Comparing statistical methods in assessing the prognostic effect of biomarker variability on time-to-event clinical outcomes.

ABSTRACT:

Background

In recent years there is increasing interest in modeling the effect of early longitudinal biomarker data on future time-to-event or other outcomes. Sometimes investigators are also interested in knowing whether the variability of biomarkers is independently predictive of clinical outcomes. This question in most applications is addressed via a two-stage approach where summary statistics such as variance are calculated in the first stage and then used in models as covariates to predict clinical outcome in the second stage. The objective of this study is to compare the relative performance of various methods in estimating the effect of biomarker variability.

Methods

A joint model and 4 different two-stage approaches (naïve, landmark analysis, time-dependent Cox model

SUBMITTER: Gao F 

PROVIDER: S-EPMC9308219 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature