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Physical Sciences and Mathematics Commons™

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Statistics and Probability

Selected Works

Laura B. Balzer

Selected Works

2016

Articles 1 - 6 of 6

Full-Text Articles in Physical Sciences and Mathematics

Adaptive Pre-Specification In Randomized Trials With And Without Pair-Matching, Laura Balzer, M. Van Der Laan, M. Petersen, The Search Collaboration Nov 2016

Adaptive Pre-Specification In Randomized Trials With And Without Pair-Matching, Laura Balzer, M. Van Der Laan, M. Petersen, The Search Collaboration

Laura B. Balzer

In randomized trials, adjustment for measured covariates during the analysis can reduce variance and increase power. To avoid misleading inference, the analysis plan must be pre-specified. However, it is often unclear a priori which baseline covariates (if any) should be adjusted for in the analysis.  Consider, for example, the Sustainable East Africa Research in Community Health (SEARCH) trial for HIV prevention and treatment.  There are 16 matched pairs of communities and many potential adjustment variables, including region, HIV prevalence, male circumcision coverage and measures of community-level viral load.  In this paper, we propose a rigorous procedure to data-adaptively select the …

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Targeted Estimation And Inference For The Sample Average Treatment Effect In Trials With And Without Pair-Matching, Laura Balzer, M. Petersen, M. Van Der Laan, The Search Collaboration Oct 2016

Targeted Estimation And Inference For The Sample Average Treatment Effect In Trials With And Without Pair-Matching, Laura Balzer, M. Petersen, M. Van Der Laan, The Search Collaboration

Laura B. Balzer

In cluster randomized trials, the study units usually are not a simple random sample from some clearly defined
target population. Instead, the target population tends to be hypothetical or ill-defined, and the selection of study
units tends to be systematic, driven by logistical and practical considerations. As a result, the population average
treatment effect (PATE) may be neither well-defined nor easily interpretable. In contrast, the sample average
treatment effect (SATE) is the mean difference in the counterfactual outcomes for the study units. The sample
parameter is easily interpretable and arguably the most relevant when the study units are not sampled …

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Performance-Constrained Binary Classification Using Ensemble Learning: An Application To Cost-Efficient Targeted Prep Strategies, Wenjing Zheng, Laura Balzer, Maya L. Petersen, Mark J. Van Der Laan Oct 2016

Performance-Constrained Binary Classification Using Ensemble Learning: An Application To Cost-Efficient Targeted Prep Strategies, Wenjing Zheng, Laura Balzer, Maya L. Petersen, Mark J. Van Der Laan

Laura B. Balzer

Binary classifications problems are ubiquitous in health and social science applications. In many cases, one wishes to balance two conflicting criteria for an optimal binary classifier. For instance, in resource-limited settings, an HIV prevention program based on offering Pre-Exposure Prophylaxis (PrEP) to select high-risk individuals must balance the sensitivity of the binary classifier in detecting future seroconverters (and hence offering them PrEP regimens) with the total number of PrEP regimens that is financially and logistically feasible for the program to deliver. In this article, we consider a general class of performance-constrained binary classification problems wherein the objective function and the …

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Estimating Effects With Rare Outcomes And High Dimensional Covariates: Knowledge Is Power, Laura Balzer, J. Ahern, S. Galea, M. Van Der Laan Sep 2016

Estimating Effects With Rare Outcomes And High Dimensional Covariates: Knowledge Is Power, Laura Balzer, J. Ahern, S. Galea, M. Van Der Laan

Laura B. Balzer

Many of the secondary outcomes in observational studies and randomized trials are rare. Methods for estimating causal effects and associations with rare outcomes, however, are limited, and this represents a missed opportunity for investigation. In this article, we construct a new targeted minimum loss-based estimator (TMLE) for the effect or association of an exposure on a rare outcome. We focus on the causal risk difference and statistical models incorporating bounds on the conditional mean of the outcome, given the exposure and measured confounders. By construction, the proposed estimator constrains the predicted outcomes to respect this model knowledge. Theoretically, this bounding …

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Targeted Estimation Of Marginal Absolute And Relative Associations In Case-Control Data: An Application In Social Epidemiology, M. Pearl, Laura Balzer, J. Ahern Aug 2016

Targeted Estimation Of Marginal Absolute And Relative Associations In Case-Control Data: An Application In Social Epidemiology, M. Pearl, Laura Balzer, J. Ahern

Laura B. Balzer

Background: Case-control studies are useful for rare outcomes, but typical analyses limit investigators to parametric estimation of conditional odds ratios. Several methods exist for obtaining marginal risk differences and risk ratios in a case-control setting, including a recently described semiparametric targeted approach optimized for rare outcomes.
Methods: Using case-control data from a study of neighborhood poverty and very preterm birth, we demonstrate estimation of marginal risk differences and risk ratios and compare a parametric substitution estimator based on maximum likelihood estimation with targeted maximum likelihood estimation (TMLE), and a refinement of TMLE for rare outcomes that incorporates bounds on the …

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Introduction To Targeted Learning, Laura Balzer Dec 2015

Introduction To Targeted Learning, Laura Balzer

Laura B. Balzer

No abstract provided.

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