A comparison of several procedures to estimate the confidence interval forattributable risk in case-control studies

The estimation of a confidence interval for attributable risk from the logi stic model based on data from case-control studies is a problem for which a n accepted solution is lacking. Two methods, one based on the delta method and one bootstrap on the population base, have been described but their acc uracy has not been compared. We present two other methods, one based on a j ack-knife approach and the other using a bootstrap on two samples (cases an d controls). The four methods are compared in a simulation study. The four methods are also applied to a case-control study on risk factors for preter m delivery; the confidence intervals are obtained assuming normality and by logarithmic transformation. When attributable risk is not smooth (for exam ple, when exposure prevalence is low) both the jack-knife and the delta met hod tend to fail. If attributable risk is close to zero or one, normality c annot be assumed and log-transformed confidence intervals must be used. Fin ally, the extension to matched studies is analysed using a case-control stu dy on risk factors of cutaneous malignant melanoma. In this situation, the population-based bootstrap is not available. Copyright (C) 2000 John Wiley & Sons, Ltd.