Summary
Objectives:
Typically, methods for the estimation of differences in proportions from clustered
data are based on complete cases with no missing information [1, 2]. In this paper
we propose an extension to the method of Rao and Scott [3] and Obuchowski [2] to allow
for the explicit computation of the variance of the estimator for the difference in
presence of incomplete cases.
Methods:
We divided the full analysis set into a set of complete cases and a set of incomplete
cases. The differences in proportions of correct diagnoses were estimated for each
set by taking into consideration the clustering effect for both sets and the correlation
between the procedures in the set with complete cases. Then the estimates of the two
parts were combined by appropriate weights, which then allowed the explicit calculation
of the variance. The performance of the extension as compared to the original method
and generalized estimation equations model (GEEs) was examined by simulations.
Results:
The results of the examples suggest that the extended approach is superior to the
complete-case method and is therefore appropriate when all data are to be used. In
comparison to GEEs, the extended method appears to be slightly inferior, when the
number of observations per patient is high, but of similar efficiency with a low number
of observations per patient.
Conclusions:
With the extension of the method by Rao and Scott [3] and Obuchowski [2] we make
use of all available data. Therefore, we follow the intent-to-treat principle as close
as possible.
Keywords
Clustered data - matched-pair - missing data - binary outcomes
