Power analysis for multivariable Cox regression models.

Area of Science:

• Biostatistics
• Statistical modeling
• Clinical trial design

Background:

• Power analysis in multivariable Cox regression models typically uses variance approximations based on the null information matrix.
• This approximation's accuracy is questionable when true hazard ratios deviate significantly from unity.

Purpose of the Study:

• To evaluate alternative variance expressions for power calculations in multivariable Cox models.
• To compare the performance of these alternatives against the traditional null variance approximation.

Main Methods:

• Analytical exploration of alternative variance expressions.
• Simulation studies to assess performance under various scenarios.
• Comparison of accuracy in estimating the variance of the log-hazard ratio.

Main Results:

• The traditional null variance approximation can be highly inaccurate, under- or overestimating true variance, particularly with imbalanced group sizes and non-unity hazard ratios.
• Alternative variance expressions demonstrate superior theoretical properties and improved accuracy in simulations.
• A simple, accurate alternative involves the sum of inverse expected event counts scaled by a variance inflation factor.

Conclusions:

• Alternative variance expressions offer more reliable power calculations for multivariable Cox models than the traditional null variance method.
• These improved methods are crucial for realistic scenarios in clinical trial design and biostatistical analysis.
• The recommended alternative provides a practical and accurate approach for power estimation.