Abstract
In a nested case-control study, controls are selected among those at risk at the failure times of the cases (corresponding to matching on study time). Usually additionally matching is also performed to control for potential confounders. If one breaks the matching, nested case-control data may be considered as case-cohort data with a non-standard sampling design for the subcohort. The data may then be analysed using inverse probability weighting or by methods that make use of the data for the full cohort (ML-estimation and multiple imputation). In the talk, methods for analysing nested case-control data will be reviewed, and it will be discussed when it may be useful to break the matching and when bias will be introduced by doing so.