Susan Gruber is Senior Director of IMEDS-Methods Research at the Reagan-Udall
Foundation for the FDA. Her work focuses on initiating and facilitating methods
development for detecting safety signals in electronic health data. She holds a Ph.D. in
Biostatistics, an MPH in Epidemiology and Biostatistics, and an MS in Computer Science.

Articles

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Evaluating treatment effectiveness under model misspecification: A comparison of targeted maximum likelihood estimation with bias-corrected matching (with Noemi Kreif, Rosalba Radice, Richard Grieve, and Jasjeet S. Sekhon), Statistical Methods in Medical Research (2014)

Statistical approaches for estimating treatment effectiveness commonly model the endpoint, or the propensity score, using...

 

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Empirical Performance of a New User Cohort Method: Lessons for Developing a Risk Identification and Analysis System (with Martijn J. Schuemie, Patrick B. Ryan, Ivan Zorych, and David Madigan), Drug Safety (2013)

Background: Observational healthcare data offer the potential to enable identification of risks of medical pro-...

 

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An Application of Targeted Maximum Likelihood Estimation to the Meta-Analysis of Safety Data (with Mark van der Laan), Biometrics (2013)

Safety analysis to estimate the effect of a treatment on an adverse event poses a...

 
Targeted Minimum Loss Based Estimation of a Causal Effect on an Outcome with Known Conditional Bounds (with Mark J. van der Laan), The International Journal of Biostatistics (2012)

This paper presents a targeted minimum loss based estimator (TMLE) that incorporates known conditional bounds...

 
Targeted Minimum Loss Based Estimation of Causal Effects of Multiple Time Point Interventions (with Mark J. van der Laan), The International Journal of Biostatistics (2012)

We consider estimation of the effect of a multiple time point intervention on an outcome...

 

Technical Reports

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Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models (with Maya L. Petersen, Joshua Schwab, Nello Blaser, Michael Schomaker, and Mark J. van der Laan), U.C. Berkeley Division of Biostatistics Working Paper Series (2013)

This paper presents a novel targeted maximum likelihood estimator (TMLE) estimator for the parameters of...
 

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Targeted Minimum Loss Based Estimation of an Intervention Specific Mean Outcome (with Mark J. van der Laan), U.C. Berkeley Division of Biostatistics Working Paper Series (2011)

Targeted minimum loss based estimation (TMLE) provides a template for the construction of semiparametric locally...

 

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Targeted Minimum Loss Based Estimator that Outperforms a given Estimator (with Mark J. van der Laan), U.C. Berkeley Division of Biostatistics Working Paper Series (2011)

Targeted minimum loss based estimation (TMLE) provides a template for the construction of semiparametric locally...

 

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The Relative Performance of Targeted Maximum Likelihood Estimators (with Kristin E. Porter, Mark J. van der Laan, and Jasjeet S. Sekhon), U.C. Berkeley Division of Biostatistics Working Paper Series (2011)

There is an active debate in the literature on censored data about the relative performance...

 

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tmle: An R Package for Targeted Maximum Likelihood Estimation (with Mark J. van der Laan), U.C. Berkeley Division of Biostatistics Working Paper Series (2011)

Targeted maximum likelihood estimation (TMLE) represents an approach for construction of an efficient double-robust semi-parametric...

 

Software

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bias.pboot (with Kristin Porter, Maya Petersen, Yue Wang, and Mark J. van der Laan) (2010)

bias.pboot is R software for diagnosing positivity bias using a parametric bootstrap

 

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ctmle: an R package for collaborative targeted maximum likelihood estimation (with Mark J. van der Laan) (2010)

ctmle provides R source code for estimating an additive point treatment effect using collaborative targeted...

 

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tmle: an R package for targeted maximum likelihood estimation (with Mark J. van der Laan) (2010)

Targeted maximum likelihood estimation of a point treatment effect on a binary or continuous outcome

 

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tmleLite: A simplified R package for targeted maximum likelihood estimation (with Mark J. van der Laan) (2009)

tmleLite provides software to estimate the additive effect of a binary point treatment on a...

 

Presentations

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Active presecription drug safety surveillance: Exploring OMOP 2011-2012 experiments (with James M. Robins), OMOP community meeting (2013)

The Observational Medical Outcomes Partnership (OMOP), a consortium of pharmaceutical, FDA, and academic researchers focuses...

 

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A Targeted Confounder Selection Strategy for Propensity Score Estimation, McGill University (2013)

These slides provide an introduction to data-adaptive propensity score estimation, and the collaborative targeted maximum...

 

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An Overview of Targeted Maximum Likelihood Estimation, UNC Gillings School of Public Health (2013)

These slides provide an introduction to targeted maximum likelihood estimation in a point treatment setting.