  Sample size calculations play a central role in study design because sample
size affects study interpretability, costs, hospital resources, and staff time.
For most veterinary orthopaedic risk-factor studies, either the sample size
calculation or the post-hoc power calculation assumes the disease status of
control subjects is perfectly ascertained, when it may not be. That means
control groups may be mixtures of both unaffected cases and some unidentified
affected cases. In this study, we demonstrate the consequences of using
misclassified groups as control groups on the power of risk association tests,
with the intent of showing that control groups with even small
misclassification rates can reduce the power of association tests. In addition,
we offer a range of correction factors to adjust sample size calculations back
to 80% power. This was a simulation study using study designs from published
orthopaedic risk-factor studies. The approach was to use their designs but
simulate the data to include known proportions of misclassified affected
subjects in the control group. The simulated data was used to calculate the
power of a risk-association test. We calculated powers for several study
designs and misclassification rates and compared them to a reference model.
Treating misclassified data as disease-negative only always reduced statistical
power compared to the reference power, and power loss increased with increasing
misclassification rate. For this study, power could be improved back to 80% by
increasing the sample size by a factor of 1.1 to 1.4. Researchers should use
caution in calculating sample sizes for risk-factor studies and consider
adjustments for estimated misclassification rates.
