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General Departmental Seminar Series


Risk assessment via a robust probit model, with application to toxicology

Jason Fine, Department of Biostatistics & Medical Informatics,
University of Wisconsin

Wednesday, November 1, 2000, 4:00 pm

1221 Computer Sciences and Statistics Center
1210 W. Dayton St.

ABSTRACT

A number of frameworks may be used to assess the risk associated with a continuous toxicity outcome. For example, in a rat study of aconiazide, a drug under investigation for treatment of tuberculosis, animals receiving high doses tended to experience increased weight loss. The goal of the analysis is to identify a "safe'' dose. One approach is to formulate the effect of the exposure on the adverse effect with a simple normal model and to compute the risk function using tail probabilities from the standard normal distribution. This risk function depends heavily on the assumed model and may be sensitive to misspecification. A semiparametric alternative based on another definition of risk has recently been studied. However, it is not clear whether the two approaches are related. We explore a semiparametric normal model, in which an unknown transformation of the adverse response satisfies the linear model. It is demonstrated that this formulation unifies the two approaches, allowing for a coherent risk analysis of the dose-response data. The methodology includes estimation and inference for the unknown transformation in the semiparametric model for the continuous response. Novel model-checking techniques are proposed for diagnosing lack-of-fit, including a formal sup-norm test of the simple normal model. The aconiazide dataset serves as a case study for the risk assessment procedure.


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