Cargando…

Unweighted regression models perform better than weighted regression techniques for respondent-driven sampling data: results from a simulation study

BACKGROUND: It is unclear whether weighted or unweighted regression is preferred in the analysis of data derived from respondent driven sampling. Our objective was to evaluate the validity of various regression models, with and without weights and with various controls for clustering in the estimati...

Descripción completa

Detalles Bibliográficos
Autores principales: Avery, Lisa, Rotondi, Nooshin, McKnight, Constance, Firestone, Michelle, Smylie, Janet, Rotondi, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6819607/
https://www.ncbi.nlm.nih.gov/pubmed/31664912
http://dx.doi.org/10.1186/s12874-019-0842-5
_version_ 1783463773005152256
author Avery, Lisa
Rotondi, Nooshin
McKnight, Constance
Firestone, Michelle
Smylie, Janet
Rotondi, Michael
author_facet Avery, Lisa
Rotondi, Nooshin
McKnight, Constance
Firestone, Michelle
Smylie, Janet
Rotondi, Michael
author_sort Avery, Lisa
collection PubMed
description BACKGROUND: It is unclear whether weighted or unweighted regression is preferred in the analysis of data derived from respondent driven sampling. Our objective was to evaluate the validity of various regression models, with and without weights and with various controls for clustering in the estimation of the risk of group membership from data collected using respondent-driven sampling (RDS). METHODS: Twelve networked populations, with varying levels of homophily and prevalence, based on a known distribution of a continuous predictor were simulated using 1000 RDS samples from each population. Weighted and unweighted binomial and Poisson general linear models, with and without various clustering controls and standard error adjustments were modelled for each sample and evaluated with respect to validity, bias and coverage rate. Population prevalence was also estimated. RESULTS: In the regression analysis, the unweighted log-link (Poisson) models maintained the nominal type-I error rate across all populations. Bias was substantial and type-I error rates unacceptably high for weighted binomial regression. Coverage rates for the estimation of prevalence were highest using RDS-weighted logistic regression, except at low prevalence (10%) where unweighted models are recommended. CONCLUSIONS: Caution is warranted when undertaking regression analysis of RDS data. Even when reported degree is accurate, low reported degree can unduly influence regression estimates. Unweighted Poisson regression is therefore recommended.
format Online
Article
Text
id pubmed-6819607
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-68196072019-10-31 Unweighted regression models perform better than weighted regression techniques for respondent-driven sampling data: results from a simulation study Avery, Lisa Rotondi, Nooshin McKnight, Constance Firestone, Michelle Smylie, Janet Rotondi, Michael BMC Med Res Methodol Research Article BACKGROUND: It is unclear whether weighted or unweighted regression is preferred in the analysis of data derived from respondent driven sampling. Our objective was to evaluate the validity of various regression models, with and without weights and with various controls for clustering in the estimation of the risk of group membership from data collected using respondent-driven sampling (RDS). METHODS: Twelve networked populations, with varying levels of homophily and prevalence, based on a known distribution of a continuous predictor were simulated using 1000 RDS samples from each population. Weighted and unweighted binomial and Poisson general linear models, with and without various clustering controls and standard error adjustments were modelled for each sample and evaluated with respect to validity, bias and coverage rate. Population prevalence was also estimated. RESULTS: In the regression analysis, the unweighted log-link (Poisson) models maintained the nominal type-I error rate across all populations. Bias was substantial and type-I error rates unacceptably high for weighted binomial regression. Coverage rates for the estimation of prevalence were highest using RDS-weighted logistic regression, except at low prevalence (10%) where unweighted models are recommended. CONCLUSIONS: Caution is warranted when undertaking regression analysis of RDS data. Even when reported degree is accurate, low reported degree can unduly influence regression estimates. Unweighted Poisson regression is therefore recommended. BioMed Central 2019-10-29 /pmc/articles/PMC6819607/ /pubmed/31664912 http://dx.doi.org/10.1186/s12874-019-0842-5 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Avery, Lisa
Rotondi, Nooshin
McKnight, Constance
Firestone, Michelle
Smylie, Janet
Rotondi, Michael
Unweighted regression models perform better than weighted regression techniques for respondent-driven sampling data: results from a simulation study
title Unweighted regression models perform better than weighted regression techniques for respondent-driven sampling data: results from a simulation study
title_full Unweighted regression models perform better than weighted regression techniques for respondent-driven sampling data: results from a simulation study
title_fullStr Unweighted regression models perform better than weighted regression techniques for respondent-driven sampling data: results from a simulation study
title_full_unstemmed Unweighted regression models perform better than weighted regression techniques for respondent-driven sampling data: results from a simulation study
title_short Unweighted regression models perform better than weighted regression techniques for respondent-driven sampling data: results from a simulation study
title_sort unweighted regression models perform better than weighted regression techniques for respondent-driven sampling data: results from a simulation study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6819607/
https://www.ncbi.nlm.nih.gov/pubmed/31664912
http://dx.doi.org/10.1186/s12874-019-0842-5
work_keys_str_mv AT averylisa unweightedregressionmodelsperformbetterthanweightedregressiontechniquesforrespondentdrivensamplingdataresultsfromasimulationstudy
AT rotondinooshin unweightedregressionmodelsperformbetterthanweightedregressiontechniquesforrespondentdrivensamplingdataresultsfromasimulationstudy
AT mcknightconstance unweightedregressionmodelsperformbetterthanweightedregressiontechniquesforrespondentdrivensamplingdataresultsfromasimulationstudy
AT firestonemichelle unweightedregressionmodelsperformbetterthanweightedregressiontechniquesforrespondentdrivensamplingdataresultsfromasimulationstudy
AT smyliejanet unweightedregressionmodelsperformbetterthanweightedregressiontechniquesforrespondentdrivensamplingdataresultsfromasimulationstudy
AT rotondimichael unweightedregressionmodelsperformbetterthanweightedregressiontechniquesforrespondentdrivensamplingdataresultsfromasimulationstudy