App Stats: Pattanayak on "A Potential Outcomes, and Typically More Powerful, Alternative to 'Cochran-Mantel-Haenszel'" 

 We hope you can join us this Wednesday, November 14, 2012 for the  Applied Statistics Workshop .  Cassandra Wolos Pattanayak , a College Fellow from the  Department of Statistics  at Harvard University, will give a presentation entitled "A Potential Outcomes, and Typically More Powerful, Alternative to 'Cochran-Mantel-Haenszel'". A light lunch will be served at 12 pm and the talk will begin at 12.15. 

 "A Potential Outcomes, and Typically More Powerful, Alternative to 'Cochran-Mantel-Haenszel'" 
Cassandra Wolos Pattanayak 
Statistics Department, Harvard University 
CGIS K354 (1737 Cambridge St.)  
Wednesday, November 14th, 2012 12.00 pm 

 Abstract: 
 In studies of public health, outcome measures such as the odds ratio, rate ratio, or efficacy are often estimated across strata to assess the overall effect of active treatment versus control treatment. Patients may be partitioned into such strata or blocks by experimental design, or, in non-randomized studies, patients may be partitioned into subclasses based on key covariates or estimated propensity scores to improve observed covariate balance across treatment groups. In finite samples, there exist tests and intervals for these estimands that can be more powerful than tests and intervals created with Cochran-Mantel-Haenszel or analogous procedures . The proposed methods multiply impute missing potential outcomes within the Rubin Causal Model so that estimands can be directly estimated. The assumptions underlying these typically more powerful methods are appropriate in many circumstances, especially when the strata are based on covariates highly predictive of treatment decisions and outcomes. When used to draw inferences about a population from which the patients in the study are considered a random sample, and the sample is large, these methods are extremely similar to the classical methods. The proposed approach is particularly relevant when assessing the safety of a new treatment relative to a standard one because, under typical conditions, the tests are more powerful and the intervals are shorter, thereby detecting smaller differences.