Robust Estimation of Mean Functions and Treatment Effects for Recurrent Events Under Event-Dependent Censoring and Termination: Application to Skeletal Complications in Cancer Metastatic to Bone
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Date
2009
Authors
Cook, Richard J.
Lakhal-Chaieb, Lajmi
Lee, Ker-Ai
Lawless, Jerald F.
Journal Title
Journal ISSN
Volume Title
Publisher
Taylor & Francis
Abstract
In clinical trials featuring recurrent clinical events, the definition and estimation of treatment
effects involves a number of interesting issues, especially when loss to follow-up may be eventrelated
and when terminal events such as death preclude the occurrence of further events. This
paper discusses a clinical trial of breast cancer patients with bone metastases where the recurrent
events are skeletal complications, and where patients may die during the trial. We argue that treatment
effects should be based on marginal rate and mean functions. When recurrent event data are
subject to event-dependent censoring, however, ordinary marginal methods may yield inconsistent
estimates. Incorporating correctly specified inverse probability of censoring weights into analyses
can protect against dependent censoring and yield consistent estimates of marginal features. An
alternative approach is to obtain estimates of rate and mean functions from models that involve
some conditioning to render censoring conditionally independent. We consider three methods of
estimating mean functions of recurrent event processes and examine the bias and efficiency of
unweighted and inverse probability weighted versions of the methods with and without a terminating
event. We compare the methods via simulation and use them to analyse the data from the
breast cancer trial.
Description
Keywords
Dependent censoring, Inverse probability of censoring weights, Recurrent event, Terminal event