In a case-control study, matching on a disease risk score (DRS), which includes many confounders, should theoretically result in greater precision than matching on only a few confounders; however, this has not been investigated. We simulated 1,000 hypothetical cohorts with a binary exposure, a time-to-event outcome, and 13 covariates. Each cohort comprised 2 subcohorts of 10,000 patients each: a historical subcohort and a concurrent subcohort. DRS were estimated in the historical subcohorts and applied to the concurrent subcohorts. Nested case-control studies were conducted in the concurrent subcohorts using incidence density sampling with 2 strategies—matching on age and sex, with adjustment for additional confounders, and matching on DRS—followed by conditional logistic regression for 9 outcome-exposure incidence scenarios. In all scenarios, DRS matching yielded lower average standard errors and mean squared errors than did matching on age and sex. In 6 scenarios, DRS matching also resulted in greater empirical power. DRS matching resulted in less relative bias than did matching on age and sex at lower outcome incidences but more relative bias at higher incidences. Post-hoc analysis revealed that the effect of DRS model misspecification might be more pronounced at higher outcome incidences, resulting in higher relative bias. These results suggest that DRS matching might increase the statistical efficiency of case-control studies, particularly when the outcome is rare.