{"69143":{"#nid":"69143","#data":{"type":"event","title":"Optimal Design of Prostate Cancer Screening Policies","body":[{"value":"\u003Cp\u003ETITLE: Optimal Design of Prostate Cancer Screening\nPolicies\u003C\/p\u003E\u003Cp\u003ESPEAKER: Brian Denton\u003C\/p\u003E\u003Cp\u003EABSTRACT:\u003C\/p\u003E\u003Cp\u003EProstate\ncancer is the most common solid tumor that affects American men. Screening\ntypically involves the use of prostate specific antigen (PSA) tests. However,\nthe imperfect nature of PSA tests, and the potential for subsequent harm from\nunnecessary biopsies and treatment, has raised debate about whether and when to\nscreen. In this talk I will provide some background on prostate cancer, current\nscreening guidelines, and a summary of the recent controversy over PSA testing.\nNext, I will discuss a partially observable Markov decision process (POMDP)\nmodel to investigate the optimal design of screening policies. Screening policies are defined by the patient\u2019s probability of having\nprostate cancer which is estimated from their history of PSA tests results\nusing Bayesian updating. The core states are the patients\u2019 prostate cancer\nrelated health states. Transition probabilities among health states are\nestimated using a large longitudinal dataset from Olmsted County, the Mayo\nClinic Radical Prostatectomy Registry (MCRPR) and the medical literature.\u0026nbsp; Reward functions that are considered include\nquality adjusted survival (patient perspective) and costs (third party payer\nperspective). \u003C\/p\u003E\n\n\n\n\u003Cp\u003ESome\ntheoretical properties that define the optimal policy will be discussed, and a\nnew approximation method suited to solving finite horizon non-stationary POMDPs\nwill be presented. \u0026nbsp;The\nresults of computational experiments will be used to\nillustrate the use of the model for making screening decisions, such as if and\nwhen to recommend a patient for a PSA test, and when to refer patients for\nbiopsy and subsequent treatment.\u0026nbsp; Sensitivity\nanalysis will be presented to demonstrate the relative importance of factors\nthat define patient specific preferences and risk factors. Finally, future\nresearch directions in the area will be discussed. \u0026nbsp;\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Optimal Design of Prostate Cancer Screening Policies"}],"uid":"27187","created_gmt":"2011-08-03 07:54:14","changed_gmt":"2016-10-08 01:55:18","author":"Anita Race","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2012-02-16T10:00:00-05:00","event_time_end":"2012-02-16T12:00:00-05:00","event_time_end_last":"2012-02-16T12:00:00-05:00","gmt_time_start":"2012-02-16 15:00:00","gmt_time_end":"2012-02-16 17:00:00","gmt_time_end_last":"2012-02-16 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[{"id":"166896","name":"seminar"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}