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The intermediate endpoint effect in logistic and probit regression

Department of Psychology, Arizona State University, Tempe, Arizona, USA, davidpm{at}asu.edu

C.M. Lockwood

Department of Psychology, Arizona State University, Tempe, Arizona, USA

C.H. Brown

Department of Epidemiology and Biostatistics, University of South Florida and Center for Health Statistics, University of Illinois at Chicago, USA

W. Wang

Department of Epidemiology and Biostatistics, University of South Florida, USA

J.M. Hoffman

Department of Rehabilitation Medicine, University of Washington, Seattle, Washington, USA

Background An intermediate endpoint is hypothesized to be in the middle of the causal sequence relating an independent variable to a dependent variable. The intermediate variable is also called a surrogate or mediating variable and the corresponding effect is called the mediated, surrogate endpoint, or intermediate endpoint effect. Clinical studies are often designed to change an intermediate or surrogate endpoint and through this intermediate change influence the ultimate endpoint. In many intermediate endpoint clinical studies the dependent variable is binary, and logistic or probit regression is used.

Purpose The purpose of this study is to describe a limitation of a widely used approach to assessing intermediate endpoint effects and to propose an alternative method, based on products of coefficients, that yields more accurate results.

Methods The intermediate endpoint model for a binary outcome is described for a true binary outcome and for a dichotomization of a latent continuous outcome. Plots of true values and a simulation study are used to evaluate the different methods.

Results Distorted estimates of the intermediate endpoint effect and incorrect conclusions can result from the application of widely used methods to assess the intermediate endpoint effect. The same problem occurs for the proportion of an effect explained by an intermediate endpoint, which has been suggested as a useful measure for identifying intermediate endpoints. A solution to this problem is given based on the relationship between latent variable modeling and logistic or probit regression.

Limitations More complicated intermediate variable models are not addressed in the study, although the methods described in the article can be extended to these more complicated models.

Conclusions Researchers are encouraged to use an intermediate endpoint method based on the product of regression coefficients. A common method based on difference in coefficient methods can lead to distorted conclusions regarding the intermediate effect. Clinical Trials 2007; 4: 499—513. http://ctj.sagepub.com

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