Search

Using data from observational studies to estimate the causal effect of a time-varying exposure, repeatedly measured over time, on an outcome of interest requires careful adjustment for confounding. Standard regression adjustment...

Aalen's additive model accelerated failure time model Causal effect marginal structural model structural nested cumulative failure time model time-varying confounding Bias Causality Humans Linear Models Models Statistical Probability

Abstract: Observational longitudinal data on treatments and covariates are increasingly used to investigate treatment effects, but are often subject to time‐dependent confounding. Marginal structural models (MSMs), estimated...

Research Paper Research Papers additive hazard model causal inference congenial models longitudinal data marginal structural model simulation study Survival Analysis time‐dependent confounding

Accounting for time-varying confounding when assessing the causal effects of time-varying exposures on survival time is challenging. Standard survival methods that incorporate time-varying confounders as covariates generally...

Aalen's additive model G-estimation accelerated failure time model marginal structural model survival data Biometry Computer Simulation Confidence Intervals Confounding Factors Epidemiologic Cystic Fibrosis Deoxyribonucleases Humans Linear Models Models Statistical Proportional Hazards Models Registries Survival Analysis Time Factors United Kingdom

Using data from observational studies to estimate the causal effect of a time‐varying exposure, repeatedly measured over time, on an outcome of interest requires careful adjustment for confounding. Standard regression adjustment...

RESEARCH ARTICLE RESEARCH ARTICLES Aalen's additive model accelerated failure time model Causal effect marginal structural model structural nested cumulative failure time model time‐varying confounding