Targeted maximum likelihood estimation for a binary treatment: A tutorial
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Miguel Angel Luque-Fernandez | Michael Schomaker | Mireille E Schnitzer | Bernard Rachet | B. Rachet | M. Schnitzer | M. Schomaker | M. Luque-Fernández
[1] Laura Balzer,et al. Estimating Effects with Rare Outcomes and High Dimensional Covariates: Knowledge is Power , 2016, Epidemiologic methods.
[2] M. J. van der Laan,et al. Evaluating the Impact of a HIV Low-Risk Express Care Task-Shifting Program: A Case Study of the Targeted Learning Roadmap , 2016, Epidemiologic methods.
[3] Dennis D. Boos,et al. Essential Statistical Inference: Theory and Methods , 2013 .
[4] Mark J van der Laan,et al. EFFECT OF BREASTFEEDING ON GASTROINTESTINAL INFECTION IN INFANTS: A TARGETED MAXIMUM LIKELIHOOD APPROACH FOR CLUSTERED LONGITUDINAL DATA. , 2014, The annals of applied statistics.
[5] D. Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .
[6] Susan Gruber,et al. Targeted Learning in Healthcare Research , 2015, Big Data.
[7] M. J. van der Laan,et al. The International Journal of Biostatistics Targeted Maximum Likelihood Learning , 2011 .
[8] Emma Sutton. Rejoinder , 2010 .
[9] P. Austin. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies , 2011, Multivariate behavioral research.
[10] J. Robins,et al. Marginal Structural Models and Causal Inference in Epidemiology , 2000, Epidemiology.
[11] M. J. van der Laan,et al. Collaborative Double Robust Targeted Maximum Likelihood Estimation , 2010, The international journal of biostatistics.
[12] Mark J. van der Laan,et al. Why prefer double robust estimators in causal inference , 2005 .
[13] James M. Robins,et al. Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models: Rejoinder , 1999 .
[14] Dennis D. Boos,et al. Essential Statistical Inference , 2013 .
[15] E. Steyerberg,et al. The changing prevalence of comorbidity across the age spectrum. , 2008, Critical reviews in oncology/hematology.
[16] D. Rubin. Causal Inference Using Potential Outcomes , 2005 .
[17] Catherine M Crespi,et al. Semiparametric Estimation of the Impacts of Longitudinal Interventions on Adolescent Obesity using Targeted Maximum-Likelihood: Accessible Estimation with the ltmle Package , 2014, Journal of causal inference.
[18] Mark J. van der Laan,et al. Super learner based conditional density estimation with application to marginal structural models. , 2011 .
[19] Mark J van der Laan,et al. The International Journal of Biostatistics An Application of Collaborative Targeted Maximum Likelihood Estimation in Causal Inference and Genomics , 2011 .
[20] Stephen R Cole,et al. The Parametric g-Formula for Time-to-event Data: Intuition and a Worked Example , 2014, Epidemiology.
[21] Robert W. Platt,et al. Targeted Maximum Likelihood Estimation for Pharmacoepidemiologic Research , 2016, Epidemiology.
[22] Mark J van der Laan,et al. The International Journal of Biostatistics Super Learner Based Conditional Density Estimation with Application to Marginal Structural Models , 2012 .
[23] M. J. van der Laan,et al. A General Implementation of TMLE for Longitudinal Data Applied to Causal Inference in Survival Analysis , 2012, The international journal of biostatistics.
[24] Robert Platt. Faculty of 1000 evaluation for Targeted maximum likelihood estimation for a binary treatment: A tutorial. , 2018 .
[25] M. J. van der Laan,et al. Statistical Applications in Genetics and Molecular Biology Super Learner , 2010 .
[26] C. Gross,et al. Diagnosis of cancer as an emergency: a critical review of current evidence , 2017, Nature Reviews Clinical Oncology.
[27] Ashutosh Kumar Singh,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .
[28] J. Robins,et al. Doubly Robust Estimation in Missing Data and Causal Inference Models , 2005, Biometrics.
[29] Mark J. van der Laan,et al. Handbook of Big Data , 2016 .
[30] M. J. van der Laan,et al. Practice of Epidemiology Improving Propensity Score Estimators ’ Robustness to Model Misspecification Using Super Learner , 2015 .
[31] Shenyang Guo,et al. Propensity Score Analysis: Statistical Methods and Applications , 2014 .
[32] J M Robins,et al. Identifiability, exchangeability, and epidemiological confounding. , 1986, International journal of epidemiology.
[33] Charles F. Manski,et al. Identification for Prediction and Decision , 2008 .
[34] J. Pearl. Causality: Models, Reasoning and Inference , 2000 .
[35] J. Lunceford,et al. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study , 2004, Statistics in medicine.
[36] Mark J van der Laan,et al. Collaborative targeted maximum likelihood estimation for variable importance measure: Illustration for functional outcome prediction in mild traumatic brain injuries , 2018, Statistical methods in medical research.
[37] Leo Breiman,et al. Stacked regressions , 2004, Machine Learning.
[38] Sherri Rose,et al. Implementation of G-computation on a simulated data set: demonstration of a causal inference technique. , 2011, American journal of epidemiology.
[39] D. Rubin,et al. The central role of the propensity score in observational studies for causal effects , 1983 .
[40] J. Sekhon,et al. Evaluating treatment effectiveness under model misspecification: A comparison of targeted maximum likelihood estimation with bias-corrected matching , 2014, Statistical methods in medical research.
[41] Kristin E. Porter,et al. The Relative Performance of Targeted Maximum Likelihood Estimators , 2011, The international journal of biostatistics.
[42] F. Hampel. The Influence Curve and Its Role in Robust Estimation , 1974 .
[43] M. J. Laan,et al. Targeted Learning: Causal Inference for Observational and Experimental Data , 2011 .
[44] Sherri Rose,et al. Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies , 2017, American journal of epidemiology.