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Purpose: The nested case–control study design, in which a fixed number of controls are matched to each case, is often used to analyze exposure–response associations within a cohort. It has become common practice to sample four or five controls per case; however, previous research has shown that in certain instances, significant gains in relative efficiency can be realized when more controls are matched to each case. This study expanded upon this and investigated the effect of (i) the number of cases, (ii) the strength of the exposure–response, and (iii) the skewness of the exposure distribution on the bias and relative efficiency of the conditional likelihood estimator from a nested case–control study. Methods: Cohorts were simulated and analyzed using conditional logistic regression. Results: The relative efficiency decreased and bias away from the null increased, as the true exposure–response parameter increased and the skewness of the exposure distribution of the risk-sets increased. This became more pronounced when the number of cases in the cohort was small. Conclusions: Gains in relative efficiency and a reduction in bias can be realized by sampling more than four or five controls per case generally used, especially when there are few cases, a strong exposure–response relation, and a skewed exposure variable.

A Simulation Study of Relative Efficiency and Bias in the Nested Case–Control Study Design