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Comparing the cohort design and the nested case-control design in the presence of both time-invariant and time-dependent treatment and competing risks: bias and precision

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Purpose — Observational studies using electronic administrative healthcare databases are often used to estimate the effects of treatments and exposures. Traditionally, a cohort design has been used to estimate these effects, but increasingly, studies are using a nested case-control (NCC) design. The relative statistical efficiency of these two designs has not been examined in detail.

Methods — We used Monte Carlo simulations to compare these two designs in terms of the bias and precision of effect estimates. We examined three different settings: (A) treatment occurred at baseline, and there was a single outcome of interest; (B) treatment was time varying, and there was a single outcome; and C treatment occurred at baseline, and there was a secondary event that competed with the primary event of interest. Comparisons were made of percentage bias, length of 95% confidence interval, and mean squared error (MSE) as a combined measure of bias and precision.

Results — In Setting A, bias was similar between designs, but the cohort design was more precise and had a lower MSE in all scenarios. In Settings B and C, the cohort design was more precise and had a lower MSE in all scenarios. In both Settings B and C, the NCC design tended to result in estimates with greater bias compared with the cohort design.

Conclusions — We conclude that in a range of settings and scenarios, the cohort design is superior in terms of precision and MSE.

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Citation

Austin PC, Anderson GM, Cigsar C, Gruneir A. Pharmacoepidemiol Drug Saf. 2012; 21(7):714-24. Epub 2012 Jun 1.

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