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Full-Text Articles in Epidemiology
Depicting Estimates Using The Intercept In Meta-Regression Models: The Moving Constant Technique, Blair T. Johnson Dr., Tania B. Huedo-Medina Dr.
Depicting Estimates Using The Intercept In Meta-Regression Models: The Moving Constant Technique, Blair T. Johnson Dr., Tania B. Huedo-Medina Dr.
Blair T. Johnson
In any scientific discipline, the ability to portray research patterns graphically often aids greatly in interpreting a phenomenon. In part to depict phenomena, the statistics and capabilities of meta-analytic models have grown increasingly sophisticated. Accordingly, this article details how to move the constant in weighted meta-analysis regression models (viz. “meta-regression”) to illuminate the patterns in such models across a range of complexities. Although it is commonly ignored in practice, the constant (or intercept) in such models can be indispensible when it is not relegated to its usual static role. The moving constant technique makes possible estimates and confidence intervals at …
Big Data And The Future, Sherri Rose
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
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
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
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 …
Ensuring The Comparability Of Comparison Groups: Is Randomization Enough?, Vance Berger, Sherri Rose
Ensuring The Comparability Of Comparison Groups: Is Randomization Enough?, Vance Berger, Sherri Rose
Sherri Rose
It is widely believed that baseline imbalances in randomized trials must necessarily be random. In fact, there is a type of selection bias that can cause substantial, systematic and reproducible baseline imbalances of prognostic covariates even in properly randomized trials. It is possible, given complete data, to quantify both the susceptibility of a given trial to this type of selection bias and the extent to which selection bias appears to have caused either observable or unobservable baseline imbalances. Yet, in articles reporting on randomized trials, it is uncommon to find either these assessments or the information that would enable a …