Preprints and Articles under Review
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Williams, N., Rosenblum, M. & Iván Díaz. "Optimizing Precision and Power by Machine Learning in Randomized Trials, with an Application to COVID-19" arXiv preprint arXiv:2109.04294 (2021). [link]
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Benkeser, David, Iván Díaz, and Jialu Ran. "Inference for natural mediation effects under case-cohort sampling with applications in identifying COVID-19 vaccine correlates of protection." arXiv preprint arXiv:2103.02643 (2021). [link]
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Hejazi, N.S., Rudolph, K.E., van der Laan, M., and Díaz, Iván. "Nonparametric causal mediation analysis for stochastic interventional (in) direct effects." arXiv preprint arXiv:2009.06203 (2020). [link]
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Rudolph, Kara E., Catherine Gimbrone, Ellicott C. Matthay, Ivan Diaz, Corey S. Davis, John R. Pamplin II, Katherine Keyes, and Magdalena Cerda. "When effects cannot be estimated: redefining estimands to understand the effects of naloxone access laws." arXiv preprint arXiv:2105.02757 (2021). [link]
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Rudolph, K. E. & Díaz, Iván. When the ends don’t justify the means: Learning a treatment strategy to prevent harmful indirect effects. arXiv preprint arXiv:2101.08590 (2021). [link]
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Ogburn, E. L., Sofrygin, O., Díaz, Iván & van der Laan, M. J. Causal inference for social network data. arXiv preprint arXiv:1705.08527 (2019). [link]
Research Articles in Statistics and Epidemiology
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Díaz, Iván, Nicholas Williams, Katherine L. Hoffman, and Edward J. Schenck. "Nonparametric causal effects based on longitudinal modified treatment policies." Journal of the American Statistical Association (2021): 1-16. [software] [link]
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Rudolph, K.E., and Díaz, Iván. Efficiently transporting causal (in) direct effects to new populations under intermediate confounding and with multiple mediators. Biostatistics (2020). [link]
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Díaz, I., Hejazi, N. S., Rudolph, K. E., & van Der Laan, M. J. (2021). Nonparametric efficient causal mediation with intermediate confounders. Biometrika, 108(3), 627-641. [software] [link]
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Benkeser, D, Díaz, Iván, Luedtke, A, Segal, J, Scharfstein, D, Rosenblum, M. Improving precision and power in randomized trials for COVID‐19 treatments using covariate adjustment, for binary, ordinal, and time‐to‐event outcomes. Biometrics. (2020).[link]
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Díaz, Iván. Machine learning in the estimation of causal effects: targeted minimum loss-based estimation and double/debiased machine learning. Biostatistics 21, 353–358 (2020). [link]
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Díaz, Iván, Savenkov, O. & Kamel, H. Non-parametric targeted Bayesian estimation of class proportions in unlabeled data. Biostatistics (2020). [software] [link]
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Díaz, Iván & Hejazi, N. S. Causal mediation analysis for stochastic interventions. Journal of the Royal Statistical Society: Series B (Statistical Methodology) n/a. doi:10.1111/rssb.12362 (2020). [software] [link]
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Díaz, Iván. Statistical inference for data-adaptive doubly robust estimators with survival outcomes. Statistics in Medicine 38, 2735–2748 (2019). [software] [link]
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Díaz, Iván, Savenkov, O. & Ballman, K. Targeted learning ensembles for optimal individualized treatment rules with time-to-event outcomes. Biometrika 105, 723–738 (2018). [link]
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Díaz, Iván, Colantuoni, E., Hanley, D. F. & Rosenblum, M. Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards. Lifetime Data Analysis. ISSN: 1572-9249 (Feb. 2018). [software] [link]
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Díaz, Iván & van der Laan, M. J. Doubly robust inference for targeted minimum loss-based estimation in randomized trials with missing outcome data. Statistics in Medicine. ISSN: 1097-0258. doi:10.1002/sim.7389 (2018). [link]
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Scharfstein, D., McDermott, A., Díaz, Iván, Carone, M., Lunardon, N. & Turkoz, I. Global sensitivity analysis for repeated measures studies with informative drop-out: A semi-parametric approach. Biometrics 74, 207–219 (2018). [link]
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Díaz, Iván. Efficient estimation of quantiles in missing data models. Journal of Statistical Planning and Inference 190 (2017): 39-51. [software] [link]
