JSM 2005 - Toronto

Abstract #302777

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 502
Type: Invited
Date/Time: Thursday, August 11, 2005 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract - #302777
Title: Semiparametric Bivariate Linear Regression Model for Estimating the Effect of Treatment on Time to Disease Progression and Death
Author(s): Daniel Scharfstein*+
Companies: Johns Hopkins University
Address: Department of Biostatistics, Baltimore, MD, 21205-2103,
Keywords:
Abstract:

In this talk, we will consider estimation of the effect of a randomized treatment on time to disease progression and death after adjusting for potentially high-dimensional baseline prognostic factors. We assume patients may or may not have disease progression prior to death, and those who have disease progression are followed for their survival information. Disease progression and survival also may be subject to additional censoring (e.g., loss to followup or study termination). We posit a semiparametric bivariate linear regression model with unspecified error distribution and show how to construct estimators of the regression parameters. The causal interpretation of the parameters depends on no-identifiable assumptions. We discuss two assumptions: the first applies to situations where it is reasonable to view disease progression as well defined after death and the second applies to situations where such a view is unreasonable. We conduct a simulation study and analyze data from a randomized trial for the treatment of brain cancer.

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Revised March 2005