme used by Ackerman et al. (2009) as it represents the most comprehensive scenario we could find in the scientific literature (in a paper that compares the parameterization of current methods). The example analysis reported here was performed on small datasets (3 replicates associated with each condition), as this kind of dataset is far more frequent and more difficult to analyze. We hope that the validation strategy reported here will be reproduced by authors of future methodologies to compare their findings with current methods and to provide a comprehensive evaluation of their methods with regards to several biological scenarios and the underlying mathematical properties of the expression values.In a previous work, we reported a new benchmarking strategy for individual expression analysis on real-world data involving several datasets . In futuhttp://urbm-cluster.urbm.fundp.ac.be/phoenix)FAERI and the ANOVA-2 procedure were implemented using R language, and the source code is included in PEGASE, an R package we designed for differential expression analysis . URL: (hFB scripted the FAERI methodology and performed the analysis presented here. BDM took part in the methodology performance assessment and creation of the methodology. AG took part in analysis and interpretation of the results. SD took part in analysis of the biological data. EB took part in scripting and automation. MP took part in interpretation and collection of the biological data. BDH took part in creation of the methodology and scripting. MD took part in the genesis of the methodology. ED coordinated the work and gave final approval for submission. All authors read and approved the final manuscript.Procedure section, describing the mathematical computing of FAERI. Pearson's correlation coefficients computed on ranks, for each method between 3 datasets, for each geneset definition. Figure Click here for file


To compare two approaches to the statistical analysis of the relationship between the baseline incidence of adverse events and the effect of medical emergency teams (METs).Using data from a cluster randomized controlled trial (the MERIT study), we analysed the relationship between the baseline incidence of adverse events and its change from baseline to the MET activation phase using quadratic modelling techniques. We compared the findings with those obtained with conventional subgroup analysis.Using linear and quadratic modelling techniques, we found that each unit increase in the baseline incidence of adverse events in MET hospitals was associated with a 0.59 unit subsequent reduction in adverse events (95%CI: 0.33 to 0.86) after MET implementation and activation. This applied to cardiac arrests , unplanned ICU admissions  and unexpected deaths . Control hospitals showed a similar reduction only for cardiac arrests . Comparison using conventional subgroup analysis, on the other hand, detected no significant difference between MET and control hospitals.Our study showed that, in the MERIT study, when there was dependence of treatment effect on baseline performance, an approach based on regression modelling helped illustrate the nature and magnitude of such dependence while sub-group analysis did not. The ability to assess the nature and magnitude of such dependence may have policy implications. Regression technique may thus prove useful in analysing data when there is a conditional treatment effect. Following the landmark reports from the Institute of Medicine (IOM), numerous programs designed to improve patient safety have been introduced -6. RigorThe medical emergency team (MET) was first introduced in Australia in the early 1990s. Its main aim is to reduce unexpected deaths, cardiac arrests and unanticipated Intensive Care Unit (ICU) admissions -14. The However, statistical analysis of data from the Medical Early Response & Intervention Therapy (MERIT) study, a 23-hospital cluster randomised controlled trial of the MET system, failed to show a difference in the aggregate incidence of unexpected cardiac arrests, unexpected deaths and unanticipated ICU admissions between MET and control hospitals . The priThe baseline incidence of a specified outcome is an important hospital performance characteristic that may predict the magnitude of improvement in response to an intervention . It variThe aim of this study was to compare a regression based approach with subgroup analysis in terms of its results and empirical interpretation when the baseline outcome proved to be a continuous variable and treatment effect modifier. Accordingly, we developed a methodology that incorporated regression-modelling techniques and applied it to the MERIT study data. With this methodology, we studied the relationship between the baseline incidence of adverse events and the change that occurred during preparation for and activation of METs. We then compared the findings identified with this approach with those obtained from conventional subgroup analysis.baseline period. This was followed by a four month standardised implementation period during which education was delivered on the concepts and practice changes required with the introduction of the MET and then by a six month study activation period during which the MET system was operational [The sample recruitment, size calculation, ethical approval, and randomisation scheme for the MERIT study have been described previously . The prirational . The MERData collection was conducted in control hospitals during the same time periods. The conduct of the study was not publicised in the control hospitals, and the management and resuscitation committees of the control hospitals agreed that the operation of their cardiac arrest teams would continue unchanged during the study.To test our hypothesis, we used the previously published MERIT data. We set the change in incidence of adverse events as the dependent variable. We then tested for interaction effects (both linear and quadratic) between the baseline incidence of the primary and secondary outcomes and treatment allocation (MET versus control). We used an analytically weighted regression model. We weighted