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- Causal Inference (3)
- Causal inference (2)
- Efficient influence curve (2)
- Matching (2)
- Adaptive designs; Average treatment effect; Cluster randomized trials; Pair-matching; Randomized trials; Targeted minimum loss-based estimation (TMLE) (1)
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- Adaptive designs; causal inference; efficiency (1)
- Adaptive randomization (1)
- Asymptotic linearity of an estimator (1)
- Average treatment effect (1)
- Causal effect (1)
- Causal inference; efficiency; population average treatment effect (PATE); power; sample average treatment effect (SATE); targeted maximum likelihood estimation (TMLE) (1)
- Cluster randomized trials (1)
- Confounding (1)
- Constrained binary classification (1)
- Covariate selection (1)
- Cross-validation (1)
- Data-adaptive (1)
- Dependent treatment allocation (1)
- Empirical process (1)
- Ensemble classification (1)
- G-computation formula (1)
- HIV care cascade; population-level; informative measurement; SEARCH; Super Learner; targeted maximum likelihood estimation; UNAIDS 90-90-90; viral suppression (1)
- Influence curve (1)
- Loss function (1)
- Luster randomized trials (1)
- Neyman-Pearson (1)
- Pair-matched (1)
- Pair-matching; (1)
- PrEP (1)
- Pre-specified (1)
Articles 1 - 20 of 20
Full-Text Articles in Physical Sciences and Mathematics
Stacked Generalization: An Introduction To Super Learning, Ashley Naimi, Laura Balzer
Stacked Generalization: An Introduction To Super Learning, Ashley Naimi, Laura Balzer
Laura B. Balzer
Evaluation Of Progress Towards The Unaids 90-90-90 Hiv Care Cascade: A Description Of Statistical Methods Used In An Interim Analysis Of The Intervention Communities In The Search Study, Laura Balzer, Joshua Schwab, Mark J. Van Der Laan, Maya L. Petersen
Evaluation Of Progress Towards The Unaids 90-90-90 Hiv Care Cascade: A Description Of Statistical Methods Used In An Interim Analysis Of The Intervention Communities In The Search Study, Laura Balzer, Joshua Schwab, Mark J. Van Der Laan, Maya L. Petersen
Laura B. Balzer
WHO guidelines call for universal antiretroviral treatment, and UNAIDS has set a global target to virally suppress most HIV-positive individuals. Accurate estimates of population-level coverage at each step of the HIV care cascade (testing, treatment, and viral suppression) are needed to assess the effectiveness of "test and treat" strategies implemented to achieve this goal. The data available to inform such estimates, however, are susceptible to informative missingness: the number of HIV-positive individuals in a population is unknown; individuals tested for HIV may not be representative of those whom a testing intervention fails to reach, and HIV-positive individuals with a viral …
Adaptive Pre-Specification In Randomized Trials With And Without Pair-Matching, Laura Balzer, M. Van Der Laan, M. Petersen, The Search Collaboration
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
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
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
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
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 …
Estimating Effects With Rare Outcomes And High Dimensional Covariates: Knowledge Is Power, Laura Balzer, J. Ahern, S. Galea, M. Van Der Laan
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
Targeted Estimation Of Marginal Absolute And Relative Associations In Case-Control Data: An Application In Social Epidemiology, M. Pearl, Laura Balzer, J. Ahern
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
Introduction To Targeted Learning, Laura Balzer
Adaptive Pre-Specification In Randomized Trials With And Without Pair-Matching, Laura B. Balzer, Mark J. Van Der Laan, Maya L. Petersen
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 …
Targeted Estimation And Inference For The Sample Average Treatment Effect, Laura B. Balzer, Maya L. Petersen, Mark J. Van Der Laan
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 …
2015_Balzer_Adaptive.Pdf, Laura Balzer
2015_Balzer_Adaptive.Pdf, Laura Balzer
Laura B. Balzer
Adaptive Pair-Matching In Randomized Trials With Unbiased And Efficient Effect Estimation, Laura Balzer, M Petersen, M Van Der Laan, The Search Consortium
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
Adaptive Pair-Matching In The Search Trial And Estimation Of The Intervention Effect, Laura Balzer, Maya L. Petersen, Mark J. Van Der Laan
Adaptive Pair-Matching In The Search Trial And Estimation Of The Intervention Effect, Laura Balzer, Maya L. Petersen, Mark J. Van Der Laan
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 the matched pairs depends on the baseline covariates of all candidate units. As consequence, the observed data cannot be considered as n/2 independent, identically distributed (i.i.d.) pairs of units, as current practice assumes. …
Designing The Search Trial: Ph250b In Practice, Laura Balzer
Designing The Search Trial: Ph250b In Practice, Laura Balzer
Laura B. Balzer
No abstract provided.
Estimating Effects On Rare Outcomes: Knowledge Is Power, Laura B. Balzer, Mark J. Van Der Laan
Estimating Effects On Rare Outcomes: Knowledge Is Power, Laura B. Balzer, Mark J. 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 of an exposure or treatment on a rare outcome. We focus on the causal risk difference and statistical models incorporating bounds on the conditional risk of the outcome, given the exposure and covariates. By construction, the proposed estimator constrains the predicted outcomes to respect this model knowledge. Theoretically, this bounding provides …
Adaptive Matching In Randomized Trials And Observational Studies, Mark J. Van Der Laan, Laura Balzer, Maya L. Petersen
Adaptive Matching In Randomized Trials And Observational Studies, Mark J. Van Der Laan, Laura Balzer, Maya L. Petersen
Laura B. Balzer
In many randomized and observational studies the allocation of treatment among a sample of n independent and identically distributed units is a function of the covariates of all sampled units. As a result, the treatment labels among the units are possibly dependent, complicating estimation and posing challenges for statistical inference. For example, cluster randomized trials frequently sample communities from some target population, construct matched pairs of communities from those included in the sample based on some metric of similarity in baseline community characteristics, and then randomly allocate a treatment and a control intervention within each matched pair. In this case, …
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 …
Adaptive Matching In Randomized Trials And Observational Studies, M. Van Der Laan, Laura Balzer, M. Petersen
Adaptive Matching In Randomized Trials And Observational Studies, M. Van Der Laan, Laura Balzer, M. Petersen
Laura B. Balzer