The case-crossover design is useful for studying the effects of transient exposures on short-term risk of diseases or injuries when only data on cases are available. The crossover nature of this design allows each subject to serve as his/her own control. While the original design was proposed for univariate event data, in many applications recurrent events are encountered (e.g. elderly falls, gout attacks, and sexually transmitted infections). In such situations, the within-subject dependence among recurrent events needs to be taken into account in the analysis. We review three existing conditional logistic regression (CLR)-based approaches for recurrent event data under the case-crossover design. A simple approach is to use only the first event for each subject; however, we would expect loss of efficiency in estimation. The other two reviewed approaches rely on independence assumptions for the recurrent events, conditionally on a set of covariates. Furthermore, we propose new methods that adjust the CLR using either a within-subject pairwise resampling technique or a weighted estimating equation. No specific dependency structure among recurrent events is needed therein, and hence, they have more flexibility than the existing methods in the situations with unknown correlation structures. We also propose a weighted Mantel-Haenszel estimator, which is easy to implement for data with a binary exposure. In simulation studies, we show that all discussed methods yield virtually unbiased estimates when the conditional independence assumption holds. These methods are illustrated using data from a study of the effect of medication changes on falls among the elderly.
- Case crossover
- Conditional logistic regression
- Recurrent events
- Weighted estimating equation
- Within-cluster resampling