Confidence intervals for measures of interaction

Abstract

Interaction, defined as departure of disease rates from an additive model, can be measured by the relative excess risk due to interaction, or the attributable proportion due to interaction. Point estimates can be obtained using multiple logistic regression. Using simulated case-control data, we compare several confidence interval estimation techniques for these measures. These include a symmetrical interval based on the delta method estimate of the variance, and three types of bootstrap confidence intervals. One such bootstrap method has coverage closest to the nominal level and is the most evenly balanced with respect to the direction in which intervals miss the true value. The estimation methods are applied to data from an actual case-control study, and the results are interpreted in light of the simulation study.