Joint Statistics / Biostatistics Seminar
Estimating mean response as a function of treatment duration in an observational study, where duration may be informatively censored
Brent Johnson, Ph.D. Candidate, North Carolina State University
Statistics / Biostatistics and Medical Informatics Assistant Professor Candidate
Wednesday, February 19, 2003, 4-5 p.m.
1221 Computer Science and Statistics Center, 1210 W. Dayton St.
After a treatment is found to be effective in a clinical study, attention often focuses on the effect of treatment duration on outcome. Such an analysis facilitates recommendations on the most beneficial treatment duration. In many studies, the treatment duration, within certain limits, is left to the discretion of the investigators. It is often the case that treatment must be terminated prematurely due to an adverse event, in which case a recommended treatment duration is part of a policy that treats patients for a specified length of time or until a treatment-censoring event occurs, whichever comes first. Evaluating mean response for a particular treatment duration policy from observational data is difficult due to censoring and the fact that it may not be reasonable to assume patients are prognostically similar across all treatment strategies. We propose an estimator for mean response as a function of treatment duration policy under these conditions. The method uses potential outcomes and embodies assumptions that allow consistent estimation of the mean response. The estimator is evaluated through simulation studies and demonstrated by application to the ESPRIT infusion trial coordinated at Duke University Medical Center. Extensions of this research to future research problems will also be discussed.
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