Paul Meier died a week ago Sunday (Aug. 7); he was 87.
Meir formulated (with Edward Kaplan) the Kaplan-Meier survival-curve as a way of measuring and comparing patient survival. In the 1950's, Meier was instrumental in convincing the FDA to require randomized controlled trials in the drug approval process. Previously, new treatments were given to all study subjects, and results compared to previous practice, or to untreated patients.
Coming up at statistics.com are two courses in these areas:
Sep 16: Avoiding Selection Bias in Randomized Clinical Trials (5 weeks)
Sep 23: Survival Analysis (4 weeks)
(1) “Avoiding Selection Bias in Randomized Clinical Trials” covers the essential concepts required to design randomized trials so as to ensure valid treatment comparisons. The nature and objectives of randomization are discussed, as are masking, allocation concealment, blocking, stratification, dynamic randomization, and the various types of selection bias that can arise. In addition, the course covers analysis techniques that can be used to salvage reliable treatment comparisons even if some of these selection biases are detected. These methods are more advanced, and involve adaptations of the propensity score.
Dr. Berger, the author of “Selection Bias and Covariate Imbalances in Randomized Clinical Trials,” serves on the adjunct faculty of the University of Maryland Baltimore County, has taught at Rutgers and Johns Hopkins, and has served as an FDA reviewer for over four years.
(2) “Survival Analysis” (David Kleinbaum and Matthew Strickland) describes the various methods used for modeling and evaluating survival data, also called time-to-event data. Survival models are used in a variety of health and social sciences, including biostatistics, epidemiology, anthropology, sociology, psychology, economics, and engineering (where it is called "time-to-failure" analysis). Topics include survival and hazard functions, Kaplan-Meier graphs, log-rank and related tests, Cox proportional hazards model, and the extended Cox model for time-varying covariates.
Dr. Kleinbaum is the author of “Survival Analysis – A Self Learning Text” (the course text); Dr. Strickland is Assistant Professor in the Department of Environmental and Occupational Health at Emory University. They have taught the survival analysis course at statistics.com since 2006.
Details:
http://www.statistics.com/clinbias
http://www.statistics.com/survival
As with all online courses at statistics.com, there are no set hours when you must be online, and you can interact with the instructor throughout the course period via a private discussion board. We estimate you will need about 15 hours per week.
Peter Bruce
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