SelectedWorks - Laura B. Balzer

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Selected Works of Laura B. Balzer

Assistant Professor of Biostatistics

Laura B. Balzer, PhD MPhil, is an Assistant Professor of Biostatistics at the University of Massachusetts-Amherst. She earned her PhD from the University of California-Berkeley and completed her post-doctoral studies at the Harvard T.H. Chan School of Public Health. Dr. Balzer's areas of expertise include Causal Inference and Machine Learning. She is the lead statistician for two ongoing cluster randomized trials: the SEARCH study for HIV prevention and treatment in East Africa and the SPIRIT study for TB prevention in Uganda. Dr. Balzer is passionate about teaching introductory and advanced causal inference and statistical methods. She was awarded the ASA’s Causality in Statistics Education Award as well as the Gertrude M. Cox Scholarship.

Learn more about Causal Inference: www.ucbbiostat.com

Learn more about the Sustainable East Africa Research in Community Health (SEARCH) trial: www.searchendaids.com

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About Laura B. Balzer

Laura B. Balzer, PhD MPhil, is an Assistant Professor of Biostatistics at the University of Massachusetts-Amherst. She earned her PhD from the University of California-Berkeley and completed her post-doctoral studies at the Harvard T.H. Chan School of Public Health. Dr. Balzer's areas of expertise include Causal Inference and Machine Learning. She is the lead statistician for two ongoing cluster randomized trials: the SEARCH study for HIV prevention and treatment in East Africa and the SPIRIT study for TB prevention in Uganda. Dr. Balzer is passionate about teaching introductory and advanced causal inference and statistical methods. She was awarded the ASA’s Causality in Statistics Education Award as well as the Gertrude M. Cox Scholarship.

Learn more about Causal Inference: www.ucbbiostat.com

Learn more about the Sustainable East Africa Research in Community Health (SEARCH) trial: www.searchendaids.com

July 2017 - Present	Assistant Professor of Biostatistics, University of Massachusetts Amherst
Institution Organization Position Type Position Title Time Period to Options Current Position Affiliate my profile with this Institution
2015 - 2017	Post-Doctoral Fellow in Biostatistics, Harvard University
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2010 - 2015	PhD in Biostatistics, University of California, Berkeley
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Public Health, Epidemiology, Biostatistics, Clinical Trials, and Design of Experiments and Sample Surveys

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

Department of Biostatistics & Epidemiology

School of Public Health & Health Sciences

University of Massachusetts - Amherst

416 Arnold House, 715 North Pleasant St.

Amherst, MA 01003-9304

Email:

SER2018 - Workshop materials (Laura Balzer) (2018)

This workshop will introduce participants to a “causal roadmap” approach to epidemiologic questions: 1) clear statement of the scientific question, 2) definition of the causal model and parameter of interest, 3) assessment of identifiability – ...

Articles (12)

Stacked generalization: an introduction to super learning (Laura Balzer and Ashley Naimi) 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 (Maya Petersen, Laura Balzer, Dalsone Kwarsiima, et al.) 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% ...

“All Generalizations Are Dangerous, Even This One.” —Alexandre Dumas (Laura Balzer) Epidemiology (2017)

Constrained binary classification using ensemble learning: an application to cost-efficient targeted PrEP strategies (Wenjing Zheng, Laura Balzer, Mark J. van der Laan, et al.) 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 (Laura Balzer, Patrick Staples, Jukka-Pekka Onnela, et al.) 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 ...

Supplementary Materials: 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 (Laura Balzer, Patrick Staples, Jukka-Pekka Onnela, et al.) Clinical Trials (2017)

Supplementary Materials

Adaptive Pre-specification in Randomized Trials With and Without Pair-Matching (Laura Balzer, M. van der Laan, M. Petersen, et al.) 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 (Laura Balzer, M. Petersen, M. van der Laan, et al.) 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 (Laura Balzer, J. Ahern, S. Galea, et al.) 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 (M. Pearl, Laura Balzer and J. Ahern) 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 (Laura Balzer, M Petersen, M van der Laan, et al.) 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 (M. van der Laan, Laura Balzer and M. Petersen) 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 ...

Stacked Generalization: An Introduction to Super Learning (Ashley Naimi and Laura Balzer) (2017)

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 what is now known as “Super ...

A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure (Laura Balzer, Wenjing Zheng, Mark J. van der Laan, et al.) arXiv - UPDATED VERSION (2017)

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 ...

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, et al.) 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, ...

Why Match in Individually and Cluster Randomized Trials? (Laura B. Balzer, Maya L. Petersen and Mark J. van der Laan) U.C. Berkeley Division of Biostatistics Working Paper Series (2012)

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 ...

"Tutorial For Causal Inference" - Chapter 20 of Handbook of Big Data (Laura Balzer, Maya Petersen and Mark J. van der Laan) Handbook of Big Data (2016)

L. Balzer, M. Petersen, and M.J. van der Laan. Tutorial for causal inference. In P. Buhlmann,P. Drineas, M. Kane, and M. van der Laan, editors, Handbook of Big Data. Chapman & Hall/CRC,2016.

Overview of ongoing & future research (Laura Balzer) UMass Amherst (2018)

Causal Inference in a Big Data World: The Roadmap (Laura Balzer) X Congresso Brasileiro de Epidemiologia (2017)

Applications of Targeted Maximum Likelihood Estimation (Laura Balzer) 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 ...

Introduction to Targeted Learning (Laura Balzer) University of California, Berkeley (2016)

Adaptive Pair-Matching in the SEARCH trial & Estimation of the Intervention Effect (Laura Balzer, Maya Petersen and Mark van der Laan) 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 ...

Designing the SEARCH trial: Ph250b in Practice (Laura Balzer and the SEARCH Consortium) (2013)

Estimating the impact of community-level interventions: The SEARCH Trial and HIV Prevention in Sub-Saharan Africa (Laura Balzer, Maya Petersen, Joshua Schwab, et al.) 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, ...