this model by the number of admissions during the study period. This weighting is an extension of the weighted-t test often used in cluster randomised controlled trials -31. ThisWe analysed the primary outcome and secondary outcomes in the same way. First, we fitted a quadratic model. If the model detected a significant quadratic effect, these results were presented together with the results of the linear effect model for comparison. If the model detected no significant quadratic effect, a linear effect model only was fitted and presented. This was done to minimize the problem of multi-co linearity. This model showed that only unexpected cardiac arrests had negative linear slopes for both groups. We examined the statistical difference between these two slopes by testing the interaction effect between treatment group and the baseline incidence of unexpected cardiac arrests. We then addressed the issues of a) small sample size, b) lack of normality, and c) possible heteroscedasticity. We did this by comparing the analytically weighted regression modelling results using two different methods: the ordinary least square method and the heteroscedasticity-consistent covariance matrix estimation method (HC3) ,32. We aFor the purpose of this analysis, we followed convention and split the sample into two groups: those hospitals equal to or above the median baseline value and those hospitals below the median baseline value for a given outcome. We then used the weighted-t test for the analysis of both sub-groups separately.All the analyses were performed using Stata™ 9.2 .The baseline data for hospital and patient characteristics and the numbers for each event have been presented previously . The basTable Baseline, study and change in incidence from baseline to study period for the primary and secondary outcomes showed large variability Table . The relFor both MET and control hospitals, the greater the baseline incidence of unexpected cardiac arrests detected, the greater its reduction during the study period. For every 10 additional baseline cardiac arrests per 1000 admissions, there was an additional reduction of 9.45 and 7.36 arrests for control and MET hospitals, respectively. Furthermore, the baseline incidence of unexpected cardiac arrests accounted for 78.2% and 85.1% of the variance of this change, respectively. The interaction effect test showed no statistical difference. The baseline incidence had no impact on the change in unplanned ICU admissions in control hospitals. In contrast, in MET hospitals, there was a significant linear relationship between the baseline incidence and its change during the study period. In MET hospitals, for every 10 additional baseline events per 1000 admissions, there was an additional reduction of 5.56 events (55.6%). Furthermore, the baseline incidence of unplanned ICU admissions accounted for 64.3% of the variance of this change. In comparison, in control hospitals, it only accounted for 11.2% of this variance.The baseline incidence showed a significant quadratic relationship with the change in unexpected deaths in control hospitals and a linear relationship in MET hospitals. In MET hospitals, for every 10 additional baseline unexpected deaths per 1000 admissions, there was an additional reduction of 6.76 events (67.6%). The baseline incidence accounted for 81.9% of the variance of this change. In comparison, in control hospitals, it accounted for 92.3% of this variance.The relationship between baseline incidence and its change between baseline and implementation periods for each outcome is shown in Table There was no significant interaction effect (p = 0.081) for a dichotomized modifier using the baseline median value as cut-off. Logically, therefore, there was no need to conduct further subgroup analysis. Nevertheless, the following subgroup analysis was presented for demonstrative purpose. Table We applied a statistical approach that incorporates regression-modelling techniques to the analysis of data from the MERIT study; a cluster randomized controlled trial of the implementation of Medical Emergency Teams (METs). We compared this approach to one based on the conventional method of using a median cut off value to separate groups and then performing subgroup analysis. We found that an approach incorporating regression modelling detected a significant effect of the baseline incidence of the adverse events upon the subsequent effect of introducing METs. In contrast, conventional subgroup analysis did not. The findings based on regression modelling suggest that the baseline incidence of cardiac arrests, unplanned ICU admissions, and unexpected deaths has a significant association with their subsequent reduction after the introduction of a MET. They also indicate that the magnitude of this reduction was proportional to their baseline incidence. Finally, they demonstrate that the choice of statistical analysis plan can significantly affect the interpretation of the outcome data obtained during MERIT The MERIT study paper was designed to compare the incidence of the primary and secondary outcomes during the study period and powered according to the expected size of the therapeutic effect and the expected incidence and variance of the primary outcome . AccordiGiven the above considerations, we set out to test whether choice of statistical technique affected our ability to detect the impact of baseline incidence of adverse events on the effect of introducing MET systems. We used regression modelling techniques to explore the relationship between the baseline incidence of adverse events and its change. The main advantage of using the change of the outcome as the endpoint is in its intuitive interpretation. That is, a lot of system and policy initiatives aim to affect change. The changes incurred by the intervention are often the outcomes we want to understand. Also given that the distribution of baseline incidences was balanced and the before-after correlation was high for the primary endpoint, the efficacy gain of using an ANCOVA approach may have been negligible . We asseThe MERIT main-effect analysis, an unconditional valid