We describe a computer program to assist a clinician with assessing the e\u000efficacy of treatments in experimental studies for which treatment assignment is random but subject compliance is imperfect. The major diffi\u000eculty in such studies is that treatment effi\u000ecacy is not “identi\fable”, that is, it cannot be estimated from the data, even when the number of subjects is in\fnite, unless additional knowledge is provided. Our system combines Bayesian learning with Gibbs sampling using two inputs: (1) the investigator’s prior probabilities of the relative sizes of subpopulations and (2) the observed data from the experiment. The system outputs a histogram depicting the posterior distribution of the average treatment eff\u000bect, that is, the probability that the average outcome (e.g.,survival) would attain a given level, had the treatment been taken uniformly by the entire population. This paper describes the theoretical basis for the proposed approach and presents experimental results on both simulated and real data, showing agreement with the theoretical asymptotic bounds.<\/p>\n","protected":false},"excerpt":{"rendered":"
We describe a computer program to assist a clinician with assessing the e\u000efficacy of treatments in experimental studies for which treatment assignment is random but subject compliance is imperfect. The major diffi\u000eculty in such studies is that treatment effi\u000ecacy is not “identi\fable”, that is, it cannot be estimated from the data, even when the number […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), \u00ae Portland, OR","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"1269-1276","msr_page_range_start":"1269","msr_page_range_end":"1276","msr_series":"","msr_volume":"2","msr_copyright":"","msr_conference_name":"Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96)","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"D.M. 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