Estimating heterogeneous survival treatment effects of lung cancer screening approaches: A causal machine learning analysis.
Estimating heterogeneous survival treatment effects of lung cancer screening approaches: A causal machine learning analysis.
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DOI：
10.1016/j.annepidem.2021.06.008
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发表时间：
2021-10
影响因子：
5.6
通讯作者：
Kale M
中科院分区：
文献类型：
--
作者：
Hu L;Lin JY (Joyce);Sigel K;Kale M
国家肺筛查试验（NLST）发现，与胸部X线摄影（CXR）相比，低剂量计算机断层扫描（LDCT）筛查可降低肺癌（LC）死亡率。相当多的研究涉及确定可能存在于某些亚群中的不同治疗效果。我们阐明了现有研究中的几个重要问题，并强调需要进一步研究LDCT与CXR的异质性比较效应，使用更灵活和严格的统计方法。我们使用了一种针对删失生存数据设计的高性能贝叶斯机器学习方法，加速失效时间贝叶斯加性回归树模型（AFT-BART），灵活地捕捉失效时间和预测因子之间的关系。然后，我们使用反事实框架来绘制每个参与者的个体治疗效果的马尔可夫链蒙特卡罗样本。使用这些后验样本，我们通过逐步二叉树方法探索了可能的治疗效果异质性。当用AFT-BART重新分析时，与CXR相比，LDCT没有统计学显著的LC或总体死亡率获益。亚洲和黑人（尤其是包年≥ 37岁且无肺气肿的患者）NLST人群显示LDCT的总体死亡率获益高于人群平均水平。尽管LC死亡率获益尚无定论，但有慢性阻塞性肺疾病史的亚洲人、黑人和白人显示出LDCT获益的小趋势。通过灵活的机器学习建模进行因果推理，可以为指导治疗决策和规划强调个性化医疗方法的有针对性的临床试验提供有价值的知识。
The National Lung Screening Trial (NLST) found that low-dose computed tomography (LDCT) screening provided lung cancer (LC) mortality benefit compared to chest radiography (CXR). Considerable research concerns identifying the differential treatment effects that may exist in certain subpopulations. We shed light on several important issues in existing research and highlight the need for further investigation of the heterogeneous comparative effect of LDCT versus CXR, using more flexible and rigorous statistical approaches. We used a high-performance Bayesian machine learning approach designed for censored survival data, accelerated failure time Bayesian additive regression trees model (AFT-BART), to flexibly capture the relationships between the failure time and predictors. We then used the counterfactual framework to draw Markov chain Monte Carlo samples of the individual treatment effect for each participant. Using these posterior samples, we explored the possible treatment effect heterogeneity via a stepwise binary tree approach. When re-analyzed with AFT-BART, LDCT did not have a statistically significant LC or overall mortality benefit compared to CXR. The Asian and Black (particularly those with pack-year ≥ 37 years and without emphysema) NLST population were shown to have enhanced overall mortality benefit from LDCT than the population average. Although inconclusive for LC mortality benefit, Asians, Blacks and Whites with history of chronic obstructive pulmonary disease showed a small trend towards benefit from LDCT. Causal inference with flexible machine learning modeling can provide valuable knowledge for informing treatment decision and planning targeted clinical trials emphasizing personalized medicine approaches.
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影响因子：
39.2
作者：
Basu S;Sussman JB;Hayward RA
通讯作者：
Hayward RA
影响因子：
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作者：
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作者：
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DOI：
10.1007/s00432-012-1228-9
发表时间：
2012-09-01
影响因子：
3.6
作者：
Becker, N.;Motsch, E.;Delorme, S.
通讯作者：
Delorme, S.