Existing literature comparing statistical properties of nested case–control and case–cohort methods have become insufficient for present day epidemiologists. The literature has not reconciled conflicting conclusions about the standard methods. Moreover, a comparison including newly developed methods, such as inverse probability weighting methods, is needed. Two analytical methods for nested case–control studies and six methods for case–cohort studies using proportional hazards regression model were summarized and their statistical properties were compared. The answer to which design and method is more powerful was more nuanced than what was previously reported. For both nested case–control and case–cohort designs, inverse probability weighting methods were more powerful than the standard methods. However, the difference became negligible when the proportion of failure events was very low (<1 %) in the full cohort. The comparison between two designs depended on the censoring types and incidence proportion: with random censoring, nested case–control designs coupled with the inverse probability weighting method yielded the highest statistical power among all methods for both designs. With fixed censoring times, there was little difference in efficiency between two designs when inverse probability weighting methods were used; however, the standard case–cohort methods were more powerful than the conditional logistic method for nested case–control designs. As the proportion of failure events in the full cohort became smaller (<10 %), nested case–control methods outperformed all case–cohort methods and the choice of analytic methods within each design became less important. When the predictor of interest was binary, the standard case–cohort methods were often more powerful than the conditional logistic method for nested case–control designs.
- Inverse probability weighting
- Nested case–control
- Simulation study
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