Multivariate analysis in case-base designs depends on approximate methods. In the present study, new pseudo-likelihood methods are developed for this design. With these methods, the case-cohort risk ratio and rate ratio as well as their standard errors are easily estimated using logistic regression and Poisson regression, respectively. This is illustrated by the association between hypertension and cardiovascular mortality in a cohort, estimated by case-cohort analysis, using samples of several sizes. The estimates are compared with those obtaining in full-cohort and nested case-control designs. The results indicate that these methods, which require nothing but widely available computer software, are valid. The case-cohort design, therefore, is a good, sometimes even advantageous alternative to the nested case-control design, in studying a disease that is not very rare. Application of the risk ratio method to the full cohort, using a 'sample' of 100 per cent follows logically; whenever the true risk ratio is desired instead of the odds ratio, a multivariate model for its estimation is therefore available.