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      Targeted maximum likelihood estimation for a binary treatment: A tutorial


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          When estimating the average effect of a binary treatment (or exposure) on an outcome, methods that incorporate propensity scores, the G‐formula, or targeted maximum likelihood estimation (TMLE) are preferred over naïve regression approaches, which are biased under misspecification of a parametric outcome model. In contrast propensity score methods require the correct specification of an exposure model. Double‐robust methods only require correct specification of either the outcome or the exposure model. Targeted maximum likelihood estimation is a semiparametric double‐robust method that improves the chances of correct model specification by allowing for flexible estimation using (nonparametric) machine‐learning methods. It therefore requires weaker assumptions than its competitors. We provide a step‐by‐step guided implementation of TMLE and illustrate it in a realistic scenario based on cancer epidemiology where assumptions about correct model specification and positivity (ie, when a study participant had 0 probability of receiving the treatment) are nearly violated. This article provides a concise and reproducible educational introduction to TMLE for a binary outcome and exposure. The reader should gain sufficient understanding of TMLE from this introductory tutorial to be able to apply the method in practice. Extensive R‐code is provided in easy‐to‐read boxes throughout the article for replicability. Stata users will find a testing implementation of TMLE and additional material in the Appendix S1 and at the following GitHub repository: https://github.com/migariane/SIM-TMLE-tutorial

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          Most cited references 26

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          Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study.

          Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score, the probability of treatment exposure conditional on covariates, is the basis for two approaches to adjusting for confounding: methods based on stratification of observations by quantiles of estimated propensity scores and methods based on weighting observations by the inverse of estimated propensity scores. We review popular versions of these approaches and related methods offering improved precision, describe theoretical properties and highlight their implications for practice, and present extensive comparisons of performance that provide guidance for practical use. 2004 John Wiley & Sons, Ltd.
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              The changing prevalence of comorbidity across the age spectrum.

              The purpose of the research was to demonstrate that comorbid health conditions disproportionately affect elderly cancer patients. Descriptive analyses and stacked area charts were used to examine the prevalence and severity of comorbid ailments by age of 27,506 newly diagnosed patients treated at one of eight cancer centers between 1998 and 2003. Hypertension was the most common ailment in all patients, diabetes was the second most prevalent ailment in middle-aged patients, and previous solid tumor(s) were the second most prevalent ailment in patients aged 74 and older. Although the prevalence and severity of comorbid ailments including dementia and congestive heart failure increased with age, some comorbidities such as HIV/AIDS and obesity decreased. Advances in cancer interventions have increased survivorship, but the impact of the changing prevalence and severity of comorbidities at different ages has implications for targeted research into targeted clinical and psychosocial interventions.

                Author and article information

                Stat Med
                Stat Med
                Statistics in Medicine
                John Wiley and Sons Inc. (Hoboken )
                23 April 2018
                20 July 2018
                : 37
                : 16 ( doiID: 10.1002/sim.v37.16 )
                : 2530-2546
                [ 1 ] Cancer Survival Group, Department of Non‐Communicable Disease Epidemiology Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine London UK
                [ 2 ] Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA USA
                [ 3 ] Biomedical Research Institute of Granada, Non‐Communicable and Cancer Epidemiology Group (ibs.Granada) Andalusian School of Public Health Granada Spain
                [ 4 ] School of Public Health and Family Medicine, Center for Infectious Disease Epidemiology and Research The University of Cape Town Cape Town South Africa
                [ 5 ] Faculté de pharmacie Université de Montréal Montréal Canada
                Author notes
                [* ] Correspondence

                Miguel Angel Luque‐Fernandez, Cancer Survival Group, Department of Non‐Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, UK Keppel Street, London WC1E 7HT, UK.

                Email: miguel-angel.luque@ 123456lshtm.ac.uk

                SIM7628 SIM-17-0362.R2
                © 2018 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 2, Tables: 2, Pages: 17, Words: 5436
                Funded by: Canadian Institutes of Health Research
                Funded by: Carlos III Institute of Health
                Award ID: CP17/00206
                Funded by: Cancer Research UK
                Award ID: C7923/A18525
                Tutorial in Biostatistics
                Tutorial in Biostatistics
                Custom metadata
                20 July 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.4.3 mode:remove_FC converted:05.07.2018


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