A comprehensive list of my publications can be found on Google Scholar.
† lead author was my student or postdoc advisee during the work
Statistical theory and methodology
▪ 2023
A. Luedtke, I. Chung. One-Step Estimation of Differentiable Hilbert-Valued Parameters. arXiv. [tech rep]
L. van der Laan,† E. Ulloa-Pérez,† M. Carone, A. Luedtke. Causal isotonic calibration for heterogeneous treatment effects. International Conference on Machine Learning. [paper] [tech rep] [code]
L. van der Laan,† E. Ulloa-Pérez,† M. Carone, A. Luedtke. Causal isotonic calibration for heterogeneous treatment effects. International Conference on Machine Learning. [paper] [tech rep] [code]
T. J. Huang,† A. Luedtke, I. McKeague. Efficient Estimation of the Maximal Association between Multiple Predictors and a Survival Outcome. Annals of Statistics (in press). [tech rep]
X. Li,† S. Li, A. Luedtke. Estimating the Efficiency Gain of Covariate-Adjusted Analyses in Future Clinical Trials Using External Data. Journal of the Royal Statistical Society: Series B. [paper] [tech rep]
T. Westling, A. Luedtke, P. Gilbert, M. Carone. Inference for treatment-specific survival curves using machine learning. Journal of the American Statistical Association. [paper] [tech rep]
N. Galanter,† M. Carone, R. C. Kessler, A. Luedtke. Can the potential benefit of individualizing treatment be assessed using trial summary statistics alone?" American Journal of Epidemiology (in press). [tech rep]
H. Qiu,† A. Luedtke. Adversarial meta-learning of Gamma-minimax estimators that leverage prior knowledge. Electronic Journal of Statistics. [paper] [tech rep] [code]
T. Fisher, A. Luedtke, M. Carone, N. Simon. Marginal Bayesian Posterior Inference using Recurrent Neural Networks with Application to Sequential Models. Statistica Sinica. [paper]
L. van der Laan,† M. Carone, A. Luedtke, M. van der Laan. Adaptive debiased machine learning using data-driven model selection techniques. arXiv. [tech rep]
S. Li,† P. B. Gilbert, A. Luedtke. Data fusion using weakly aligned sources. arXiv. [tech rep]
▪ 2022
H. Qiu,† M. Carone, A. Luedtke. Individualized treatment rules under stochastic treatment cost constraints. Journal of Causal Inference. [paper] [tech rep]
T. J. Huang,† A. Luedtke, The AMP Investigator Group. Improved Efficiency for Cross-Arm Comparisons via Platform Designs. Biostatistics. [paper] [tech rep]
N. Laha,† Z. Moodie, Y. Huang, A. Luedtke. Improved inference for vaccine-induced immune responses via shape-constrained methods. Electronic Journal of Statistics. [paper] [tech rep]
A. Elder,† M. Carone, P. Gilbert, A. Luedtke. A general adaptive framework for multivariate point null testing. arXiv. [tech rep]
▪ 2021
A. Luedtke, I. Chung, O. Sofrygin. Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures. Journal of Machine Learning Research . [paper] [code]
H. Qiu,† M. Carone, E. Sadikova, M. Petukhova, R. C. Kessler, and A. Luedtke. Optimal individualized decision rules using instrumental variable methods. Journal of the American Statistical Association (with discussion). [paper] [discussions: 1, 2] [rejoinder]
H. Qiu,† A. Luedtke, and M. Carone. Universal sieve-based strategies for efficient estimation using machine learning tools. Bernoulli. [paper] [tech rep]
D. Benkeser, I. Díaz, A. Luedtke, J. Segal, D. Scharfstein, M. Rosenblum. Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Ordinal or Time to Event Outcomes. Biometrics (with discussion). [paper] [discussions: 1, 2, 3] [rejoinder]
S. Li,† X. Li,† A. Luedtke. Discussion of Kallus (2020) and Mo, Qi, and Liu (2020): New Objectives for Policy Learning. Journal of the American Statistical Association. [paper] [tech rep]
▪ 2020
A. Luedtke, M. Carone, N. Simon, O. Sofrygin. Deep Adversarial Learning of Optimal Statistical Procedures. Science Advances. [paper] [code]
A. Luedtke and A. Chambaz. Performance guarantees for policy learning. Annales de l'Institut Henri Poincaré. [paper] [tech rep]
A. Luedtke, J. Wu. Efficient Principally Stratified Treatment Effect Estimation in Crossover Studies with Absorbent Binary Endpoints. Journal de la Société Française de Statistique. [tech rep] [paper]
L. Wang, A. Luedtke, and Y. Huang. Assessing the incremental value of new biomarkers based on OR rules. Biostatistics. [paper] [tech rep]
▪ 2019
A. Luedtke, M. Carone, and M. J. van der Laan. An omnibus test of equality in distribution for unknown functions. Journal of the Royal Statistical Society: Series B. [paper] [tech rep]
A. Luedtke, E. Kaufmann, and A. Chambaz. Asymptotically optimal algorithms for budgeted multiple play bandits. Machine Learning. [paper] [tech rep]
T. J. VanderWeele, A. Luedtke, M. J. van der Laan, R. C. Kessler. Selecting optimal subgroups for treatment using many covariates. Epidemiology. [paper] [tech rep]
H. Qiu,† A. Luedtke, M. J. van der Laan. Comment on 'Entropy Learning for Dynamic Treatment Regimes' by Binyan Jiang, Rui Song, et al.. Statistica Sinica. [paper]
▪ 2018
A. Luedtke and M. J. van der Laan. Parametric-rate inference for one-sided differentiable parameters. Journal of the American Statistical Association. [paper] [tech rep]
Suppose one has a collection of parameters indexed by a (possibly infinite dimensional) set. Given data generated from some distribution, the objective is to estimate the maximal parameter in this collection evaluated at this distribution. This estimation problem is typically non-regular when the maximizing parameter is non-unique, and as a result standard asymptotic techniques generally fail in this case. We present a technique for developing parametric-rate confidence intervals for the quantity of interest in these non-regular settings. We show that our estimator is asymptotically efficient when the maximizing parameter is unique so that regular estimation is possible.
