Correcting for selection bias in HIV prevalence estimates: an application of sample selection models using data from population‐based HIV surveys in seven sub‐Saharan African countries

INTRODUCTION: Population‐based biomarker surveys are the gold standard for estimating HIV prevalence but are susceptible to substantial non‐participation (up to 30%). Analytical missing data methods, including inverse‐probability weighting (IPW) and multiple imputation (MI), are biased when data are...

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Detalles Bibliográficos
Autores principales: Palma, Anton M., Marra, Giampiero, Bray, Rachel, Saito, Suzue, Awor, Anna Colletar, Jalloh, Mohamed F., Kailembo, Alexander, Kirungi, Wilford, Mgomella, George S., Njau, Prosper, Voetsch, Andrew C., Ward, Jennifer A., Bärnighausen, Till, Harling, Guy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9353488/
https://www.ncbi.nlm.nih.gov/pubmed/35929226
http://dx.doi.org/10.1002/jia2.25954

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9353488/
https://www.ncbi.nlm.nih.gov/pubmed/35929226
http://dx.doi.org/10.1002/jia2.25954

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