Using interviewer random effects to remove selection bias from HIV prevalence estimates Data analysis, statistics and modelling

BMC Medical Research Methodology

Volumn 15, Issue 1, 2015, Pages

  McGovern, Mark E ^a,b   Bärnighausen, Till ^b,c   Salomon, Joshua A ^b   Canning, David ^a,b  

^a HARVARD CENTER FOR POPULATION AND DEVELOPMENT STUDIES   (United States)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^c UNIVERSITY OF KWAZULU NATAL   (South Africa)

Author keywords

Heckman Selection Models; HIV Prevalence; Missing Data; Random Effects Estimation; Selection Bias

Indexed keywords

ADOLESCENT; ADULT; ALGORITHM; GHANA; HEALTH SURVEY; HIV INFECTIONS; HUMAN; INTERVIEW; MASS SCREENING; MIDDLE AGED; PREVALENCE; PROCEDURES; SELECTION BIAS; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; YOUNG ADULT; ZAMBIA;

ADOLESCENT; ADULT; ALGORITHMS; GHANA; HEALTH SURVEYS; HIV INFECTIONS; HUMANS; INTERVIEWS AS TOPIC; MASS SCREENING; MIDDLE AGED; MODELS, STATISTICAL; PREVALENCE; SELECTION BIAS; YOUNG ADULT; ZAMBIA;

EID: 84928994333     PISSN: None     EISSN: 14712288     Source Type: Journal    
DOI: 10.1186/1471-2288-15-8     Document Type: Article

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40. The pre-publication history for this paper can be accessed here: http://www.biomedcentral.com/1471-2288/15/8/prepub