analysis, showed that benefit of MET may be small on average. Our conditionally analysis showed that the benefit may still be large among those hospitals with high baseline incidences of the study outcomes. The observations derived from statistical modelling suggest a 'proportionality effect'. That is, the relative change in the incidence of an outcome was similar across all levels of baseline incidence, but the absolute change became greater as baseline incidence increased. This phenomenon has been described with other interventions . For exaOur results are consistent with the notion that the effectiveness of interventions in complex organizations may be dependent on specific local characteristics. They also suggest that baseline characteristics of individual hospitals should be explored when assessing the possible effectiveness of interventions directed at system change. The strong predictive effect and the magnitude of the variance of improvement explained by the baseline incidence of outcome variables also suggest that such outcomes may be useful indicators of quality of care. They are relatively easy to measure, define, and report upon in a timely fashion.x and y. It states that if x is above its mean, then the associated y is likely to be closer to its mean than x was. The conventional way to assess data for the presence of this effect in MERIT would be to compare the pattern of results in MET hospitals with those in control hospitals [The regression towards the mean effect may explain the baseline effect seen in MET hospitals with our statistical modelling approach. Regression toward the mean effect, sometimes called the regression effect, is a statistical principle concerning the relationship between two linked measurements, ospitals . The difospitals . Moreoveospitals . StratifOur statistical approach avoided the possibility of misspecification by incorporating a higher order interaction effect. Secondly, by not splitting the sample using a median value cut-off, our approach decreased the problems associated with loss of statistical power. It may be possible to effectively carry out a sufficiently powered conventional subgroup analysis in a very large randomized controlled trial. However, the MERIT study had only a 25% power to detect a 30% difference in the primary outcome and more than 100 hospitals would have been needed to give it sufficient power. These features of the MERIT study make it unlikely that one could detect a statistical difference when comparing 5 to 6 hospitals within two arms. A further aspect of our modelling approach is that, although we analysed MET and control hospitals separately, our conclusions are based on the comparison of the same results between two groups. This approach compared the relationship of baseline performance with study outcome between control and treatment groups and used all data to draw conclusions. Subgroup analysis, instead, assessed treatment effects in both lower and higher baseline performance groups, respectively. This aspect is a further critical factor in making it less likely that the different patterns seen were due to the regression-toward-the-mean phenomenon. The advantage of this regression approach over subgroup analysis may be that it allows for smooth linear or nonlinear relationships with baseline variables. These smooth relationships may be plausible in some settings such as in the MERIT study. However, one potential disadvantage of our approach is that, due to the existence of a significant quadratic interaction effect, it may not be feasible to provide a unique treatment effect estimate (such as absolute risk difference) as would be the case with a conventional approach. Furthermore, the existence of a non-linear interaction effects means that the incremental value of the treatment effect is dependent on the value of the baseline incidence. The baseline period was short compared to the implementation and study periods. This may have influenced our findings. However, we found a strong association between the baseline incidence and its change. The strength of this association provides evidence that the baseline incidence had sufficient predictive validity.Our analysis was performed post hoc. Furthermore, the number of hospitals included in the study was relatively small. However, sensitivity analysis showed that even after removing the two hospitals with the highest baseline incidence for the primary outcome, the findings were not qualitatively different. Nonetheless, our results should be considered preliminary and they should be confirmed in future studies with similar design.In summary, we used regression modelling to test the hypothesis that the baseline incidence of cardiac arrests, unplanned ICU admissions, and unexpected deaths has a significant influence on the change in their incidence that could be achieved through the introduction of a MET. Our findings support this hypothesis. We also found that the magnitude of this reduction was proportional to the baseline incidence of adverse events such that the higher the baseline incidence, the greater the absolute reduction. These differences were consistent with the initial findings of the significant interaction effects. We found that these observations were in contrast to those obtained using conventional sub-group analysis. They suggest that the choice of statistical analysis can significantly affect the interpretation of the findings of the MERIT study. They also raise concerns about the robustness of conventional sub-group analysis in similar settings.MET: Medical Emergency Team. ICU: Intensive Care Unit;MERIT: Medical Early Response Intervention & Therapy; ANCOVA: Analysis of Covariance.The authors declare that they have no competing interests.JC contributed to the conceptualisation, all data analyses and prepared the first draft of the paper. AF, RB, KH and SF contributed to the conceptualisation and critical writing of the paper. All authors approved the final draft of the paper and have the access to the data used in generating the paper. The draft was approved by the ANZICS Clinical Trials Group Executive Committee.


