Robust variance estimation for rate ratio parameter estimates from individually matched case-control data

Abstract

The asymptotic variance and robust variance estimators of rate ratios estimated using conditional logistic regression from individually‐matched case‐control data are derived when the presumed proportional hazards model is misspecified. The robust variance estimators are easily computed using Schoenfeld residuals generated from standard partial likelihood estimation software for failure time data. Simulation studies indicate that the robust variance estimators perform well for typical sizes and that the ‘rare disease’ version should be adequate for all practical purposes. It was also found that model misspecification must be quite extreme before the model‐based, i.e. inverse information, variance is significantly biased and that the robust variance estimators are somewhat more variable than the model‐based. We conclude that the model‐based variance estimator can be used when model misspecification is not severe. The robust estimator should be used when the presumed model clearly fits the data poorly.