Investigation of one-stage meta-analysis methods for joint longitudinal and time-to-event data through simulation and real data application.

ABSTRACT:

Background

Joint modeling of longitudinal and time-to-event data is often advantageous over separate longitudinal or time-to-event analyses as it can account for study dropout, error in longitudinally measured covariates, and correlation between longitudinal and time-to-event outcomes. The current literature on joint modeling focuses mainly on the analysis of single studies with a lack of methods available for the meta-analysis of joint data from multiple studies.

Methods

We investigate a variety of one-stage methods for the meta-analysis of joint longitudinal and time-to-event outcome data. These methods are applied to the INDANA dataset to investigate longitudinally measured systolic blood pressure, with each of time to death, time to myocardial infarction, and time to str

SUBMITTER: Sudell M 

PROVIDER: S-EPMC6492085 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature