Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

OBJECTIVES: To empirically evaluate the concordance of effect estimates between case-only and parallel group designs and to identify predictors of discrepancies. STUDY DESIGN AND SETTING: MEDLINE and EMBASE databases were searched through June 31, 2013. Studies that used both a case-only (case crossover or self-controlled case series) and a parallel group design (cohort or case-control) were identified. Spearman correlation coefficient was used to evaluate the concordance between designs. Z-scores were used to assess whether differences in the effect estimates were common, using an absolute threshold value of 1.96. A prediction model was built to identify predictors of discrepancies. RESULTS: The search identified 1,367 articles of which 53 were included for analysis. In total, 519 comparisons were made. The correlation coefficient between case-only vs. parallel group studies was 0.64 (P < .001). In 221 of the 519 comparisons (43%), the difference between both study designs was larger than the predetermined threshold. The following predictors of discrepancy were found: intermittent exposure, rare event, acute outcome, length of hazard period, type of case-only design, and sample size (C statistic of 0.783). CONCLUSION: The concordance between effect estimates of case-only and parallel group designs is moderate. Such discrepancies could be predicted by failure to meet assumptions of case-only designs.

Original publication

DOI

10.1016/j.jclinepi.2015.09.018

Type

Journal

J Clin Epidemiol

Publication Date

03/2016

Volume

71

Pages

18 - 24

Keywords

Case crossover, Case–control studies, Cohort studies, Crossover studies, Research design, Self-controlled case series, Case-Control Studies, Clinical Studies as Topic, Cohort Studies, Cross-Over Studies, Databases, Factual, Humans, Research Design