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首页> 外文期刊>American Journal of Epidemiology >Data-Adaptive Estimation for Double-Robust Methods in Population-Based Cancer Epidemiology: Risk Differences for Lung Cancer Mortality by Emergency Presentation
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Data-Adaptive Estimation for Double-Robust Methods in Population-Based Cancer Epidemiology: Risk Differences for Lung Cancer Mortality by Emergency Presentation

机译：基于人群癌症流行病学中的双稳压方法的数据 - 自适应估算：急急介绍肺癌死亡率的风险差异

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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.
机译：本文提出了一种基于人群的癌症流行病学的结构框架，评价双重强度估计在癌症死亡率中的二元暴露的性能。我们进行数值分析，以研究这些估算器的偏差和效率。此外，我们基于1）Akaike的信息标准和贝叶斯信息标准和2）机器学习算法比较2不同的模型选择策略，并在真实世界中说明了双重强大估计器的性能。在模拟中，具有正确指定的模型和近乎阳性违规，但朴素的估计变得相对较好。然而，增强逆概率的处理加权估计器显示了最大的相对偏差。根据双模误操作和近乎阳性违规，所有双重强大估算器都偏见。然而，目标最大似然估计器显示了最佳的偏差折衷，更精确的估计，以及适当的95％置信区间覆盖，支持基于机器学习算法的数据 - 自适应模型选择策略的使用。我们将这些方法应用于估计183,426名肺癌患者的调整后的1年死亡率风险差异，该肺癌患者在入场后对急诊部门的患者（2006-2013）进行了无情部门的癌症诊断。调整后的死亡率风险（对于在入场后患有肺癌的患者）男性患者较高，女性较高16％，表明靶向肺癌症状和症状的早期检测的干预措施的重要性。

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