Elastic Integrative Analysis of Randomized Trial and Real-World Data for Treatment Heterogeneity Estimation

Parallel randomized trial (RT) and real-world (RW) data are becoming increasingly available for treatment evaluation. Given the complementary features of the RT and RW data, we propose a test-based elastic integrative analysis of the RT and RW data for accurate and robust estimation of the heterogeneity of treatment effect (HTE), which lies at the heart of precision medicine. When the RW data are not subject to bias, e.g., due to unmeasured confounding, our approach combines the RT and RW data for optimal estimation by exploiting semiparametric efficiency theory. Utilizing the design advantage of RTs, we construct a built-in test procedure to gauge the reliability of the RW data and decide whether or not to use RW data in an integrative analysis. We characterize the asymptotic distribution of the test-based elastic integrative estimator under local alternatives, which provides a better approximation of the finite-sample behaviors of the test and estimator when the idealistic assumption required for the RW data is weakly violated. We provide a data-adaptive procedure to select the threshold of the test statistic that promises the smallest mean square error of the proposed estimator of the HTE. Lastly, we construct an elastic confidence interval that has a good finite-sample coverage property. We apply the proposed method to characterize who can benefit from adjuvant chemotherapy in patients with stage IB non-small cell lung cancer

Evaluation of Ecosystem Services in Strategic Environmental Assessment for River Basin Planning

Water Resources Planning and Managemen

Efficient Semiparametric Estimation of Short-Term and Long-Term Hazard Ratios with Right-Censored Data: Semiparametric Estimation of Short-Term and Long-Term Hazard Ratios

The proportional hazards assumption in the commonly used Cox model for censored failure time data is often violated in scientific studies. Yang and Prentice (2005) proposed a novel semiparametric two-sample model that includes the proportional hazards model and the proportional odds model as sub-models, and accommodates crossing survival curves. The model leaves the baseline hazard unspecified and the two model parameters can be interpreted as the short-term and long-term hazard ratios. Inference procedures were developed based on a pseudo score approach. Although extension to accommodate covariates was mentioned, no formal procedures have been provided or proved. Furthermore, the pseudo score approach may not be asymptotically efficient. We study the extension of the short-term and long-term hazard ratio model of Yang and Prentice (2005) to accommodate potentially time-dependent covariates. We develop efficient likelihood-based estimation and inference procedures. The nonparametric maximum likelihood estimators are shown to be consistent, asymptotically normal, and asymptotically efficient. Extensive simulation studies demonstrate that the proposed methods perform well in practical settings. The proposed method successfully captured the phenomenon of crossing hazards in a cancer clinical trial and identified a genetic marker with significant long-term effect missed by using the proportional hazards model on age-at-onset of alcoholism in a genetic study