Cox regression is robust to inaccurate EHR-extracted event time: an application to EHR-based GWAS.

ABSTRACT:

Motivation

Logistic regression models are used in genomic studies to analyze the genetic data linked to electronic health records (EHRs), and do not take full usage of the time-to-event information available in EHRs. Previous work has shown that Cox regression, which can account for left truncation and right censoring in EHRs, increased the power to detect genotype-phenotype associations compared to logistic regression. We extend this to evaluate the relative performance of Cox regression and various logistic regression models in the presence of positive errors in event time (delayed event time), relating to recorded event time accuracy.

Results

One Cox model and three logistic regression models were considered under different scenarios of delayed event time. Extensive simul

SUBMITTER: Irlmeier R 

PROVIDER: S-EPMC10060718 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature