STATISTICAL VALIDATION OF INTERMEDIATE END-POINTS FOR CHRONIC DISEASES

We discuss the implementation of a criterion due to Prentice for the statistical validation of intermediate endpoints for chronic disease. The criterion involves examining in a cohort or intervention study whether an exposure or intervention effect, adjusted for the intermediate endpoint, is reduced to zero. For example, to examine whether serum cholesterol level is an intermediate endpoint for coronary heart disease (CHD), we may investigate the effect of the cholesterol lowering drug cholestyramine on CHD incidence adjusted for serum cholesterol levels. We show that use of this criterion will usually demand some form of model selection. When the unadjusted exposure or treatment effect is less than four times its standard error, the analysis can usually lead only to a weak form of validation, a conclusion that the data are not inconsistent with the validation criterion. More significant unadjusted exposure effects offer the potential for stronger types of validation statement such as 'the intermediate endpoint explains at least 50 per cent (or 75 per cent) of the exposure effect'.