Comparison of censoring assumptions to reduce bias in tuberculosis treatment cohort analyses.

ABSTRACT:

Objective

Observational tuberculosis cohort studies are often limited by a lack of long-term data characterizing survival beyond the initial treatment outcome. Though Cox proportional hazards models are often applied to these data, differential risk of long-term survival, dependent on the initial treatment outcome, can lead to violations of model assumptions. We evaluate the performance of two alternate censoring approaches on reducing bias in treatment effect estimates.

Design

We simulate a typical multidrug-resistant tuberculosis cohort study and use Cox proportional hazards models to assess the relationship of an aggressive treatment regimen with hazard of death. We compare three assumptions regarding censored observations to determine which produces least biased treatmen

SUBMITTER: Brooks MB 

PROVIDER: S-EPMC7571697 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature