About Laura B. Balzer
Dr. Laura B. Balzer is an Associate Professor of Biostatistics at the University of California, Berkeley. Her expertise is in causal inference, machine learning, and messy real-world data. Dr. Balzer’s work addresses challenges in the design and analysis of both randomized trials and observational studies, including novel approaches for semi-parametric inference, differential measurement, and complex dependence. Dr. Balzer’s work has largely been motivated by ongoing collaborations in Uganda and Kenya. She is the Primary Statistician for several studies aiming to eliminate HIV and improve community health in rural East Africa (e.g., searchendaids.com). Overall, Dr. Balzer’s work is informed by cross-disciplinary, real-world problems and aims to ensure methodological advances in academia translate into real-world impact.
|Present||Associate Professor of Biostatistics, University of California - Berkeley|
|2015 - 2017||Post-Doctoral Fellow in Biostatistics, Harvard University|
|2010 - 2015||PhD in Biostatistics, University of California, Berkeley|
|Assistant Professor of Biostatistics, University of Massachusetts Amherst|
Honors and Awards
- Chin Long Chiang Biostatistics Student of the Year
- Causality in Statistics Education Award
- Gertrude M. Cox Scholarship
- Berkeley Fellow (University of California, Berkeley)
- Director's Award (University of Cambridge, UK)
- Summa Cum Laude (University of Vermont)
- Honors College Scholar (University of Vermont)
- Barry M. Goldwater Scholarship Award
Division of Biostatistics
School of Public Health
University of California Berkeley
Methodological Articles; Please see Google Scholar for a full list (13)
A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure Statistical Methods in Medical Research (2018)
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 health continues to grow. Likewise, community randomized trials are ...
Stacked generalization: an introduction to super learning European Journal of Epidemiology (2018)
Stacked generalization is an ensemble method that allows researchers to combine several different prediction algorithms into one. Since its introduction in the early 1990s, the method has evolved several times into a host of methods ...
Association of Implementation of a Universal Testing and Treatment Intervention With HIV Diagnosis, Receipt of Antiretroviral Therapy, and Viral Suppression in East Africa JAMA (2017)
IMPORTANCE Antiretroviral treatment (ART) is now recommended for all HIV-positive persons. UNAIDS has set global targets to diagnose 90% of HIV-positive individuals, treat 90% of diagnosed individuals with ART, and suppress viral replication among 90% ...
Constrained binary classification using ensemble learning: an application to cost-efficient targeted PrEP strategies Statistics in Medicine (2017)
Binary classification problems are ubiquitous in health and social sciences. In many cases, one wishes to balance two competing optimality considerations for a binary classifier. For instance, in resource-limited settings, an human immunodeficiency virus prevention ...
Using a network-based approach and targeted maximum likelihood estimation to evaluate the effect of adding pre-exposure prophylaxis to an ongoing Test-and-Treat trial Clinical Trials (2017)
Background: Several cluster randomized trials are underway to investigate the implementation and effectiveness of a universal test-and-treat strategy on the HIV epidemic in sub-Saharan Africa. We consider nesting studies of pre-exposure prophylaxis (PrEP) within these ...
Adaptive Pre-specification in Randomized Trials With and Without Pair-Matching Statistics in Medicine (2016)
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 ...
Targeted estimation and inference for the sample average treatment effect in trials with and without pair-matching Statistics in Medicine (2016)
In cluster randomized trials, the study units usually are not a simple random sample from some clearly definedtarget population. Instead, the target population tends to be hypothetical or ill-defined, and the selection of studyunits tends ...
Estimating Effects with Rare Outcomes and High Dimensional Covariates: Knowledge is Power Epidemiologic Methods (2016)
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 ...
Targeted Estimation of Marginal Absolute and Relative Associations in Case-Control Data: An Application in Social Epidemiology Epidemiology (2016)
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, ...
Adaptive pair-matching in randomized trials with unbiased and efficient effect estimation Statistics in Medicine (2015)
In randomized trials, pair-matching is an intuitive design strategy to protect study validity and to potentiallyincrease study power. In a common design, candidate units are identified, and their baseline characteristics usedto create the best n∕2 ...
Adaptive matching in randomized trials and observational studies Journal of Statistical Research (2012)
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 ...
Technical Reports (2)
Sustainable East Africa Research in Community Health (SEARCH): a community cluster randomized study of HIV "test and treat" using multi-disease approach in rural Uganda and Kenya (2017)
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. Embargoed until July 25, 2018.
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 U.C. Berkeley Division of Biostatistics Working Paper Series (2017)
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, ...
Contributions to Books (1)
Applications of Targeted Maximum Likelihood Estimation UCSF (2016)
This workshop will introduce participants to a "causal roadmap" approach to Public Health and Medical questions: 1) clear statement of the scientific question, 2) definition of the causal model and parameter of interest, 3) assessment ...
Adaptive Pair-Matching in the SEARCH trial & Estimation of the Intervention Effect Joint Statistical Meetings (JSM) (2014)
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 ...
Estimating the impact of community-level interventions: The SEARCH Trial and HIV Prevention in Sub-Saharan Africa WNAR/IMS and Graybill Conference (2012)
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, ...
Software - For updates, please see GITHUB: https://github.com/LauraBalzer (5)
R Code for Adaptive Prespecification (2016)
Sample R code and simulations to illustrate estimation and inference for the PATE and SATE with the unadjusted estimator, MLE with a priori-specified adjustment set, TMLE with adaptive pre-specification for initial estimation of outcome regression, and ...