title: The Impact of Sparse Follow-up on Marginal Structural Models for Time-to-Event Data.
abstract: The impact of risk factors on the amount of time taken to reach an endpoint is a common parameter of interest. Hazard ratios are often estimated using a discrete-time approximation, which works well when the by-interval event rate is low. However, if the intervals are made more frequent than the observation times, missing values will arise. We investigated common analytical approaches, including available-case (AC) analysis, last observation carried forward (LOCF), and multiple imputation (MI), in a setting where time-dependent covariates also act as mediators. We generated complete data to obtain monthly information for all individuals, and from the complete data, we selected "observed" data by assuming that follow-up visits occurred every 6 months. MI proved superior to LOCF and AC analyses when only data on confounding variables were missing; AC analysis also performed well when data for additional variables were missing completely at random. We applied the 3 approaches to data from the Canadian HIV-Hepatitis C Co-infection Cohort Study (2003-2014) to estimate the association of alcohol abuse with liver fibrosis. The AC and LOCF estimates were larger but less precise than those obtained from the analysis that employed MI.
authors.forename: Nassim
authors.initials: N
authors.lastname: Mojaverian
authors.forename: Erica E M
authors.initials: EE
authors.lastname: Moodie
authors.forename: Alex
authors.initials: A
authors.lastname: Bliu
authors.forename: Marina B
authors.initials: MB
authors.lastname: Klein
mesh_terms: D035843:Biomedical Research / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D005500:Follow-Up Studies; D006801:Humans; D015233:Models, Statistical
keywords: available-case analysis; last observation carried forward; marginal structural models; missing data; multiple imputation; survival analysis