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Harvard School of Public Health

 

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Summer 2014
Jun 25, 2017
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RDS 286 - Decision Analy in Clin Resch
Course Restrictions:
A) Intended for physicians, other health professionals with advanced degrees, and others with significant clinical research experience.
B) Instructor's permissions required, with the following exceptions:
  1. Current or former participants in the Program in Clinical Effectiveness
  2. Degree candidates in the Departments of Epidemiology or Biostatistics, or the MPH in Quantitative Methods or Clinical Effectiveness, and who have an MD degree.

Course Prerequisites:
BIO202 & BIO203 or BIO206 & 208 (which may be taken concurrently), or BIO201


Decision Analysis in Clinical Research (RDS286) introduces the following topics: decision analysis methods relevant to clinical decision making, clinical research and comparative effectiveness research; the use of probability to express uncertainty; Bayes theorem and evaluation of diagnostic test strategies; sensitivity analysis; utility theory and its use to express patient preferences for health outcomes; cost-effectiveness analysis in clinical research and health policy; and use, limits and ethical issues of decision analysis and cost-effectiveness in clinical decision making and research design.

Requires knowledge of clinical medicine, though training and/or clinical research experience. Strong quantitative ability/aptitude is also required. Priority for enrollment will be given to students in the Program for Clinical Effectiveness (PCE). HSPH degree candidates who are not in PCE must demonstrate knowledge of clinical medicine, though training and/or clinical research experience. (Others should consider taking RDS 280 as an alternative.) Non-degree students must provide evidence of both clinical training/ research experience and mathematical ability (e.g., grades in quantitative courses taken, test scores).

2.500 Credit hours
2.500 Lecture hours

Levels: Graduate - Non-Degree, Graduate
Schedule Types: Lecture

Health Policy & Management Department

Course Attributes:
Health Policy & Management, Risk & Decision Sciences


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