TY - JOUR

T1 - Confidence regions for treatment effects in subgroups in biomarker stratified designs

AU - Wan, Fang

AU - Kunz, Cornelia Ursula

AU - Jaki, Thomas Friedrich

N1 - This is the peer reviewed version of the following article: Wan F, Kunz CU, Jaki TF. Confidence regions for treatment effects in subgroups in biomarker stratified designs. Biometrical Journal. 2019; 61:27–39. https://doi.org/10.1002/bimj.201700303 which has been published in final form at https://onlinelibrary.wiley.com/doi/full/10.1002/bimj.201700303 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - biomarker stratified design

KW - confidence region

KW - subgroup

KW - treatment effects

U2 - 10.1002/bimj.201700303

DO - 10.1002/bimj.201700303

M3 - Journal article

VL - 61

SP - 27

EP - 39

JO - Biometrical Journal

JF - Biometrical Journal

SN - 0323-3847

IS - 1

ER -