McGovern, Mark E.; Bärnighausen, Till; Marra,
Giampiero; Radice, Rosalba, Epidemiology. 26(2):229-237, March 2015.
Abstract
Background: Heckman-type selection models
have been used to control HIV prevalence estimates for selection bias when
participation in HIV testing and HIV status are associated after controlling
for observed variables. These models typically rely on the strong assumption
that the error terms in the participation and the outcome equations that
comprise the model are distributed as bivariate normal.
Methods: We introduce a novel approach for relaxing the bivariate normality assumption in selection models using copula functions. We apply this method to estimating HIV prevalence and new confidence intervals (CI) in the 2007 Zambia Demographic and Health Survey (DHS) by using interviewer identity as the selection variable that predicts participation (consent to test) but not the outcome (HIV status).
Methods: We introduce a novel approach for relaxing the bivariate normality assumption in selection models using copula functions. We apply this method to estimating HIV prevalence and new confidence intervals (CI) in the 2007 Zambia Demographic and Health Survey (DHS) by using interviewer identity as the selection variable that predicts participation (consent to test) but not the outcome (HIV status).
Results: We show in a simulation study that
selection models can generate biased results when the bivariate normality
assumption is violated. In the 2007 Zambia DHS, HIV prevalence estimates are
similar irrespective of the structure of the association assumed between
participation and outcome. For men, we estimate a population HIV prevalence of
21% (95% CI = 16%–25%) compared with 12% (11%–13%) among those who consented to
be tested; for women, the corresponding figures are 19% (13%–24%) and 16%
(15%–17%).
Conclusions: Copula approaches to Heckman-type
selection models are a useful addition to the methodological toolkit of HIV
epidemiology and of epidemiology in general. We develop the use of this
approach to systematically evaluate the robustness of HIV prevalence estimates
based on selection models, both empirically and in a simulation study.
No comments:
Post a Comment