Sample Size Considerations in the Design of Orthopaedic Risk-factor Studies

TL;DR

This study highlights how misclassification of control groups in orthopaedic risk-factor studies reduces statistical power and provides correction factors to adjust sample size calculations accordingly.

Contribution

It demonstrates the impact of control group misclassification on study power and offers practical correction factors for sample size planning.

Findings

Misclassification reduces power proportionally to the rate.

Power can be restored by increasing sample size by 10-40%.

Adjustments improve study design accuracy.

Abstract

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.