44 citations · 146 across the 16 of their papers we have counts for
16 papers
Machine learning to optimize precision in the analysis of randomized trials: A journey in pre-specified, yet data-adaptive learning
Laura B. Balzer, Mark J. van der Laan, Maya L. Petersen
Covariate adjustment is an approach to improve the precision of trial analyses by adjusting for baseline variables that are prognostic of the primary endpoint. Motivated by the SEA…
Causal Inference in Randomized Trials with Partial Clustering
Joshua Nugent, Elijah Kakande, Gabriel Chamie +5
Clustering and dependence are common in trials. For example, in some cluster randomized trials (CRTs), pre-existing clusters are enrolled, randomized, and serve as the basis of int…
When exposure affects subgroup membership: Framing relevant causal questions in perinatal epidemiology and beyond
Shalika Gupta, Laura B. Balzer, Moses R. Kamya +2
Perinatal epidemiology often aims to evaluate exposures on infant outcomes. When the exposure affects the composition of people who give birth to live infants (e.g., by affecting f…
Statistical Analysis Plan for Primary and Selected Secondary Health Endpoints of the SEARCH-Youth Study
Laura B. Balzer, Theodore Ruel, Diane V. Havlir +1
This document provides the statistical analytic plan (SAP) for evaluating health outcomes in the SEARCH-Youth study, a cluster randomized trial designed to evaluate the effect of a…
Adaptive Selection of the Optimal Strategy to Improve Precision and Power in Randomized Trials
Laura B. Balzer, Erica Cai, Lucas Godoy Garraza +1
Benkeser et al. demonstrate how adjustment for baseline covariates in randomized trials can meaningfully improve precision for a variety of outcome types. Their findings build on a…
Blurring cluster randomized trials and observational studies using Two-Stage TMLE to address sub-sampling, missingness, and minimal independent units
Joshua R. Nugent, Carina Marquez, Edwin D. Charlebois +2
Cluster randomized trials (CRTs) often enroll large numbers of participants, but due to logistical and fiscal challenges, only a subset of participants may be selected for measurem…
Statistical Analysis Plan for Health Outcomes in Phase 1 of the SEARCH-IPT Study
Laura B. Balzer, Joshua Nugent, Diane V. Havlir +1
This document provides the statistical analytic plan (SAP) for evaluating health outcomes in Phase 1 of the SEARCH-IPT Study, a cluster randomized trial to evaluate whether a multi…
Evaluating the Impact of State-Level Public Masking Mandates on New COVID-19 Cases and Deaths in the United States: A Demonstration of the Causal Roadmap
Angus K. Wong, Laura B. Balzer
At a national-level, we sought to investigate the effect of public masking mandates on COVID-19 in Fall 2020. Specifically, we aimed to evaluate how the relative growth of COVID-19…
Evaluating shifts in mobility and COVID-19 case rates in U.S. counties: A demonstration of modified treatment policies for causal inference with continuous exposures
Joshua R. Nugent, Laura B. Balzer
Previous research has shown mixed evidence on the associations between mobility data and COVID-19 case rates, analysis of which is complicated by differences between places on fact…
Defining and Estimating Effects in Cluster Randomized Trials: A Methods Comparison
Alejandra Benitez, Maya L. Petersen, Mark J. van der Laan +7
Across research disciplines, cluster randomized trials (CRTs) are commonly implemented to evaluate interventions delivered to groups of participants, such as communities and clinic…
Two-Stage TMLE to Reduce Bias and Improve Efficiency in Cluster Randomized Trials
Laura B. Balzer, Mark van der Laan, James Ayieko +5
Cluster randomized trials (CRTs) randomly assign an intervention to groups of individuals (e.g., clinics or communities) and measure outcomes on individuals in those groups. While…
The covariate-adjusted residual estimator and its use in both randomized trials and observational settings
Stephen A. Lauer, Nicholas G. Reich, Laura B. Balzer
We often seek to estimate the causal effect of an exposure on a particular outcome in both randomized and observational settings. One such estimation method is the covariate-adjust…
Semi-Supervised Record Linkage for Construction of Large-Scale Sociocentric Networks in Resource-limited Settings: An application to the SEARCH Study in Rural Uganda and Kenya
Yiqun Chen, Wenjing Zheng, Lillian B. Brown +13
This paper presents a novel semi-supervised algorithmic approach to creating large scale sociocentric networks in rural East Africa. We describe the construction of 32 large-scale…
A Primer on Causality in Data Science
Hachem Saddiki, Laura B. Balzer
Many questions in Data Science are fundamentally causal in that our objective is to learn the effect of some exposure, randomized or not, on an outcome interest. Even studies that…
Statistical Analysis Plan for SEARCH Phase I: Health Outcomes among Adults
Laura B. Balzer, Diane V. Havlir, Joshua Schwab +2
This document provides the analytic plan for evaluating adult HIV incidence, health, and implementation outcomes for the first phase of the SEARCH Study. Locked: November 27, 2017.…
A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure
Laura B. Balzer, Wenjing Zheng, Mark J. van der Laan +1
We often seek to estimate the impact of an exposure naturally occurring or randomly assigned at the cluster-level. For example, the literature on neighborhood determinants of healt…