eprintid: 1476060
rev_number: 28
eprint_status: archive
userid: 608
dir: disk0/01/47/60/60
datestamp: 2016-04-06 13:58:12
lastmod: 2021-10-05 00:26:44
status_changed: 2016-04-06 13:58:12
type: article
metadata_visibility: show
creators_name: McGovern, ME
creators_name: Bärnighausen, T
creators_name: Marra, G
creators_name: Radice, R
title: On the assumption of bivariate normality in selection models: a Copula approach applied to estimating HIV prevalence.
ispublished: pub
divisions: UCL
divisions: B04
divisions: C06
divisions: F61
keywords: Adult, Computer Simulation, Female, HIV Infections, Humans, Male, Models, Statistical, Normal Distribution, Prevalence, Selection Bias, Zambia
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). 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.
date: 2015-03
date_type: published
official_url: http://dx.doi.org/10.1097/EDE.0000000000000218
oa_status: green
full_text_type: other
pmcid: PMC4726739
language: eng
primo: open
primo_central: open_green
article_type_text: Journal Article
verified: verified_manual
elements_id: 1021952
doi: 10.1097/EDE.0000000000000218
pii: 00001648-201503000-00016
lyricists_name: Marra, Giampiero
lyricists_id: GMARR98
actors_name: Marra, Giampiero
actors_name: Smith, Robert
actors_id: GMARR98
actors_id: RSSMI20
actors_role: owner
actors_role: impersonator
full_text_status: public
publication: Epidemiology
volume: 26
number: 2
pagerange: 229-237
event_location: United States
issn: 1531-5487
citation:        McGovern, ME;    Bärnighausen, T;    Marra, G;    Radice, R;      (2015)    On the assumption of bivariate normality in selection models: a Copula approach applied to estimating HIV prevalence.                   Epidemiology , 26  (2)   pp. 229-237.    10.1097/EDE.0000000000000218 <https://doi.org/10.1097/EDE.0000000000000218>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1476060/3/Marra_1476060_Epi%20Paper%2010%2015%2013.pdf