Statistical Modelling 6 (2006), 141–157

Handling dropout and clustering in longitudinal multicentre clinical trials

P Del Bianco
Clinical Trials and Biostatistics Unit,
Istituto Oncologico Veneto,
Via Gattamelata 64,
I–35128 Padua
Italy
eMail: paula.delbianco@istitutoncologicoveneto.it

R. Borgoni
Department of Statistics,
University of Milano-Bicocca
Italy

Abstract:

Many clinical trials enrol patients from different medical centres. Multi-centre studies are particularly helpful in cancer research as they allow researchers to evaluate the efficacy of a therapy in a variety of patients and settings, making it possible to investigate the effect of treatments in those caseswhen it is difficult, or even impossible, for a single centre to recruit the required number of patients. It is often argued, however, that despite agreement among different centres to followcommon standardized protocols, variation may occur in both baseline characteristics of the recruited patients and in treatment effects. This heterogeneity should be detected and, if present, accounted for in the data analysis. Furthermore, the longitudinal nature of these types of experimental studies raises the problem of attrition, that is, patients may dropout of the study for a number of reasons mainly death or disease progression. In this paper, we consider the health related quality of life of advanced melanoma patients in a longitudinal multi-centre randomized clinical trial comparing two different anti-tumoural treatments.We propose a Heckman type model to account for the possibility that patients dropout according to a non-ignorable mechanism. The model is extended to a multilevel setting to account both for the longitudinal nature and the multicentre structure of the design. We found a strong variation across centres in the quality of life evaluation. The effect of centres on the dropout was not found to be relevant in the considered data although dropout does depend on patient’s characteristics.

Keywords:

quality of life; Heckman selection model; multilevel models; not ignorable dropout
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