Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 4 of 4
Full-Text Articles in Medicine and Health Sciences
History-Adjusted Marginal Structural Models To Estimate Time-Varying Effect Modification, Maya Petersen, Steven Deeks, Jefferey Martin, Mark Van Der Laan
History-Adjusted Marginal Structural Models To Estimate Time-Varying Effect Modification, Maya Petersen, Steven Deeks, Jefferey Martin, Mark Van Der Laan
Maya Petersen
Much of epidemiology and clinical medicine is focused on the estimation of treatments or interventions administered over time. In such settings of longitudinal treatment, time-dependent confounding is often an important source of bias. Marginal structural models are a powerful tool for estimating the causal effect of a treatment using observational data, particularly when time-dependent confounding is present. Recent statistical work presented a generalization of marginal structural models, called history-adjusted marginal structural models. Unlike standard marginal structural models, history-adjusted marginal structural models can be used to estimate modification of treatment effects by time-varying covariates. Estimation of time-dependent causal effect modification is …
Data-Adaptive Estimation Of The Treatment-Specific Mean, Yue Wang, Oliver Bembom, Mark Van Der Laan
Data-Adaptive Estimation Of The Treatment-Specific Mean, Yue Wang, Oliver Bembom, Mark Van Der Laan
Oliver Bembom
An important problem in epidemiology and medical research is the estimation of the causal effect of a treatment action at a single point in time on the mean of an outcome, possibly within strata of the target population defined by a subset of the baseline covariates. Current approaches to this problem are based on marginal structural models, i.e. parametric models for the marginal distribution of counterfactual outcomes as a function of treatment and effect modifiers. The various estimators developed in this context furthermore each depend on a high-dimensional nuisance parameter whose estimation currently also relies on parametric models. Since misspecification …
Analyzing Sequentially Randomized Trials Based On Causal Effect Models For Realistic Individualized Treatment Rules, Oliver Bembom, Mark J. Van Der Laan
Analyzing Sequentially Randomized Trials Based On Causal Effect Models For Realistic Individualized Treatment Rules, Oliver Bembom, Mark J. Van Der Laan
Oliver Bembom
In this paper, we argue that causal effect models for realistic individualized treatment rules represent an attractive tool for analyzing sequentially randomized trials. Unlike a number of methods proposed previously, this approach does not rely on the assumption that intermediate outcomes are discrete or that models for the distributions of these intermediate outcomes given the observed past are correctly specified. In addition, it generalizes the methodology for performing pairwise comparisons between individualized treatment rules by allowing the user to posit a marginal structural model for all candidate treatment rules simultaneously. If only a small number of candidate treatment rules are …
The Causal Effect Of Recent Leisure-Time Physical Activity On All-Cause Mortality Among The Elderly, Oliver Bembom, Mark J. Van Der Laan, Ira B. Tager
The Causal Effect Of Recent Leisure-Time Physical Activity On All-Cause Mortality Among The Elderly, Oliver Bembom, Mark J. Van Der Laan, Ira B. Tager
Oliver Bembom
We analyze data collected as part of a prospective cohort study of elderly people living in and around Sonoma, CA, in order to estimate, for each round of interviews, the causal effect of leisure-time physical activity (LTPA) over the past year on the risk of mortality in the following two years. For each round of interviews, this effect is estimated separately for subpopulations defined based on past exercise habits, age, and whether subjects have had cardiac events in the past. This decomposition of the original longitudinal data structure into a series of point-treatment data structures corresponds to an application of …