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Full-Text Articles in Physical Sciences and Mathematics
Estimating Controlled Direct Effects Of Restrictive Feeding Practices In The `Early Dieting In Girls' Study, Yeying Zhu, Debashis Ghosh, Donna L. Coffman, Jennifer S. Williams
Estimating Controlled Direct Effects Of Restrictive Feeding Practices In The `Early Dieting In Girls' Study, Yeying Zhu, Debashis Ghosh, Donna L. Coffman, Jennifer S. Williams
Debashis Ghosh
In this article, we examine the causal effect of parental restrictive feeding practices on children’s weight status. An important mediator we are interested in is children’s self-regulation status. Traditional mediation analysis (Baron and Kenny, 1986) applies a structural equation modelling (SEM) approach and decomposes the intent-to-treat (ITT) effect into direct and indirect effects. More recent approaches interpret the mediation effects based on the potential outcomes framework. In practice, there often exist confounders that jointly influence the mediator and the outcome. Inverse probability weighting based on propensity scores are used to adjust for confounding and reduce the dimensionality of confounders simultaneously. …
Moving Towards Best Practice When Using Inverse Probability Of Treatment Weighting (Iptw) Using The Propensity Score To Estimate Causal Treatment Effects In Observational Studies, Peter Austin, Elizabeth Stuart
Moving Towards Best Practice When Using Inverse Probability Of Treatment Weighting (Iptw) Using The Propensity Score To Estimate Causal Treatment Effects In Observational Studies, Peter Austin, Elizabeth Stuart
Peter Austin
The propensity score is defined as a subject’s probability of treatment selection, conditional on observed baseline covariates.Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in …
Using Ensemble-Based Methods For Directly Estimating Causal Effects: An Investigation Of Tree-Based G-Computation, Peter C. Austin
Using Ensemble-Based Methods For Directly Estimating Causal Effects: An Investigation Of Tree-Based G-Computation, Peter C. Austin
Peter Austin
Researchers are increasingly using observational or nonrandomized data to estimate causal treatment effects. Essential to the production of high-quality evidence is the ability to reduce or minimize the confounding that frequently occurs in observational studies. When using the potential outcome framework to define causal treatment effects, one requires the potential outcome under each possible treatment. However, only the outcome under the actual treatment received is observed, whereas the potential outcomes under the other treatments are considered missing data. Some authors have proposed that parametric regression models be used to estimate potential outcomes. In this study, we examined the use of …
Optimal Caliper Widths For Propensity-Score Matching When Estimating Differences In Means And Differences In Proportions In Observational Studies., Peter C. Austin
Optimal Caliper Widths For Propensity-Score Matching When Estimating Differences In Means And Differences In Proportions In Observational Studies., Peter C. Austin
Peter Austin
In a study comparing the effects of two treatments, the propensity score is the probability of assignment to one treatment conditional on a subject's measured baseline covariates. Propensity-score matching is increasingly being used to estimate the effects of exposures using observational data. In the most common implementation of propensity-score matching, pairs of treated and untreated subjects are formed whose propensity scores differ by at most a pre-specified amount (the caliper width). There has been a little research into the optimal caliper width. We conducted an extensive series of Monte Carlo simulations to determine the optimal caliper width for estimating differences …
Direct Effect Models, Mark J. Van Der Laan, Maya L. Petersen
Direct Effect Models, Mark J. Van Der Laan, Maya L. Petersen
Maya Petersen
The causal effect of a treatment on an outcome is generally mediated by several intermediate variables. Estimation of the component of the causal effect of a treatment that is not mediated by an intermediate variable (the direct effect of the treatment) is often relevant to mechanistic understanding and to the design of clinical and public health interventions. Robins, Greenland and Pearl develop counterfactual definitions for two types of direct effects, natural and controlled, and discuss assumptions, beyond those of sequential randomization, required for the identifiability of natural direct effects. Building on their earlier work and that of others, this article …
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 …