International Journal of Epidemiology, Vol 26, 1334-1339, Copyright © 1997 by International Epidemiological Association
LH Moulton and MJ Dibley
BACKGROUND: Standard Poisson regression analyses of infectious disease
incidence may not be optimal when covariates change over time. When a
longitudinal study is of sufficient duration so that multiple disease
episodes per person may occur, within-subject correlation may be present
and require special statistical consideration. Seasonality of disease
incidence is a common feature of field studies of respiratory and
diarrhoeal diseases in children. Accurate analysis may require keeping the
children aligned with respect to the same baseline hazard function. These
data features often are ignored in the analysis of such studies. METHODS:
Methods for accounting for multiple events are discussed. We propose the
use of a counting process model to retain the alignment with respect to
season. This model is supplemented by a simple bootstrap procedure to
account for the within-child correlation. RESULTS: The bootstrap technique
is illustrated with data from a randomized trial of the effects of vitamin
A supplementation on childhood morbidity. Standard errors for the main
effect of vitamin A are larger using the bootstrap, indicating the presence
of positive correlation. CONCLUSIONS: Time-to-event models can be useful in
the analysis of studies of childhood morbidity, especially for situations
with seasonality, waning of effect, or multiple events. The bootstrap
provides an appropriate, straightforward method for handling within- child
correlation in such settings.
ARTICLES
Multivariate time-to-event models for studies of recurrent childhood diseases
Department of International Health, Johns Hopkins University, School of Hygiene and Public Health, Baltimore, MD 21205, USA.
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