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Estimating the Complier-Average Causal Effect for Treatment in the Presence of Noncompliance
We consider the analysis of trials that involve randomization to an active treatment (T = 1) or a control treatment (T = 0), when the active treatment is subject to all-or-nothing compliance. We compare three approaches to estimating treatment efficacy in this situation: as-treated analysis, per-protocol analysis, and instrumental variable (IV) estimation, where the treatment effect is estimated using the randomization indicator as an instrumental variable. The assumptions underlying these estimators are assessed, standard errors and mean squared errors of the estimates are compared, and design implications of the three methods are examined. Extensions of the methods to include observed covariates are then discussed, emphasizing the role of compliance propensity methods and the contrasting role of covariates in these extensions. The goal is to present important concepts of causal inference in a relatively nontechnical way.
2009-06-18 00:00
2009-06-18 12:00
2009-06-18 13:30
Roderick J. Little, PhD
Roderick Little is Richard D. Remington Collegiate Professor and Chair of the Department of Biostatistics. He also holds professorial appointments in the Department of Statistics and the Institute for Social Research. He directs the Biostatistics Core of CHCR, and has broad collaborative research experience in medicine, demography, economics, psychiatry, aging and the environment. His statistical research interests include analysis of data with missing values, sample survey inference and causal inference
7C09 North Ingalls
http://wocket.chcr.med.umich.edu/chcr/seminars/2009-06-18-little.htm
2009-06-18-Little.pdf
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