Modeling unmeasured baseline information in observational time-to-event data subject to delayed study entry.

ABSTRACT: Unmeasured baseline information in left-truncated data situations frequently occurs in observational time-to-event analyses. For instance, a typical timescale in trials of antidiabetic treatment is "time since treatment initiation", but individuals may have initiated treatment before the start of longitudinal data collection. When the focus is on baseline effects, one widespread approach is to fit a Cox proportional hazards model incorporating the measurements at delayed study entry. This has been criticized because of the potential time dependency of covariates. We tackle this problem by using a Bayesian joint model that combines a mixed-effects model for the longitudinal trajectory with a proportional hazards model for the event of interest incorporating the baseline covariate, possibly

SUBMITTER: Stegherr R 

PROVIDER: S-EPMC10248294 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature