An approach to combining parallel and cross-over trials with and without run-in periods using individual patient data


BACKGROUND: In active run-in trials, where patients may be excluded after a run-in period based on their response to the treatment, it is implicitly assumed that patients have individual treatment effects. If individual patient data are available, active run-in trials can be modelled using patient-specific random effects. With more than one trial on the same medication available, one can obtain a more precise overall treatment effect estimate. METHODS: We present a model for joint analysis of a two-sequence, four-period cross-over trial (AABB/BBAA) and a three-sequence, two-period active run-in trial (AB/AA/A), where the aim is to investigate the effect of a new treatment for patients with pain due to osteoarthritis. RESULTS: Our approach enables us to separately estimate the direct treatment effect for all patients, for the patients excluded after the active run-in trial prior to randomisation, and for the patients who completed the active run-in trial. A similar model approach can be used to analyse other types of run-in trials, but this depends on the data and type of other trials available. LIMITATIONS: We assume equality of the various carry-over effects over time. CONCLUSIONS: The proposed approach is flexible and can be modified to handle other designs. Our results should be encouraging for those responsible for planning cost-efficient clinical development programmes.

Clinical Trials (London, England)