Generalizability and Transportability of the National Lung Screening Trial Data: Extending Trial Results to Different Populations.
Generalizability and Transportability of the National Lung Screening Trial Data: Extending Trial Results to Different Populations.
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DOI：
10.1158/1055-9965.epi-21-0585
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发表时间：
2021-12
期刊：
影响因子：
--
通讯作者：
Bui AAT
中科院分区：
文献类型：
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作者：
Inoue K;Hsu W;Arah OA;Prosper AE;Aberle DR;Bui AAT
随机对照试验（RCT）在循证医疗保健中发挥着核心作用。然而，在临床实践中实施随机对照试验的临床和政策影响很难预测，因为研究人群通常与应用结果的目标人群不同。本研究阐明了普遍性和可移植性的概念，证明了它们在解释国家肺筛查试验（NLST）结果中的效用。通过使用逆优势加权，我们展示了如何使用普遍性和可转运性技术来推断治疗效果，从(i) NLST的一个子集到整个NLST人群，从（ii）整个NLST到不同的目标人群。我们的概括性分析显示，LDCT筛查在整个NLST中降低肺癌死亡率[16%(95%置信区间[CI]: 4-24)]，可以使用更小的NLST参与者子集来估计。通过可转运性分析，我们发现女性和当前吸烟者患病率较高的人群相比女性和当前吸烟者患病率较低的人群，LDCT筛查的肺癌死亡率降低更大[例如，80%女性和80%当前吸烟者的人群肺癌死亡率降低27% (95% CI, 11-37)]。本文说明了可通用性和可移植性方法如何将随机对照试验的效用估计扩展到试验参与者之外的外部人群，包括那些更接近真实世界人群的人群。可通用性和可移植性方法可用于量化感兴趣人群的治疗效果，可用于设计未来的试验或调整肺癌筛查资格标准。
Randomized controlled trials (RCT) play a central role in evidence-based healthcare. However, the clinical and policy implications of implementing RCTs in clinical practice are difficult to predict as the studied population is often different from the target population where results are being applied. This study illustrates the concepts of generalizability and transportability, demonstrating their utility in interpreting results from the National Lung Screening Trial (NLST). Using inverse-odds weighting, we demonstrate how generalizability and transportability techniques can be used to extrapolate treatment effect from (i) a subset of NLST to the entire NLST population and from (ii) the entire NLST to different target populations. Our generalizability analysis revealed that lung cancer mortality reduction by LDCT screening across the entire NLST [16% (95% confidence interval [CI]: 4–24)] could have been estimated using a smaller subset of NLST participants. Using transportability analysis, we showed that populations with a higher prevalence of females and current smokers had a greater reduction in lung cancer mortality with LDCT screening [e.g., 27% (95% CI, 11–37) for the population with 80% females and 80% current smokers] than those with lower prevalence of females and current smokers. This article illustrates how generalizability and transportability methods extend estimation of RCTs' utility beyond trial participants, to external populations of interest, including those that more closely mirror real-world populations. Generalizability and transportability approaches can be used to quantify treatment effects for populations of interest, which may be used to design future trials or adjust lung cancer screening eligibility criteria.