Confidence regions for treatment effects in subgroups in biomarker stratified designs.
Subgroup analysis has important applications in the analysis of controlled clinical trials. Sometimes the result of the overall group fails to demonstrate that the new treatment is better than the control therapy, but for a subgroup of patients, the treatment benefit may exist; or sometimes, the new treatment is better for the overall group but not for a subgroup. Hence we are interested in constructing a simultaneous confidence interval for the difference of the treatment effects in a subgroup and the overall group. Subgroups are usually formed on the basis of a predictive biomarker such as age, sex, or some genetic marker. While, for example, age can be detected precisely, it is often only possible to detect the biomarker status with a certain probability. Because patients detected with a positive or negative biomarker may not be truly biomarker positive or negative, responses in the subgroups depend on the treatment therapy as well as on the sensitivity and specificity of the assay used in detecting the biomarkers. In this work, we show how (approximate) simultaneous confidence intervals and confidence ellipsoid for the treatment effects in subgroups can be found for biomarker stratified clinical trials using a normal framework with normally distributed or binary data. We show that these intervals maintain the nominal confidence level via simulations.
['Wan|Fang|F|0000-0001-5107-9757', 'Kunz|Cornelia U|CU|', 'Jaki|Thomas F|TF|']
D000328:Adult D001249:Asthma D015415:Biomarkers D001699:Biometry D002986:Clinical Trials as Topic D016001:Confidence Intervals D005260:Female D006801:Humans D008297:Male D018418:Th2 Cells D016896:Treatment Outcome
confidence region treatment effects subgroup biomarker stratified design