David  Benkeser

Publications

• Wu Z, Berokwitz S, Heagerty P, Benkeser D, 2022, A two-stage super learner for healthcare expenditures, Health Services and Outcomes Research Methodology, ,
• Yang G, Balzer LB, Benkeser D, 2022, Causal Inference Methods for Vaccine Sieve Analysis with Effect Modification, Statistics in Medicine, ,
• Gilbert PB, Montefiori DC, McDermott A, Fong Y, Benkeser D, Deng W, Zhou H, Houchens CR, et al., 2021, Immune Correlates Analysis of the mRNA-1273 COVID-19 Vaccine Efficacy Trial, Science, ,
• Benkeser D, Ran J, 2021, Nonparametric inference for interventional effects with multiple mediators, Journal of Causal Inference, ,
• Benkeser D, Cai W, van der Laan MJ, 2020, A nonparametric super-efficient estimator of the average treatment effect, Statistical Science, ,
• Millett GA, Jones AT, Benkeser D, Baral S, Mercer L, Beyrer C, Honermann B, Lankiewicz E, Mena L, Crowley J, Sherwood J, Sullivan P, 2020, Assessing Differential Impacts of COVID-19 on Black Communities, Annals of Epidemiology, ,
• Mehrotra DV, Janes HE, Fleming TR, Annunziato PW, Neuzil KM, Carpp LN, Benkeser D, Brown ER, Cho I, Donnell D, Fay MP, Fong Y, Han S, Hirsch I, Huang Y, Huang Y, Hyrien O, Juraska M, Luedtke A, Nason M, Vandebosch A, Zhou H, Cohen M, Corey L, Hartzel J, Follmann D, Gilbert PB, 2020, Clinical Endpoints for Evaluating Efficacy in COVID-19 Vaccine Trials, Annals of Internal Medicine, ,
• Benkeser D, Horvath K, Reback CJ, Rusow J, Hudgens M, 2020, Design and analysis considerations for a sequentially randomized HIV prevention trial, Statistics in Biosciences, ,
• Hejazi N, van der Laan MJ, Gilbert P, Janes H, Benkeser D, 2020, Efficient nonparametric inference on the effects of stochastic interventions under two-phase sampling, with applications to vaccine efficacy trials, Biometrics, ,
• Benkeser D†, Diaz I†, Luedtke A†, Segal J, Scharfstein D, Rosenblum M, 2020, Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Binary, Ordinal, and Time-to-Event Outcomes, Biometrics, ,
• Benkeser D, Carone M, Gilbert PB, 2019, Estimating and testing vaccine sieve effects using machine learning, Journal of the American Statistical Association, 114, 1038-1049
• Benkeser D, Petersen M, van der Laan MJ, 2019, Improved small-sample estimation of non- linear cross-validated prediction metrics, Journal of the American Statistical Association, ,
• Magaret CA†, Benkeser D†, Williamson BD†, Borate BR, Carpp L, Georgiev I, Setliff I, Dingens AS, Simon N, Carone M, Simpkins C, Montefiori D, Alter G, Juraska M, Edelfsen PT, Karuna S, Mgodi NM, Edugupanti S, Gilbert PB , 2019, Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features, PLoS Computational Biology, 15,
• Ju C†, Benkeser D†, van der Laan MJ, 2019, Robust inference on the average treatment effect using the outcome highly adaptive lasso, Biometrics, ,
• Benkeser D, Carone M, van der Laan MJ, Gilbert PB, 2017, Nonparametric doubly-robust inference on the average treatment effect, Biometrika, 104, 863-880
• Neafsey D, Juraska M, Bedford T†, Benkeser D†, Valim C†, Griggs A, Lievens M, et al, 2015, Genetic diversity and protective efficacy of the RTS,S/AS01 Malaria Vaccine, New England Journal of Medicine, 373, 2025-2037