Case-Crossover Designs Compared With Dynamic Follow-up Designs

Abstract

In the late 1980s, when we started developing the case crossover design, confusion about the relation between case-control studies and cohort or follow-up studies was still common. Some epidemiologists were still teaching students that a case-control study is like a backwards cohort study the "trohoc" fallacy.' The turning point was the realization that most case control studies are (or ought to be) the same as dynamic population follow-up studies, except for a key difference: controls are a small sample rather than a complete census of the study base-the population-time that produced the cases.2 This realization played a role in our formulation of the case-crossover design, as we puzzled over how to identify triggers of myocardial infarction (MI) onset by retrospective interviews with patients.3 Here the underlying dynamic fol low-up study was like a crossover experiment, except that the time at which subjects crossed back and forth between expo sure and nonexposure was not controlled by an experimenter but influenced by natural events or subjects' personal decisions. The impetus for using cases as their own controls was our finding that all other control groups are too vulnerable to selection or information bias.4 By interviewing patients about the day of their heart attack (the case day) as well as the day before (the matching control day), we could rule out con founding by constant characteristics of patients. Also the subjects' interpretations of our questions about exposures would be the same for both days. Initially, we saw little reason why case-crossover de signs might be useful in absence of those biases, such as in the context of database studies of the health effects of air pollution. Such studies typically capture all deaths or hospi talizations in a population, minimizing selection bias. Air pollution data are comprehensive and collected independently from the health outcome data, so differential information bias is negligible. However, it rapidly became apparent that case-cross over studies do offer an additional research strategy for air pollution researchers, especially when data are collected from individual subjects. Even with no additional data besides what are already available in health and air pollution data bases, a case-crossover analysis can complement time-series analyses. By redefining the time scale so "time zero" is the onset of the outcome, a case-crossover analysis enables the investigator to directly conceptualize the causal relation, and may thereby reveal associations as well as problems that were initially overlooked in time-series analyses. For example, long-term, seasonal and weekly patterns of traffic density, air pollution and timing of disease onset make it necessary to control for time trends in exposure. In a case-crossover analysis, this can be achieved by design, ie, by selecting control intervals on the same day of week and from the calendar month as each case interval, and ignoring exposure data from other times. This controls confounding by time trends to the extent that the dimensions of the strata capture the temporal variation. In a time-series analysis, the same can be achieved by modeling, ie, by keeping the exposure data from all other times in the follow-up period and adding terms to a multivariate model to control for such time trends. If the model specification happens to be right, then using exposure data from other times preserves statistical information in the analysis and improves statistical precision. However, model ing can add misinformation if the modeling assumptions happen to be wrongeg, uncontrolled confounding can re sult from incorrectly specifying the shape of the longand short-term time trends. Some people understand and trust analyses more when confounding is controlled by design. Simple crude analyses from case-crossover studies-espe cially raw counts of events in case and control periods-are easy to understand, despite their involving control for many confounders by self-matching. Does a case-crossover analysis actually uncover new information, or does it just help investigators to perceive it? First, we must understand the nature of this question by contrasting a case-crossover study with a traditional case control study. The latter asks "Why them?" (Why did these people become cases whereas those people did not?). A Submitted 6 November 2007; accepted 8 November 2007. From the *Pharmaceutical Services Division, British Columbia Ministry of Health, Victoria, BC, Canada; tEpidemiology Department, Harvard School of Public Health, Boston, MA; tSchool of Health Information Science, University of Victoria, Victoria, BC; and ?Cardiovascular Epidemiology Research Unit, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA. Correspondence: Malcolm Maclure, University of Victoria, 1774 Armstrong Street, Victoria BC V8R 5S6 Canada. E-mail: malcolmmaclure@shaw.ca. Copyright C) 2008 by Lippincott Williams & Wilkins ISSN: 1044-3983/08/1902-0176 DOI: 10.1097/EDE.Ob013e318162afb9