Analysis of case-cohort data: A comparison of different methods

ObjectiveThe case-cohort design combines the advantages of a prospective cohort study and the efficiency of a case–control design. Usually a Cox proportional-hazards model is used for the analyses. However, adaptation of the model is necessary because of the sampling. We compared three methods that were proposed in the literature, which differ in weighting of study subjects: Prentice's, Barlow's, and Self and Prentice's method.Study Design and SettingIn a cohort of 17,357 women we studied the relationship between body mass index and cardiovascular disease (n = 821) with varying subcohort sizes (sampling fraction = 0.005, 0.01, 0.05, 0.10, 0.15).ResultsEven with a sampling fraction of 0.01, all three methods showed identical estimates and standard errors (SE). With sampling fractions ≥0.10, results of the case-cohort analyses were similar to the full-cohort analyses. With simulations, the three methods provided different results if the full cohort is small (<1,250 subjects, subcohort = 10%, 8% failures) or if the subcohort size was smaller than 15% (full cohort of 1,000 observations, 8% failures). The difference between the methods did not change with the number of failures or with different effect sizes.ConclusionIn the above-mentioned situations, the effect estimates and SE of Prentice's method most resembled the estimates of the full-cohort estimates.