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Statistics and Probability

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Selected Works

Laura B. Balzer

2015

Articles 1 - 4 of 4

Full-Text Articles in Physical Sciences and Mathematics

Adaptive Pre-Specification In Randomized Trials With And Without Pair-Matching, Laura B. Balzer, Mark J. Van Der Laan, Maya L. Petersen May 2015

Adaptive Pre-Specification In Randomized Trials With And Without Pair-Matching, Laura B. Balzer, Mark J. Van Der Laan, Maya L. Petersen

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 unclear a priori which baseline covariates (if any) should be included 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 adjustment set …

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Targeted Estimation And Inference For The Sample Average Treatment Effect, Laura B. Balzer, Maya L. Petersen, Mark J. Van Der Laan Mar 2015

Targeted Estimation And Inference For The Sample Average Treatment Effect, Laura B. Balzer, Maya L. Petersen, Mark J. Van Der Laan

Laura B. Balzer

While the population average treatment effect has been the subject of extensive methods and applied research, less consideration has been given to the sample average treatment effect: the mean difference in the counterfactual outcomes for the study units. The sample parameter is easily interpretable and is arguably the most relevant when the study units are not representative of a greater population or when the exposure's impact is heterogeneous. Formally, the sample effect is not identifiable from the observed data distribution. Nonetheless, targeted maximum likelihood estimation (TMLE) can provide an asymptotically unbiased and efficient estimate of both the population and sample …

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2015_Balzer_Adaptive.Pdf, Laura Balzer Dec 2014

2015_Balzer_Adaptive.Pdf, Laura Balzer

Laura B. Balzer

In randomized trials, pair-matching is an intuitive design strategy to protect study validity and to potentially
increase study power. In a common design, candidate units are identified, and their baseline characteristics used
to create the best n/2 matched pairs.Within the resulting pairs, the intervention is randomized, and the outcomes
measured at the end of follow-up.We consider this design to be adaptive, because the construction of thematched
pairs depends on the baseline covariates of all candidate units. As a consequence, the observed data cannot be
considered as n/2 independent, identically distributed pairs of units, as common practice assumes. Instead, the
observed …

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Adaptive Pair-Matching In Randomized Trials With Unbiased And Efficient Effect Estimation, Laura Balzer, M Petersen, M Van Der Laan, The Search Consortium Dec 2014

Adaptive Pair-Matching In Randomized Trials With Unbiased And Efficient Effect Estimation, Laura Balzer, M Petersen, M Van Der Laan, The Search Consortium

Laura B. Balzer

In randomized trials, pair-matching is an intuitive design strategy to protect study validity and to potentially
increase study power. In a common design, candidate units are identified, and their baseline characteristics used
to create the best n∕2 matched pairs.Within the resulting pairs, the intervention is randomized, and the outcomes
measured at the end of follow-up.We consider this design to be adaptive, because the construction of thematched
pairs depends on the baseline covariates of all candidate units. As a consequence, the observed data cannot be
considered as n∕2 independent, identically distributed pairs of units, as common practice assumes. Instead, the
observed …

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