10: Detection of Subgroup Treatment Effects with Missing Patient-Reported Outcomes Data

Music City Center 

Patient-reported outcomes (PRO) data provide valuable insights into treatment effects from the patient's perspective and are informative for detecting subgroup treatment effects. This study focuses on estimating individualized treatment effects using PRO data with monotone missing responses in longitudinal studies. Given the heterogeneity of treatment effects and the challenges in analyzing PRO data, such as missing data, longitudinal structure, and non-normal distribution, a semiparametric quantile regression model is proposed. The model treats the treatment effect as an unknown functional curve of a weighted linear combination of covariates to explore covariate-specific treatment effects. For data with missing values, an inverse probability weighting (IPW)-based estimator is firstly introduced, and then an improved IPW estimator is developed by incorporating an auxiliary mean model and empirical likelihood to account for within-subject correlations. The effectiveness of our approach is demonstrated through numerical experiments and an application to PRO data from a breast cancer clinical trial, which motivated this study.