Background
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.
Purpose
We present statistical methods for the design and analysis of
outcome-classification accuracy and for estimating error-corrected treatment
effects.
Methods
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.
Results
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.
Limitations
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.
Conclusions
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.
