Data-Adaptive Estimation for Double-Robust Methods in Population-Based Cancer Epidemiology: Risk Differences for Lung Cancer Mortality by Emergency Presentation

Abstract

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.

Association of medical male circumcision and sexually transmitted infections in a population-based study using targeted maximum likelihood estimation

“…TMLE, like other doubly-robust techniques, offers an opportunity to rely on nonparametric methods (like machine learning) in its estimation process, thereby increasing efficiency [15]. Previous theoretical and simulation studies have shown that TMLE has greater efficiency and less bias when compared with misspecified parametric and nonparametric singly robust methods [14,36]. This was also evident from the result of our TMLE estimates and confidence intervals in this study.…”

Impact of androgen deprivation therapy on mortality of prostate cancer patients with COVID-19: a propensity score-based analysis

“…In this model, we used all variables for the propensity score estimation, but only variables selected in the first logistic regression with backward elimination (performed before the first propensity score) were selected for outcome-based statistics. This model was assessed with a Wald test [24][25][26].…”

Comparison of Parametric and Nonparametric Estimators for the Association Between Incident Prepregnancy Obesity and Stillbirth in a Population-Based Cohort Study

“…In doing so, researchers may avoid relying on unwarranted assumptions about how the exposure and confounders relate to the exposure or the outcome (46). Numerous theoretical and simulation studies have shown that nonparametric TMLE has greater efficiency and less bias when compared with (misspecified) parametric singly robust methods (11) and nonparametric singly robust methods (46,47).…”