American Statistical Association
In many medical studies, event times are recorded in an interval-censored (IC) format. For example, in numerous cancer trials, time to disease relapse is only known to have occurred between two consecutive clinic visits. Many of the existing modeling methods in the IC context are computationally intensive and usually require numerous assumptions that could be unrealistic or difficult to verify in practice. We propose a novel, flexible and computationally efficient modeling strategy based on pseudo-observations (POs) obtained using the leave-one-out jackknife. The POs obtained based on a nonparametric estimator of the survival function are employed as outcomes in an equivalent, yet simpler regression model that produces consistent covariate effect estimates. Hence, instead of operating in the IC context, the problem is translated into the realm of generalized linear models, where numerous options are available. Outcome transformations via appropriate link functions lead to familiar modeling contexts such as the proportional hazards, proportional odds or accelerated failure time models. Moreover, the methods developed are not limited to these settings and have broader applicability. Simulations studies show that the proposed methods produce virtually unbiased covariate effect estimates, even for moderate sample sizes.
An example from the International Breast Cancer Study Group Trial VI further illustrates the practical advantages of this new approach.
Adin-Cristian Andrei received his Ph.D. from the University of Michigan in 2005. His research interests are in nonparametric and semiparametric inference in survival analysis. Recently he has been working on pseudo-observations-based regression models in difficult incomplete data settings, such as interval-censored, current status, doubly-censored and recurrent event data. At the University of Wisconsin, he collaborates with breast and prostate cancer researchers and the Department of Anesthesiology.
|Date:||Thursday, October 1, 2009|
|Time:||4:00 - 5:00 P.M.|
Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Computer Lab
6th Floor - Room 656
New York, New York