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A method to assess the proportion of treatment effect explained by a surrogate endpoint
Author(s) -
Li Zhengqing,
Meredith Michael P.,
Hoseyni Mohammad S.
Publication year - 2001
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.984
Subject(s) - surrogate endpoint , clinical trial , logistic regression , computer science , statistics , econometrics , random effects model , medicine , mathematics , machine learning , meta analysis
Randomized clinical trials are the standard for evaluating new drugs, devices and procedures. Traditional clinical trials entail not only considerable expense, but require considerable time to complete. The use of surrogate endpoints constitutes an effort to control cost and completion time for clinical trials. We propose a method to quantify the proportion of treatment effect explained by a surrogate endpoint based on a general model setting which includes the commonly used linear, logistic and Cox regression models. The interpretation of this quantitative measure is facilitated by graphical displays. To reduce the variability associated with the estimate, a meta‐analytic approach is proposed based on random effects models. An example using real clinical trial data is given to illustrate the proposed procedures. Copyright © 2001 John Wiley & Sons, Ltd.