DATA-ADAPTIVE EFFICIENT ESTIMATION STRATEGIES FOR BIOMARKER STUDIES EMBEDDED IN RANDOMIZED TRIALS

• 成果类型: Article
• 署名作者: Zhang, Weiz; Zhang, Zhi wei; Troendle, James F.; Liu, Aiyi
• 署名单位: Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI); National Institutes of Health (NIH) - USA; NIH Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1588
• 发表日期: 2022
• 页码: 2250-2265
• 关键词: clinical-trials

摘要

Predictive and prognostic biomarkers are increasingly important in clini-cal research and practice. Biomarker studies are frequently embedded in ran-domized clinical trials with biospecimens collected at baseline and assayed for biomarkers, either in real time or retrospectively. This article proposes efficient estimation strategies for two study settings in terms of biomarker ascertainment: a complete-data setting in which the biomarker is measured for all subjects in the trial, and a two-phase sampling design in which the biomarker is measured retrospectively for a random subsample of subjects se-lected in an outcome-dependent fashion. In both settings, efficient estimating functions are characterized using semiparametric theory and approximated using data-adaptive machine learning methods, leading to estimators that are consistent, asymptotically normal and (approximately) efficient under gen-eral conditions. The proposed methods are evaluated in simulation studies and applied to real data from two biomarker studies, one in each setting.