American Statistical Association
Randomized controlled trials (RCTs) are the traditional gold standard evidence for causal conclusions. However, RCTs are rarely designed to provide conclusive evidence for rare events or broad populations. For rare events, we might combine several RCTs, with the hope of increasing the power to detect a statistically significant effect size. Even when appropriate meta-analytical techniques are applied to this situation, we must be concerned about the relevance of the data and models to the question at hand. For example, RCT protocols that limit enrollment eligibility introduce selection error that severely limits a RCT's applicability to a wide range of individuals. Conversely, high quality observational data can be representative of entire populations, but freedom to choose treatment can bias estimators based on this data. Observational methods may be useful in quantifying the size of possible bias due to recruitment protocols, and I will present several proposed methods for doing so. Further, I will present a simple cross-design estimator of effect size that capitalizes on RCTs' strong internal validity and observational studies' strong external validity to create a less biased estimate for the population average effect.
Dr. Kaizar has been Assistant Professor of Statistics at The Ohio State University since she graduated from Carnegie Mellon University in 2006. Her primary research focus is on assessing the efficacy and safety of medical interventions, especially those that are rare or heterogeneous across populations. As such, she has worked on methodology to combine multiple sources of information relevant to the same overarching application question. These data sources include randomized trials, administrative data, and sample surveys. She has more recently worked on methods for multiple testing for categorical data.
|Date:||Tuesday, March 22, 2011|
|Time:||3:30 - 4:30 P.M.|
New York State Psychiatric Institute
1051 Riverside Drive
6th Floor Multipurpose Room (6602)
New York, New York