Donald A Redelmeier1. 1. Department of Medicine, University of Toronto, Toronto, Ontario, Canada M4N 3M5; Evaluative Clinical Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada. dar@ices.on.ca
Abstract
OBJECTIVES: To introduce a new design that explores how an acute exposure might lead to a sustained change in the risk of a recurrent outcome. STUDY DESIGN AND SETTING: The exposure-crossover design uses self-matching to control within-person confounding due to genetics, personality, and all other stable patient characteristics. The design is demonstrated using population-based individual-level health data from Ontario, Canada, for three separate medical conditions (n > 100,000 for each) related to the risk of a motor vehicle crash (total outcomes, >2,000 for each). RESULTS: The exposure-crossover design yields numerical risk estimates during the baseline interval before an intervention, the induction interval immediately ahead of the intervention, and the subsequent interval after the intervention. Accompanying graphs summarize results, provide an intuitive display to readers, and show risk comparisons (absolute and relative). Self-matching increases statistical efficiency, reduces selection bias, and yields quantitative analyses. The design has potential limitations related to confounding, artifacts, pragmatics, survivor bias, statistical models, potential misunderstandings, and serendipity. CONCLUSION: The exposure-crossover design may help in exploring selected questions in epidemiology science.
OBJECTIVES: To introduce a new design that explores how an acute exposure might lead to a sustained change in the risk of a recurrent outcome. STUDY DESIGN AND SETTING: The exposure-crossover design uses self-matching to control within-person confounding due to genetics, personality, and all other stable patient characteristics. The design is demonstrated using population-based individual-level health data from Ontario, Canada, for three separate medical conditions (n > 100,000 for each) related to the risk of a motor vehicle crash (total outcomes, >2,000 for each). RESULTS: The exposure-crossover design yields numerical risk estimates during the baseline interval before an intervention, the induction interval immediately ahead of the intervention, and the subsequent interval after the intervention. Accompanying graphs summarize results, provide an intuitive display to readers, and show risk comparisons (absolute and relative). Self-matching increases statistical efficiency, reduces selection bias, and yields quantitative analyses. The design has potential limitations related to confounding, artifacts, pragmatics, survivor bias, statistical models, potential misunderstandings, and serendipity. CONCLUSION: The exposure-crossover design may help in exploring selected questions in epidemiology science.
Keywords:
Comparative effectiveness research; Crossover studies; Epidemiologic research designs; Evaluation studies as topic; Minimization of bias; Outcome and process assessment; Self-matched analysis; Statistics methods
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