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On the assumption of bivariate normality in selection models: a Copula approach applied to estimating HIV prevalence.

ABSTRACT: 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.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).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%).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.

SUBMITTER: McGovern ME

PROVIDER: S-EPMC4726739 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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On the assumption of bivariate normality in selection models: a Copula approach applied to estimating HIV prevalence.

McGovern Mark E ME Bärnighausen Till T Marra Giampiero G Radice Rosalba R

Epidemiology (Cambridge, Mass.) 20150301 2

<h4>Background</h4>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.<h4>Methods</h4>We introduce a novel approach for relaxing the bivariate normality assumption ...[more]

PMID: 25643102

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Project description:BackgroundEstimation of prevalence of feeding practices during diarrhea using conventional imputation methods may be biased as these methods apply to observed factors and in this study, feeding practice status was unobserved for those without diarrhea. The study aimed at re-estimating the prevalence of feeding practices using the bivariate sample selection model.MethodsThe study used 2015-2016 Malawi demographic health survey (MDHS) data which had 16,246 children records who had diarrhea or not. A bivariate Joe copula regression model with 90 degrees rotation was fitted to either drinking or eating more, with diarrhea as a sample selection outcome in the bivariate models. The prevalence of drinking more than usual and prevalence of eating more than usual were then estimated based on the fitted bivariate model. These prevalences were then compared to the prevalences estimated using the conventional imputation method.ResultsThere was a substantial increase in the re-estimated national prevalence of drinking more fluids (40.0%, 95% CI [31.7-50.5]) or prevalence of eating more food (20.46%, 95% CI [9.87-38.55]) using the bivariate model as compared to the prevalences estimated by the conventional imputation method, that is, (28.9%, 95% CI [27.0-30.7]) and (13.1%, 95% CI [12.0-15.0]) respectively. The maps of the regional prevalences showed similar results where the prevalences estimated by the bivariate model were relatively higher than those estimated by the standard imputation method. The presence of diarrhea was somehow weakly negatively correlated with either drinking more fluids or eating more food.ConclusionThe estimation of prevalence of drinking more fluids or eating more food during diarrhea should use bivariate modelling to model sample selection variable so as to minimize bias. The observed negative correlation between diarrhea presence and feeding practices implies that mothers should be encouraged to let their children drink more fluids or eat more food during diarrhea episode to avoid dehydration and malnutrition.

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On the assumption of bivariate normality in selection models: a Copula approach applied to estimating HIV prevalence.

Publications

On the assumption of bivariate normality in selection models: a Copula approach applied to estimating HIV prevalence.

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