Biomedical Engineering Reference
In-Depth Information
CHAPTER 9
APPLICATION OF THE NON-PROPORTIONAL RATES
MODEL TO RECURRENT EVENT DATA: ANALYSIS OF
RISK FACTORS FOR PRE-SCHOOL ASTHMA
Jianwen Cai
Department of Biostatistics,
University of North Carolina at Chapel Hill,
Chapel Hill, NC, 27599-7420, USA
Douglas E. Schaubel
Department of Biostatistics,
University of Michigan,
Ann Arbor, MI, 48109-2029, USA
Asthma remains one of the most common chronic childhood illnesses
and a leading cause of hospital admissions. Our clinical objective was
to assess the eect of gender, birth characteristics and neonatal respi-
ratory disorders on pre-school asthma rates of (i) hospitalization and
(ii) days hospitalized. The proportional rates (PR) model is a exible
adaptation to recurrent event data of the well-known Cox proportional
hazards model. The PR model is related to Poisson regression, but re-
laxes the often untenable assumption that the events within a subject
are independent. Despite having been originally proposed over a decade
ago, examples of the use of the proportional rates model in the medical
literature are quite rare. Moreover, little attention has been devoted to
the extension of the PR model to accommodate covariate eects which
vary over time. We evaluate the non-proportional rates model through
simulation. We then apply the non-PR model to asthma data from a
retrospective birth cohort study.
1. Introduction
Asthma remains one of the most common chronic childhood illnesses, and
a leading cause of hospital admissions
19;22
. Rates of hospitalization for
asthma have increased in several countries during the last two decades
including Canada
11;27
and the United States
26
. Childhood asthma usually
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