The usual surgical treatment of refractory sciatica caused by lumbar disc herniation, is open discectomy. Minimally invasive procedures, including percutaneous therapies under local anesthesia, are increasingly gaining attention. One of these treatments is Percutaneous Laser Disc Decompression (PLDD). This treatment can be carried out in an outpatient setting and swift recovery and return to daily routine are suggested. Thus far, no randomized trial into cost-effectiveness of PLDD versus standard surgical procedure has been performed. We present the design of a randomized controlled trial, studying the cost-effectiveness of PLDD versus conventional open discectomy in patients with sciatica from lumbar disc herniation.The study is a randomized prospective multi-center trial, in which two treatment strategies are compared in a parallel group design. Patients (age 18–70 years) visiting the neurosurgery department of the participating hospitals, are considered for inclusion in the trial when sciatica due to lumbar disc herniation has lasted more than 8 weeks. Patients with disc herniation smaller than 1/3 of the spinal canal diameter, without concomitant lateral recess stenosis or sequestration, are eligible for participation, and are randomized into one of two treatment arms; either Percutaneous Laser Disc Decompression or conventional discectomy. The functional outcome of the patient, as assessed by the Roland Disability Questionnaire for Sciatica at 8 weeks and 1 year after treatment, is the primary outcome measure. The secondary outcome parameters are recovery as perceived by the patient, leg and back pain, incidence of re-intervention, complications, quality of life, medical consumption, absence of work and secondary costs.Open discectomy is still considered to be the golden standard in the surgical treatment of lumbar disc herniation. Whether Percutaneous Laser Disc Decompression has at least as much efficacy as the standard surgical procedure, and is more cost-effective, will be determined by this trial.Current Controlled Trials ISRCTN25884790. In the majority of patients, experiencing their first episode of sciatica due to lumbar disc herniation, the symptoms recede to a non-disabling level within a period of six weeks . The hisPLDD is an attractive treatment because of the minimally invasive nature and therefore the assumed decrease in risk of structural damage to the muscles, bone, ligaments and nerves. Furthermore, the patients are expected to have less back pain, shorter hospitalization and a shorter reconvalescence period than with conventional surgery. The actual recovery of the sciatica however, might take more time than after conventional surgery, although immediate resolution of the symptoms does occur.Although several cohort studies have been published, and FDA approval was given, to date no randomized trial has been performed comparing PLDD with conventional surgical procedures. The cohort studies showed the safety and potential benefits of PLDD. No evidence regarding the efficacy and cost-effectiveness of PLDD, compared to conventional surgery, is available to date .Currently, there is broad consensus that conventional surgery is the gold standard for surgical intervention for sciatica; therefore PLDD has to be compared to conventional surgery in order to assess the cost-effectiveness. The result will be a trade-off between the expected more swift recovery of the patients in the 'gold standard' group versus the minimally invasive nature, lower costs and patient comfort of the PLDD treatment, which might take a slightly longer period to full recovery. Several subgroups will be identified in a post-hoc analysis, using pre-defined potential risk factors, which might especially benefit from the PLDD treatment. It is hypothesized that the potential result, immediately after treatment, is non-inferior at longer follow-up. In this study the patients will therefore be assessed for primary endpoints at 8 weeks and 1-year follow up and additional endpoints up to 2 years.We designed a randomized prospective open trial aimed at showing non-inferiority of PLDD to the surgical treatment of lumbar disc herniation. Both treatments are compared in a parallel group design with at least a 2-year follow-up. The Roland Disability Questionnaire for Sciatica will be used as the primary outcome measure to determine the power of the study. A multi-center approach is used to assure a sufficient recruitment of participating patients. In this case 2 academic hospitals and 6 teaching hospitals will recruit patients. We received full approval of the ethics committees in all participating hospitals.All patients between 18 and 70 years of age with sciatica since 6–8 weeks, presenting at the neurosurgical department, are eligible for inclusion in the trial. A disc herniation at the appropriate level will have to be shown by MRI. The herniated fragment has to be smaller than 1/3 of the spinal canal  or PLDD (B), which will take place within 4 weeks after inclusion.Depending on the surgeon and patient's preference, surgery will be performed under general or spinal anesthesia. The patients will be