Self-matching increases statistical efficiency, reduces selection bias, and yields quantitative analyses. Accompanying graphs summarize results, provide an intuitive display to readers, and show risk comparisons (absolute and relative). 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. 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). The exposure- crossover design uses self-matching to control within-person confounding due to genetics, personality, and all other stable patient characteristics. To introduce a new design that explores how an acute exposure might lead to a sustained change in the risk of a recurrent outcome. The exposure- crossover design is a new method for studying sustained changes in recurrent events. R code for this task is provided and explained in the text. However, it is challenging to convert original dataset obtained from case report form to that suitable to be passed to clogit() function. The model fitting per se is not technically difficult because there is well developed statistical package. Furthermore, it allows for incorporation of other time-varying covariates that are not constant within subjects. Conditional logistic regression model is another way to estimate odds ratio of the exposure. Readers may adapt these codes to their own task. R codes for the calculation are provided in the main text. The relative risk and odds ratio, as well as their 95% confidence intervals (CIs), can be estimated using Cochran-Mantel-Haenszel method. Characteristic confounding that is constant within one person can be well controlled with this method. Case- crossover design can be viewed as the hybrid of case-control study and crossover design. PMID:24756878Ĭase- crossover design and its implementation in RĬase- crossover design is a variation of case-control design that it employs persons’ history periods as controls. In this review, we describe one type of case-only design, the case- crossover design, and discuss how the concept of exchangeability can be used to understand issues of confounding, carryover effects, period effects and selection bias in case- crossover studies. Although case-only designs use self-matching and only include individuals who develop the outcome of interest, issues of non-exchangeability are identical to those that arise in traditional case-control and cohort studies. In case-only studies, this issue is addressed by comparing each individual with his/herself. In cohort and case-control studies, confounding that arises as a result of differences in the distribution of determinants of the outcome between exposure groups leading to non-exchangeability are addressed by restriction, matching or with statistical models. Exchangeability in the case- crossover design
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