Novel Statistical Methods for Complex Time-to-Event Data in Cardiovascular Clinical Trials

项目总结:

Project Summary: Modern cardiovascular (CV) trials often collect data on a wide array of fatal and nonfatal events (e.g., heart failure, heart attack, stroke, chest pain, and etc.) with different implications for patient health. In recent years, new methods have started to emerge which seek to capture more events than the traditional endpoint of each patient’s first event. However, to account for the totality of a composite endpoint while differentiating the importance of its components (e.g., death vs CV hospitalization) is not easy. As it stands, investigators still lack adequate tools to measure treatment effects, design future trials, assess risk factors, and build prediction models. In this project, we address these gaps via four specific aims. In Aim 1, we consider a general class of nonparametric effect-size estimands defined though pairwise comparison (both overall and subgroup-wise), in which one component can be readily prioritized over another using a hierarchical rule of comparison. The inverse probability censoring weighting (IPCW) and augmented inverse probability weighting (AIPW) techniques are adapted to U-statistic estimators to correct for censoring bias and to improve efficiency (and thus reduce trial cost) using patient data both pre- and post-randomization. In Aim 2, we develop routines to calculate power and sample size for newly proposed methods for composite endpoints, such as the restricted mean time in favor of treatment and while-alive loss rate, under both fixed and group sequential designs. In Aim 3, we propose novel semiparametric regression models for composite endpoints following earlier work on the proportional win- fractions (PW) model. In particular, the generalized semiparametric proportional odds (GSPO) model accommodates nonproportional win fractions by extending traditional PO models to multiple events with ordered severities. In Aim 4, we extend survival trees as a predictive tool from univariate to composite endpoints. Drawing on classification trees for ordinal response, we develop time-integrated versions of the weighted Gini index and twoing approach for node-splitting, and of a generalized concordance index for cross-validative pruning, thereby accounting for both the timing and severity of the outcome events. The methods developed will be used for secondary analyses of the recently concluded INfluenza Vaccine to Effectively Stop cardio-Thoracic Events and Decompensated heart failure (INVESTED) trial (ClinicalTrials.gov: NCT02787044). Meanwhile, they will be incorporated into new and existing R-packages on the Comprehensive R Archive Network (CRAN.R-project.org) for public use by practitioners.