Medicine and Health Sciences Commons^™

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. …

Adaptive Pair-Matching In The Search Trial & Estimation Of The Intervention Effect, Laura Balzer, Maya Petersen, Mark Van Der Laan

Laura B. Balzer

R Lab 5 - Tmle, Laura Balzer, Maya Petersen, Alexander Luedtke

Laura B. Balzer

No abstract provided.

R Lab 1 - Causal Parameters & Intro R, Laura Balzer, Maya Petersen, Alex Luedtke

Laura B. Balzer

No abstract provided.

R Lab 3 - Superlearner (Data), Laura Balzer

Laura B. Balzer

No abstract provided.

R Lab 4 - Iptw (Data), Laura Balzer

Laura B. Balzer

No abstract provided.

R Lab 5 - Tmle (Data), Laura Balzer

Laura B. Balzer

No abstract provided.

R Lab 1 - Intro R & Causal Parameters, Laura Balzer, Maya Petersen, Alex Luedtke

Laura B. Balzer

No abstract provided.

R Lab 6 - Inference (Data), Laura Balzer

Laura B. Balzer

No abstract provided.

Estimating The Impact Of Community-Level Interventions: The Search Trial And Hiv Prevention In Sub-Saharan Africa, Laura Balzer, Maya Petersen, Joshua Schwab, Mark Van Der Laan

Laura B. Balzer

Evaluation of community level interventions to prevent HIV infection presents significant methodological challenges. Even when it is feasible to randomly assign a treatment versus control level of the intervention to each community in a sample, measurement of incident HIV infection remains difficult. In this talk we describe an experimental design developed for the SEARCH Trial, a large community randomized trial that will evaluate the impact of expanded treatment on incident HIV and other outcomes. Regular community-wide testing campaigns are conducted and a random sample of community members who fail to attend a campaign are tracked. The data generated by this …

Why Match In Individually And Cluster Randomized Trials?, Laura B. Balzer, Maya L. Petersen, Mark J. Van Der Laan

Laura B. Balzer

The decision to match individuals or clusters in randomized trials is motivated by both practical and statistical concerns. Matching protects against chance imbalances in baseline covariate distributions and is thought to improve study credibility. Matching is also implemented to increase study power. This article compares the asymptotic efficiency of the pair-matched design, where units are matched on baseline covariates and the treatment randomized within pairs, to the independent design, where units are randomly paired and the treatment randomized within pairs. We focus on estimating the average treatment effect and use the efficient influence curve to understand the information provided by …

Why Match In Individually And Cluster Randomized Trials?, Laura Balzer, Maya Petersen, Mark Van Der Laan

Laura B. Balzer

The decision to match individuals or clusters in randomized trials is motivated by both practical and statistical concerns. Matching protects against chance imbalances in baseline covariate distributions and is thereby thought to improve study credibility. Matching is also implemented to increase study power. Analogue to Rose and van der Laan (2009), this article investigates the asymptotic efficiency of pair-matching individuals or clusters relative to not matching in randomized trials. We focus on estimating the average treatment effect. We use the efficient influence curve to understand the information provided by each design for estimation of the target causal parameter. Our approach …

Statistical Methods For Analyzing Sequentially Randomized Trials, Oliver Bembom, Mark J. Van Der Laan

Oliver Bembom

In this issue of JNCI, Thall et al. present the results of a clinical trial that makes use of sequential randomization, a novel trial design that allows the investigator to study adaptive treatment strategies. Our aim is to complement this groundbreaking work by reviewing the current state of the art of statistical methods available for such analyses. Using the data collected by Thall et al. as an example, we focus on two different approaches for estimating the success rates of different adaptive treatment strategies of interest. By emphasizing the intuitive appeal and straightforward implementation of these methods and illustrating the …

Biomarker Discovery Using Targeted Maximum Likelihood Estimation: Application To The Treatment Of Antiretroviral Resistant Hiv Infection, Oliver Bembom, Maya L. Petersen, Soo-Yon Rhee, W. Jeffrey Fessel, Sandra E. Sinisi, Robert W. Shafer, Mark J. Van Der Laan

Oliver Bembom

Researchers in clinical science and bioinformatics frequently aim to learn which of a set of candidate biomarkers is important in determining a given outcome, and to rank the contributions of the candidates accordingly. This article introduces a new approach to research questions of this type, based on targeted maximum likelihood estimation of variable importance measures. The methodology is illustrated using an example drawn from the treatment of HIV infection. Specifically, given a list of candidate mutations in the protease enzyme of HIV, we aim to discover mutations that reduce clinical virologic response to antiretroviral regimens containing the protease inhibitor lopinavir. …

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 …

Estimating The Effect Of Vigorous Physical Activity On Mortality In The Elderly Based On Realistic Individualized Treatment And Intention-To-Treat Rules, Oliver Bembom, Mark J. Van Der Laan

Oliver Bembom

The effect of vigorous physical activity on mortality in the elderly is difficult to estimate using conventional approaches to causal inference that define this effect by comparing the mortality risks corresponding to hypothetical scenarios in which all subjects in the target population engage in a given level of vigorous physical activity. A causal effect defined on the basis of such a static treatment intervention can only be identified from observed data if all subjects in the target population have a positive probability of selecting each of the candidate treatment options, an assumption that is highly unrealistic in this case since …

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

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 …

A Practical Illustration Of The Importance Of Realistic Individualized Treatment Rules In Causal Inference, Oliver Bembom, Mark J. Van Der Laan

Oliver Bembom

The effect of vigorous physical activity on mortality in the elderly is difficult to estimate using conventional approaches to causal inference that define this effect by comparing the mortality risks corresponding to hypothetical scenarios in which all subjects in the target population engage in a given level of vigorous physical activity. A causal effect defined on the basis of such a static treatment intervention can only be identified from observed data if all subjects in the target population have a positive probability of selecting each of the candidate treatment options, an assumption that is highly unrealistic in this case since …