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Díaz, Iván, Colantuoni, E. & Rosenblum, M. Enhanced precision in the analysis of randomized trials with ordinal outcomes. Biometrics 72, 422 (2016). [software] [link]
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Díaz, Iván, Carone, M. & van der Laan, M. J. Second-Order Inference for the Mean of a Variable Missing at Random. The International Journal of biostatistics 12, 333–349 (2016). [link]
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Díaz, Iván, Hubbard, A., Decker, A. & Cohen, M. Variable Importance and Prediction Methods for Longitudinal Problems with Missing Variables. PloS ONE 10 (2015). [link]
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Díaz, Iván & Rosenblum, M. Targeted Maximum Likelihood Estimation using Exponential Families. International Journal of Biostatistics 11, 233–251 (2015). [link]
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Frangakis, C. E., Qian, T., Wu, Z. & Díaz, Iván. Deductive derivation and turing-computerization of semiparametric efficient estimation. Biometrics 71 (with discussion), 867–874 (2015). [link]
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Rudolph, K. E., Díaz, Iván, Rosenblum, M. & Stuart, E. A. Estimating Population Treatment Effects From a Survey Subsample. American Journal of Epidemiology 180, 737–748 (2014). [link]
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Díaz, Iván & van der Laan, M. J. Assessing the Causal Effect of Policies: An Example Using Stochastic Interventions. The international journal of biostatistics 9, 161–174 (2013). [link]
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Díaz, Iván & van der Laan, M. J. Sensitivity analysis for causal inference under unmeasured confounding and measurement error problems. The international journal of biostatistics 9, 149–160 (2013). [link]
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Díaz, Iván & van der Laan, M. J. Targeted Data Adaptive Estimation of the Causal Dose–Response Curve. Journal of Causal Inference 1, 171–192 (2013). [link]
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Díaz, Iván, and Mark van der Laan. Population intervention causal effects based on stochastic interventions. Biometrics 68.2 (2012): 541-549. [software] [link]
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Díaz, Iván & van der Laan, M. J. Super Learner Based Conditional Density Estimation With Application to Marginal Structural Models. The International Journal of Biostatistics 7, 1–20 (2011). [link]
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Cepeda-Cuervo, E., Aguilar, W., Cervantes, V., Corrales, M., Díaz, Iván & Rodríguez, D. Intervalos de confianza e intervalos de credibilidad para una proporción. Revista Colombiana de Estadística 31, 211–228 (2008).
Book Chapters
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Carone, M., Díaz, Iván & van der Laan, M. J. in Targeted Learning in Data Science 483–510 (Springer, 2018).
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Díaz, Iván, Luedtke, A. R. & van der Laan, M. J. in Targeted Learning in Data Science 511–522 (Springer, 2018).
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Díaz, Iván & van der Laan, M. J. in Targeted Learning in Data Science 219–232 (Springer, 2018).
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Díaz, Iván. in Handbook on Big Data (eds van der Laan, M. J., Buhlman, P., Kane, M. & Drineas, P.) (Chapman and Hall, 2016).
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Díaz, Iván, Hubbard, A. & van der Laan, M. in Targeted Learning (eds van der Laan, M. J. & Rose, S.) (Springer, 2011).
Selected Publications on Clinical and Health Services Research
(see CV for a full list)
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Rudolph, K., Díaz, Iván, Hejazi, N., van der Laan, M., Luo, S., Shulman, M., Campbell, A., Rotrosen, J., and Nunes, E. "Explaining differential effects of medication for opioid use disorder using a novel approach incorporating mediating variables." Addiction (2020).
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Kummer, B. R., Díaz, Iván, Wu, X., Aaroe, A. E., Chen, M. L., Iadecola, C., Kamel, H. & Navi, B. B. Associations between cerebrovascular risk factors and parkinson disease. Annals of neurology 86,572–581 (2019).
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Murthy, S., Díaz, Iván, Wu, X., Merkler, A., Iadecola, C., Navi, B. B. & Kamel, H. Intracerebral Hemorrhage and Increased Risk of Arterial Ischemic Events in Annals of Neurology 86 (2019), S259–S260.
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Mosconi, L., Rahman, A., Díaz, Iván, Wu, X., Scheyer, O., Hristov, H. W., Vallabhajosula, S., Isaacson, R. S., de Leon, M. J. & Brinton, R. D. Increased Alzheimer’s risk during the menopause transition: A 3-year longitudinal brain imaging study. PloS one 13 (2018).
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Kreif, N., Grieve, R., Díaz, Iván & Harrison, D. Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury. Health economics 24, 1213–1228 (2015).