title: Case-crossover and case-time-control designs as alternatives in pharmacoepidemiologic research.
abstract: Standard cohort and case-control designs are suited to the study of cumulative effects of chronic exposures, but they are prone to confounding by indication. Case-crossover and case-time-control studies are especially useful for studying intermittent exposures with transient effects, and are less susceptible to confounding by indication. Each design has its strengths and weaknesses. Despite the increasing availability of automated databases, cohort studies are usually time consuming and expensive, and therefore not preferred for time-critical decisions. In case-control studies, the selection of appropriate controls can be difficult and time consuming, and sometimes impractical when the exposure is rare. Case-crossover studies use the exposure history of each case as his or her own control to examine the effect of transient exposures on acute events. It further allows to study the time relationship of immediate effects to the exposure. This design eliminates between-person confounding by constant characteristics, including chronic indications. Because exposure data for the case and control periods are provided by the same person, the problems of differential recall may be reduced in many but not all case-crossover studies. Bias can result from temporal changes in prescribing or within-person confounding, including transient indication or changes in disease severity. The case-time-control design is an elaboration of the case-crossover design, which uses exposure history data from a traditional control group to estimate and adjust for the bias from temporal changes in prescribing. This paper will present a structured decision table of when to use which design in pharmacoepidemiologic research. In conclusion, case-crossover and case-time-control studies are the designs of choice when separating acute effects from chronic effects of transient exposures and if confounding by indication is an outstanding problem.
authors.affiliation: Department of Medical Informatics, Biometry and Epidemiology (IBE), Pharmacoepidemiology Group, University of Munich, Germany. schnee@ibe.med.uni-muenchen.de
authors.forename: S
authors.initials: S
authors.lastname: Schneeweiss
authors.forename: T
authors.initials: T
authors.lastname: Stürmer
authors.forename: M
authors.initials: M
authors.lastname: Maclure