Statistical Modelling 6 (2006), 141157
Handling dropout and clustering in longitudinal
multicentre clinical trials
P Del Bianco
Clinical Trials and Biostatistics Unit,
Istituto Oncologico Veneto,
Via Gattamelata 64,
I35128 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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