The application of target trials with longitudinal targeted maximum likelihood estimation to assess the effect of alcohol consumption in adolescence on depressive symptoms in adulthood

Abstract

Time-varying confounding is a common challenge for causal inference in observational studies with time-varying treatments, long follow-up periods, and participant dropout. Confounder adjustment using traditional approaches can also be limited by data sparsity, weight instability and computational issues. The Nicotine Dependence in Teens (NDIT) study is a prospective cohort study involving 24 data collection cycles to date, among 1,294 students recruited from 10 high schools in Montreal, Canada, including follow-up into adulthood. Our aim is to estimate associations between the timing of alcohol initiation and the cumulative duration of alcohol use on depression symptoms in adulthood. Based on the target trials framework, we define intention-to-treat and as-treated parameters in a marginal structural model with sex as a potential effect-modifier. We then use the observational data to emulate the trials. For estimation, we use pooled longitudinal target maximum likelihood estimation (LTMLE), a plug-in estimator with double robust and local efficiency properties. We describe strategies for dealing with high-dimensional potential drinking patterns and practical positivity violations due to a long follow-up time, including modifying the effect of interest by removing sparsely observed drinking patterns from the loss function and applying longitudinal modified treatment policies to represent the effect of discouraging drinking.