positioned prone and the affected disc level is verified with fluoroscopy. A small midline incision (2–3 cm) will be made and the paravertebral muscles will be dissected unilaterally. Laminotomy will be performed when deemed necessary. In order to decompress the nerve root, the herniated disc will be removed as much as possible through a unilateral transflaval approach. The wound will be closed in layers with a suction drain when necessary. Patients will be operated with loupe magnification or microscope depending on the surgeon's preference. The participating surgeons have large experience in the technique. A standardized case record form (CRF) will register the surgeon's findings and will be send to the data center. Patients will be admitted to hospital for 2–7 days depending on the usual care.The patient will be instructed to take a prone position on the table of the CT-scan. After placing sterile drapes the level of treatment is identified by a scan. The needle entry point is anesthetized by local lidocaine injection no deeper than the facet joint. Subsequently the 18G needle is placed centrally in the nucleus pulposus, and parallel to the endplates by means of a posterolateral approach. Through the needle, a glass fiber is placed in the disc, enabling the application of laser energy . After a total energy of 1500 J is delivered (2000 J for level L4-5), the procedure is finished. A control CT scan is performed to assess gas formation in the disc space. After the treatment the patient is allowed to drink tea or coffee and is observed for 15 minutes before he/she can return home.Both treatment strategies will be followed by active mobilization in the post-operative period. Patients in the surgery group will be discharged as soon as possible. Early resumption of daily activities and work will be stimulated in both study groups.At baseline we will register demographics, hobbies, sports, work status, smoking status, back pain history, family history of sciatica, co-morbidity, patients satisfaction at work, weight and length. The treatment preference of both patient and the including surgeon will be noted on a 5-point scale ranging from "strong preference for conventional open discectomy" to "strong preference for PLDD". We will also register the expected recovery at 8 weeks after randomization as expected by both surgeon and patient. The baseline questionnaires for the primary endpoints are also collected.The assessment of outcome will use several validated outcome parameters as described below. Patients will undergo neurological examination by the research nurse at 4, 8, 26, and 52 weeks  is an illness-specific 23-item functional assessment questionnaire that is frequently used for low back pain and sciatica -11. The Other parameters that will be used from primary endpoint analyses are:This is a 7-point Likert scale measuring the perceived recovery, varying from 'complete recovery' to 'worse than ever'. This outcome scale has been used in previous studies and is regarded valid and responsive to change . We willThe experienced pain intensity in the leg will be assessed at fixed intervals on a visual analog scale. The scale will measure 100 mm varying from 0 mm 'no pain' to 100 mm 'the worst pain imaginable'. The VAS has shown reliability, validity and responsiveness .Many of the patients have, in addition to their radicular pain, back pain as well. The back pain will be assessed by means of a VASAdditional (secondary) outcome measuresPerceived pain will be measured by using the Dutch version of the validated McGill Questionnaire .The RAND-SF 36 is a generic health status questionnaire that consists of 36 items on physical and social status of the patient subdivided in 8 domains. These are physical functioning, physical restrictions, emotional restrictions, social functioning, somatic pain, general mental health, and vitality and general health perception. The questions add up to a score of 0  to 100 . The SF 36 has been validated for surgical studies on low back pain pathology -18.This scale, ranging from 0 to 6, can asses frequency  and bothersome  of back and leg pain. The sum of the questions ranges from 0 to 24 ,19.The Prolo scale measures the evaluation of the surgeon and research nurse of the patient's functional and economic status. It is a current scale in outcome studies of lumbar spinal surgery ,21.Direct medical costs of hospital admission and surgery as well as PLDD related costs will be based on an integral cost-analysis in the participating hospitals. From this analysis, the constant costs per treatment and the variable costs will be estimated. Other medical costs  will be registered in a diary. Non-medical costs  will also be included in the diary. The research nurse will go through the diary with the patient on every follow-up moment throughout the first 2 years. To estimate the indirect costs the patient will register absenteeism. The research nurse will register the work situation, work efficiency and gross income on the follow-up moments. Absenteeism will be valued to the friction-cost method.The incidence of re-operation will be used as an outcome measure.Complications