Estimating heterogeneous survival treatment effects of lung cancer screening approaches: A causal machine learning analysis.

ABSTRACT: 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

SUBMITTER: Hu L 

PROVIDER: S-EPMC8463451 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature