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首页> 美国卫生研究院文献>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：基于人群的癌症流行病学中双稳健方法的数据自适应估计：紧急情况对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：在本文中，我们提出了基于人群的癌症流行病学的结构框架，并评估了在癌症死亡率中二元暴露的双重稳健估计量的性能。我们进行数值分析以研究这些估计量的偏差和效率。此外，我们基于1）Akaike的信息准则和贝叶斯信息准则以及2）机器学习算法，比较了两种不同的模型选择策略，并说明了在现实环境中双稳健估计器的性能。在具有正确指定的模型和接近正定性的模拟中，除幼稚的估计量外，所有模型均具有相对较好的性能。但是，增强的治疗逆概率加权估计值显示出最大的相对偏差。在双重模型错误指定和接近阳性的情况下，所有双重稳健估计量均存在偏差。尽管如此，目标最大似然估计器仍显示出最佳的偏差方差权衡，更精确的估计以及适当的95％置信区间覆盖率，从而支持使用基于机器学习算法的数据自适应模型选择策略。我们使用这些方法来估计183,426例入院急诊科后诊断为肺癌的非肺癌患者与未诊断为癌症的患者（2006-2013年）的校正后1年死亡率风险差异。调整后的死亡率风险（对于进入急诊室后被诊断出患有肺癌的患者），男性高16％，女性高18％，表明针对早期发现肺癌体征和症状进行干预的重要性。

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