• case–cohort;
  • multivariate survival data;
  • frailty models;
  • family data;
  • population registers


In the Nordic countries, there exist several registers containing information on diseases and risk factors for millions of individuals. This information can be linked to families by the use of personal identification numbers, and represents a great opportunity for studying diseases that show familial aggregation. Due to the size of the registers, it is difficult to analyze the data by using traditional methods for multivariate survival analysis, such as frailty or copula models. Since the size of the cohort is known, case–cohort methods based on pseudo-likelihoods are suitable for analyzing the data. We present methods for sampling control families both with and without replacement, and with or without stratification. The data are stratified according to family size and covariate values. Depending on the sampling method, results from simulations indicate that one only needs to sample 1–5 per cent of the control families in order to obtain good efficiency compared with a traditional cohort analysis. We also provide an application to survival data from the Medical Birth Registry of Norway. Copyright © 2007 John Wiley & Sons, Ltd.