Statistical analysis and design for estimating accuracy in clinical-center classification of cause-specific clinical events in clinical trials

When cause-specific clinical events (for instance, hospitalization for cardiac disease) are used as the primary or key secondary outcomes, it is important to assess how accurately these events have been classified and estimate the error-corrected treatment effects to ensure validity. However, it may not be feasible to verify cause classification for every event in large clinical trials.

We present statistical methods for the design and analysis of outcome-classification accuracy and for estimating error-corrected treatment effects.

Using the Hemodialysis (HEMO) Study, in which primary causes were designated for all 7822 hospitalizations by the 15 participating clinical centers - but only two subsets were audited by the Outcome Committee - we applied existing methods to obtain unbiased estimates of the sensitivity and specificity of clinical-center classifications. The multiple imputation method was used to correct for the misclassification of events. We then examined how trial results were affected by three methods of event classification: unaudited, imputed, and adjudicated.

We applied a three-step procedure to extend the results for the two subsets of audited events to estimate the sensitivity and specificity for the complete set. Finite population sample size formulas were developed for designing the quality control sample. Based on the HEMO analysis, the estimate of the intervention effect using the unaudited outcome was biased; the bias was reduced using the outcome corrected by imputation.

The methods are limited to situations in which there are clinical-center classifications for all clinical events but only partial availability of reference standard classifications, and the verification process does not depend on the true event cause or other unobserved information.

Designing a quality control study to estimate the accuracy of outcome classification is important. The multiple imputation method can be used to correct for errors in outcome classification and to estimate the error-corrected treatment effect. Trial results need to be reexamined using the error-corrected outcome.