Luque-Fernandez, Miguel AngelBelot, AurélienValeri, LindaCerulli, GiovanniMaringe, CamilleRachet, Bernard2018-05-302018Luque-Fernandez, Miguel Angel, Aurélien Belot, Linda Valeri, Giovanni Cerulli, Camille Maringe, and Bernard Rachet. 2018. “Data-Adaptive Estimation for Double-Robust Methods in Population-Based Cancer Epidemiology: Risk Differences for Lung Cancer Mortality by Emergency Presentation.” American Journal of Epidemiology 187 (4): 871-878. doi:10.1093/aje/kwx317. http://dx.doi.org/10.1093/aje/kwx317.http://nrs.harvard.edu/urn-3:HUL.InstRepos:37067983Abstract In this paper, we propose a structural framework for population-based cancer epidemiology and evaluate the performance of double-robust estimators for a binary exposure in cancer mortality. We conduct numerical analyses to study the bias and efficiency of these estimators. Furthermore, we compare 2 different model selection strategies based on 1) Akaike’s Information Criterion and the Bayesian Information Criterion and 2) machine learning algorithms, and we illustrate double-robust estimators’ performance in a real-world setting. In simulations with correctly specified models and near-positivity violations, all but the naive estimators had relatively good performance. However, the augmented inverse-probability-of-treatment weighting estimator showed the largest relative bias. Under dual model misspecification and near-positivity violations, all double-robust estimators were biased. Nevertheless, the targeted maximum likelihood estimator showed the best bias-variance trade-off, more precise estimates, and appropriate 95% confidence interval coverage, supporting the use of the data-adaptive model selection strategies based on machine learning algorithms. We applied these methods to estimate adjusted 1-year mortality risk differences in 183,426 lung cancer patients diagnosed after admittance to an emergency department versus persons with a nonemergency cancer diagnosis in England (2006–2013). The adjusted mortality risk (for patients diagnosed with lung cancer after admittance to an emergency department) was 16% higher in men and 18% higher in women, suggesting the importance of interventions targeting early detection of lung cancer signs and symptoms.en-UScancer epidemiologycausalitymachine learningpopulation-based datastatisticstargeted maximum likelihood estimationJournal Article2018-05-3010.1093/aje/kwx317