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Investigating trial and treatment heterogeneity in an individual patient data meta-analysis of survival data by means of the penalized maximum likelihood approach.

Abstract : In a meta-analysis combining survival data from different clinical trials, an important issue is the possible heterogeneity between trials. Such intertrial variation can not only be explained by heterogeneity of treatment effects across trials but also by heterogeneity of their baseline risk. In addition, one might examine the relationship between magnitude of the treatment effect and the underlying risk of the patients in the different trials. Such a scenario can be accounted for by using additive random effects in the Cox model, with a random trial effect and a random treatment-by-trial interaction. We propose to use this kind of model with a general correlation structure for the random effects and to estimate parameters and hazard function using a semi-parametric penalized marginal likelihood method (maximum penalized likelihood estimators). This approach gives smoothed estimates of the hazard function, which represents incidence in epidemiology. The idea for the approach in this paper comes from the study of heterogeneity in a large meta-analysis of randomized trials in patients with head and neck cancers (meta-analysis of chemotherapy in head and neck cancers) and the effect of adding chemotherapy to locoregional treatment. The simulation study and the application demonstrate that the proposed approach yields satisfactory results and they illustrate the need to use a flexible variance-covariance structure for the random effects.
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https://www.hal.inserm.fr/inserm-00289664
Contributor : Evelyne Mouillet <>
Submitted on : Monday, June 23, 2008 - 11:34:08 AM
Last modification on : Tuesday, April 28, 2020 - 1:02:51 AM

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Virginie Rondeau, Stefan Michiels, Benoit Liquet, Jean-Pierre Pignon. Investigating trial and treatment heterogeneity in an individual patient data meta-analysis of survival data by means of the penalized maximum likelihood approach.. Statistics in Medicine, Wiley-Blackwell, 2008, 27 (11), pp.1894-910. ⟨10.1002/sim.3161⟩. ⟨inserm-00289664⟩

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