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Analysis of dynamic cohort data.
American Journal of Epidemiology 2001;154(4):366-372.
Williamson JM, Satten GA, Hanson JA, Weinstock H, Datta S.
Abstract
Left-truncated and interval-censored data, termed dynamic cohort data, arise
in longitudinal studies with rolling admissions and only occasional follow-up.
The authors compared four approaches for analyzing such data: a constant hazard
model; maximum likelihood estimation with flexible parametric models; the midpoint
method, in which the midpoint of the last negative and first positive test
result is used in a Cox proportional hazards model that accounts for left truncation;
and a semiparametric method that uses imputed failure times in the Cox model.
By using a simulation study, they assessed the performance of these approaches
under conditions that can arise in observational studies: changes in disease
incidence and changes in the underlying population. The simulation results
indicated that the constant hazard model and midpoint method were inadequate
and that the flexible parametric model was useful when enough parameters were
used in modeling the baseline hazard. The semiparametric method ensured correct
parameter (odds ratio) estimation when the baseline hazard was misspecified,
but the trade-off increased computational complexity. In this paper, a study
of the incidence of human immunodeficiency virus in patients repeatedly tested
for the virus at a sexually transmitted disease clinic in New Orleans, Louisiana,
illustrates the methods used.