The intermediate endpoint effect in logistic and probit regression.
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.


OBJECTIVE
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.


CONCLUSIONS
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.
MacKinnon
D P
DP
Department of Psychology, Arizona State University, Tempe, Arizona 85287-1104, USA. davidpm@asu.edu
Lockwood
C M
CM
Brown
C H
CH
Wang
W
W
Hoffman
J M
JM
D015415:Biomarkers; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D014481:United States
D015415:Biomarkers