Predictive or treatment selection biomarkers are usually evaluated in a subgroup or regression analysis with focus on the treatment-by-marker interaction. Under a potential outcome framework (Huang, Gilbert and Janes [Biometrics 68 (2012) 687-696]), a predictive biomarker is considered a predictor for a desirable treatment benefit (defined by comparing potential outcomes for different treatments) and evaluated using familiar concepts in prediction and classification. However, the desired treatment benefit is unobservable because each patient can receive only one treatment in a typical study. Huang et al. overcome this problem by assuming monotonicity of potential outcomes, with one treatment dominating the other in all patients. Motivated by an HIV example that appears to violate the monotonicity assumption, we propose a different approach based on covariates and random effects for evaluating predictive biomarkers under the potential outcome framework. Under the proposed approach, the parameters of interest can be identified by assuming conditional independence of potential outcomes given observed covariates, and a sensitivity analysis can be performed by incorporating an unobserved random effect that accounts for any residual dependence. Application of this approach to the motivating example shows that baseline viral load and CD4 cell count are both useful as predictive biomarkers for choosing antiretroviral drugs for treatment-naive patients.
Joint Modeling of Biomarker Cascades Along An Unobserved Disease Progression with Differentiate Covariate Effects: An Application in Alzheimer's Disease
A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology
Generalizing imaging biomarker repeatability studies using Bayesian inference: Applications in detecting heterogeneous treatment response in whole-body diffusion-weighted MRI of metastatic prostate cancer
提出一种基于 Hamiltonian Monte Carlo 的贝叶斯框架，以量化不同分布假设下影像异质性生物标志物的测量不确定性，并在 mCRPC 的 WBDWI 数据中验证其疗效评估价值。
Evaluating the performance of personal, social, health-related, biomarker and genetic data for predicting an individuals future health using machine learning: A longitudinal analysis
A tutorial comparing different covariate balancing methods with an application evaluating the causal effects of substance use treatment programs for adolescents
Clustering-Based Outcome Models for Clinical Studies: A Scoping Review
Medical University of Vienna（维也纳医科大学）；Hoffmann-La Roche AG（罗氏制药）；University of Warwick（华威大学）；Radboud University Medical Center（拉德堡德大学医学中心）；Bayer BCC（拜耳 BCC）；Inserm, Université Paris Cité, Inria, HeKA（法国国家健康与医学研究院、巴黎西岱大学、法国国家数字科学研究所、HeKA）
Assessing lack of common support in causal inference using Bayesian nonparametrics: Implications for evaluating the effect of breastfeeding on children's cognitive outcomes
Inferential Evaluation of Surrogate-Derived Models under Covariate Shift
National University of Singapore（新加坡国立大学）；Peking University Health Science Center, Peking University（北京大学医学部，北京大学）；Beijing International Center for Mathematical Research, Peking University（北京大学北京国际数学研究中心）