We apply our technique to a recent example from the literature in which one wishes to report the maximal absolute correlation between a prespecified outcome and one of p predictors. The simplicity of our technique enables an analysis of the previously open case where p grows with sample size. Specifically, we only require that log p grows slower than the square root of n, where n is the sample size. We show that, unlike earlier approaches, our method scales to massive data sets: the point estimate and confidence intervals can be constructed in O(np) time.
M. Carone, A. Luedtke, and M. J. van der Laan. Toward computerized efficient estimation in infinite-dimensional models. Journal of the American Statistical Association. [paper] [tech rep]
▪ 2017
A. Luedtke and M. J. van der Laan. Evaluating the impact of treating the optimal subgroup. Statistical Methods in Medical Research. [paper] [tech rep]
A. Luedtke, O. Sofrygin, M. J. van der Laan, and M. Carone. Sequential double robustness in right-censored longitudinal models. arXiv. [tech rep]
▪ 2016
A. Luedtke and M. J. van der Laan. Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy. Annals of Statistics. [paper]
A. Luedtke and M. J. van der Laan. Optimal individualized treatments in resource-limited settings. International Journal of Biostatistics. [paper] [tech rep]
A. Luedtke and M. J. van der Laan. Comment. Journal of the American Statistical Association. [paper]
A. Luedtke and M. J. van der Laan. Super-learning of an optimal dynamic treatment rule. International Journal of Biostatistics. [tech rep]
▪ 2015
A. Luedtke, M. Carone, and M. J. van der Laan. Discussion of 'Deductive derivation and Turing-computerization of semiparametric efficient estimation' by Frangakis et al. Biometrics. [paper]
Comment on:
C. E. Frangakis, T. Qian, Z. Wu, and I. Díaz. Deductive derivation and Turing-computerization of semiparametric efficient estimation. Biometrics. [paper]
Rejoinder:
C. E. Frangakis, T. Qian, Z. Wu, and I. Díaz. Rejoinder to 'Discussions on: Deductive derivation and turing-computerization of semiparametric efficient estimation'. Biometrics. [paper]
C. E. Frangakis, T. Qian, Z. Wu, and I. Díaz. Deductive derivation and Turing-computerization of semiparametric efficient estimation. Biometrics. [paper]
Rejoinder:
C. E. Frangakis, T. Qian, Z. Wu, and I. Díaz. Rejoinder to 'Discussions on: Deductive derivation and turing-computerization of semiparametric efficient estimation'. Biometrics. [paper]
M. J. van der Laan and A. Luedtke. Targeted learning of the mean outcome under an optimal dynamic treatment rule. Journal of Causal Inference. [paper]
Selected scientific collaborations
C. B. Turley, L. Tables, T. Fuller, … , A. Luedtke. Modifiers of Covid-19 Vaccine Efficacy: Results from Four Covid-19 Prevention Network Efficacy Trials. Vaccine, 2023. [paper]
A. Luedtke, R. C. Kessler. New Directions in Research on Heterogeneity of Treatment Effects for Major Depression. JAMA Psychiatry, 2021. [paper]
S. Sridhar, A. Luedtke, E. Langevin, M. Zhu, M. Bonaparte, et al. Effect of Dengue Serostatus on Dengue Vaccine Safety and Efficacy. New England Journal of Medicine, 2018. [paper]
Y. Fong, A. B. McDermott, et al. Immune correlates analysis of the ENSEMBLE single Ad26.COV2.S dose vaccine efficacy clinical trial. Nature Microbiology, 2023. [paper]
R. C. Kessler, A. Luedtke. Pragmatic Precision Psychiatry — A New Direction for Optimizing Treatment Selection. JAMA Psychiatry, 2021. [paper]
C. S. Wu, A. Luedtke, E. Sadikova, H. J. Tsai, S. C. Liao, et al. Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia. JAMA Network Open, 2020. [paper]
R. C. Kessler, R. M. Bossarte, A. Luedtke, A. M. Zaslavsky, and J. R. Zubizarreta. Suicide prediction models: a critical review of recent research with recommendations for the way forward. Molecular Psychiatry, 2019. [paper]
A. Luedtke, E. Sadikova, R. C. Kessler. Sample size requirements for multivariate models to predict between-patient differences in best treatments of major depressive disorder. Clinical Psychological Science, 2019. [paper]
P. B. Gilbert and A. Luedtke. Statistical Learning Methods to Determine Immune Correlates of Herpes Zoster in Vaccine Efficacy Trials. Journal of Infectious Diseases, 2018. [paper]
J. M. Platt, K. A. McLaughlin, A. Luedtke, J. Ahern, A. Kaufman, K. M. Keyes. Targeted estimation of the relationship between childhood adversity and fluid intelligence in a US population sample of adolescents. American Journal of Epidemiology, 2018. [paper]
J. Ahern, D. Karasek, A. Luedtke, T. A. Bruckner, M. J. van der Laan. Racial/Ethnic Differences in the Role of Childhood Adversities for Mental Disorders Among a Nationally Representative Sample of Adolescents. Epidemiology, 2016. [paper]