A method to address between-subject heterogeneity for identification of principal surrogate markers in repeated low-dose challenge HIV vaccine studies

作者：Spieker Andrew J^*; Huang Ying

来源：Statistics in Medicine, 2017, 36(25): 4071-4080.

DOI：10.1002/sim.7419

• discrete-time survival
• heterogeneous infection risk
• random effects
• repeated low-dose challenge design

Repeated low-dose challenge designs in nonhuman primate studies have recently received attention in the literature as a means of evaluating vaccines for HIV prevention and identifying immune surrogates for their protective effects. Existing methods for surrogate identification in this type of study design rely on the assumption of homogeneity across subjects (namely, independent infection risks after each challenge within each subject and conditional on covariates). In practice, random variation across subjects is likely to occur because of unmeasured biologic factors. Failure to account for this heterogeneity or within-subject correlation can result in biased inference regarding the surrogate value of immune biomarkers and underpowered study designs for detecting surrogate endpoints. In this paper, we adopt a discrete-time survival model with random effects to account for between-subject heterogeneity, and we develop estimators and testing procedures for evaluating principal surrogacy of immune biomarkers. Simulation studies reveal that the heterogeneous model achieves substantial bias reduction compared to the homogeneous model, with little cost of efficiency. We recommend the use of this heterogeneous model as a complementary tool to existing methods when designing and analyzing repeated low-dose challenge studies for evaluating surrogate endpoints.

• 出版日期2017-11-10