Statistical uncertainty due to misclassification: Implications for validation substudies

The present paper shows that the alternative of a fully-validated design may provide more information per unit cost than a larger study coupled with a validation substudy.

Abstract

Certain studies incorporate a validation substudy as an integral part of their design. In the substudy, the results of a primary but error-prone measurement are compared with the results of a more accurate (but more difficult or costly) criterion measurement. The results of this substudy are then used to evaluate the impact of errors in the primary measurement on study validity. The present paper shows that the alternative of a fully-validated design (i.e. one that obtains criterion measurements on all subjects) may provide more information per unit cost than a larger study coupled with a validation substudy. Several formulas are provided to aid in detecting such a situation, and illustrated in the design of a case-control study of sudden infant death syndrome (SIDS).