will be registered systematically. This includes wound infection, deep venous thrombosis, urine tract infection, hematoma and progressive neurological deficit. The surgeon and research nurse will register the complications at the follow-up moments.The study is designed as an equivalence study with asymmetric boundaries, leading to a non-inferiority design. A difference of 4 on the Roland Disability Questionnaire has been recognized as the minimum clinically important difference. The power calculation is performed under the alternative hypothesis of a difference of 1 point on the Roland scale. By using this value 4 as the upper limit of the equivalence interval, with α-level of 0.05, a power of 0.90 and a SD of 5, the required sample size was 98 (49 per treatment arm). To adjust for an 8% loss to follow up, we plan to include 110 patients. These calculations are based on a comparison of average at single points in time. By using a repeated measurements analysis of variance, the power of the study over the entire follow-up period will be substantially higher .Baseline comparability between the two treatment groups will be evaluated with respect to disease characteristics, demographics and baseline values of self-report measures. Chi-square or Student t-test, as appropriate, will be used for these comparisons, although decisions to adjust for one or more of the base line characteristics in the actual analyses will not be based on statistical significance but on the relevance of the confounding effects in case of imbalance occurring in spite of the balanced randomization procedure. The analysis will be performed in accordance with the "intent-to-treat principle", analyzing all patients within their randomization groups, regardless of whether they completed the allocated treatment. Intent-to-treat analysis often leads to smaller observed treatment differences, thereby increasing the risk of type I error . Therefore, a per-protocol analysis is also carried out and differences in results will be described.The analysis will consist of assessment of differences in outcome measurements between both groups and differences in time to recovery. It will take into account the stratification factors  as covariates in accordance with ICH E9. Other base line covariates, measured before randomization, may be incorporated into the analysis models to increase power and to remove any residual confounding. An assessment of interaction between the treatment on the one hand and the stratification variables, or. main baseline covariates, will be considered as a mandatory part of the primary analysis and a proper interpretation of efficacy. The "difference" between the two treatment arms, the treatment effect  will be reported both as an overall (adjusted) value and – in the case of significant effect modification – in terms of its dependence on the categories or values of the effect modifiers. A significant interaction between the stratifying factors or main baseline covariates will be considered as part of a proper modeling strategy and will not imply correction for repeated significance testing. After fitting the proper model, inference on the treatment effect will be based on the smallest fitting model that allows estimation of the treatment effect.When analyzing the primary outcome measure, Roland, the primary analysis will be a repeated measurement analysis of variance, hence estimating the original measurements. As a secondary analysis of this primary outcome variable, we will define "Roland-recovery" as a change of at least 11 points between baseline and any point in time post randomization. This dichotomization of the Roland score will be used as the outcome variable in a Cox regression analysis modeling time till recovery. The multivariate analysis will be performed along the same principles for variable selection and model building as outlined above.A subgroup analysis is preplanned and will be performed to assess differences in treatment effect in subgroups of patients. Table Data will be stored via the internet-based secure data management system (ProMISe) of the department of Medical Statistics and Bioinformatics. The analysis will be carried out using SPSS.With this publication we introduce the design of the first randomized controlled trial on the cost-effectiveness of PLDD versus conventional surgical discotomy for sciatica in lumbar disc herniation. This randomized controlled trial is a prospective trial comparing the effects of the techniques as well as the economical costs. The aim is to investigate whether both treatment modalities are equivalent in their clinical effect and to study their cost-effectiveness.The inclusion is complete as off the end of 2007 with follow-up measurements going on until at least the end of 2009.The authors declare that they have no competing interests.PAB is the coordinator and principal investigator of the trial and is responsible for data management of the trial. He designed the study protocol together with WP, BK and AvdB. MA is co-principal investigator of the parallel MED-trial , and co-The pre-publication history for this paper can be accessed here: