Time-varying proportional odds model for mega-analysis of clustered event times.

ABSTRACT: Mega-analysis, or the meta-analysis of individual data, enables pooling and comparing multiple studies to enhance estimation and power. A challenge in mega-analysis is estimating the distribution for clustered, potentially censored event times where the dependency structure can introduce bias if ignored. We propose a new proportional odds model with unknown, time-varying coefficients, and random effects. The model directly captures event dependencies, handles censoring using pseudo-values, and permits a simple estimation by transforming the model into an easily estimable additive logistic mixed effect model. Our method consistently estimates the distribution for clustered event times even under covariate-dependent censoring. Applied to three observational studies of Huntington's disease, o

SUBMITTER: Garcia TP 

PROVIDER: S-EPMC6402758 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature