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Early post\u2010baseline biomarker responses may serve as a supportive guide for physicians to decide whether to continue a treatment. We propose an exploratory two\u2010stage subgroup analysis method as a statistical tool to investigate the predictive value of such a short\u2010term response for the benefit of a new treatment in terms of long\u2010term outcomes. We use a flexible probability model, Bayesian additive regression trees (BART), to derive predictive conditional treatment effects (PCTE) based on a short\u2010term post\u2010baseline biomarker response using counterfactual modeling of responses to new and standard treatments for each patient. Constructing patient subgroups according to the PCTE, we analyze an observed long\u2010term outcome to identify a sensitive subpopulation. We carry out extensive simulation studies to examine the operating characteristics of the proposed method. For illustration, we apply the proposed method to data from a randomized clinical trial in patients with locally advanced or metastatic breast cancer. These findings support the predictive value of short\u2010term responses and provide a statistical approach for identifying patients expected to benefit from a new treatment in terms of a long\u2010term outcome.<\/jats:p>","DOI":"10.1002\/sim.70620","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T05:05:51Z","timestamp":1780031151000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring Sensitive Biomarkers Associated With Short\u2010Term Responses and Long\u2010Term Outcomes Using Bayesian Additive Regression Trees"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1070-0185","authenticated-orcid":false,"given":"Zixuan","family":"Yao","sequence":"first","affiliation":[{"name":"Department of Biomedical Statistics and Bioinformatics Kyoto University Graduate School of Medicine  Kyoto 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