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Use of predicted vital status to improve survival analysis of multidrug-resistant tuberculosis cohorts

BACKGROUND: Multidrug-resistant tuberculosis (MDR-TB) cohorts often lack long-term survival data, and are summarized instead by initial treatment outcomes. When using Cox proportional hazards models to analyze these cohorts, this leads to censoring subjects at the time of the initial treatment outco...

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Autores principales: Brooks, Meredith B., Keshavjee, Salmaan, Gelmanova, Irina, Zemlyanaya, Nataliya A., Mitnick, Carole D., Manjourides, Justin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6290510/
https://www.ncbi.nlm.nih.gov/pubmed/30537944
http://dx.doi.org/10.1186/s12874-018-0637-0
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author Brooks, Meredith B.
Keshavjee, Salmaan
Gelmanova, Irina
Zemlyanaya, Nataliya A.
Mitnick, Carole D.
Manjourides, Justin
author_facet Brooks, Meredith B.
Keshavjee, Salmaan
Gelmanova, Irina
Zemlyanaya, Nataliya A.
Mitnick, Carole D.
Manjourides, Justin
author_sort Brooks, Meredith B.
collection PubMed
description BACKGROUND: Multidrug-resistant tuberculosis (MDR-TB) cohorts often lack long-term survival data, and are summarized instead by initial treatment outcomes. When using Cox proportional hazards models to analyze these cohorts, this leads to censoring subjects at the time of the initial treatment outcome, instead of them providing full survival data. This may violate the non-informative censoring assumption of the model and may produce biased effect estimates. To address this problem, we develop a tool to predict vital status at the end of a cohort period using the initial treatment outcome and assess its ability to reduce bias in treatment effect estimates. METHODS: We derive and apply a logistic regression model to predict vital status at the end of the cohort period and modify the unobserved survival outcomes to better match the predicted survival experience of study subjects. We compare hazard ratio estimates for effect of an aggressive treatment regimen from Cox proportional hazards models using time to initial treatment outcome, predicted vital status, and true vital status at the end of the cohort period. RESULTS: Models fit from initial treatment outcomes underestimate treatment effects by up to 22.1%, while using predicted vital status reduced this bias by 5.4%. Models utilizing the predicted vital status produce effect estimates consistently stronger and closer to the true treatment effect than estimates produced by models using the initial treatment outcome. CONCLUSIONS: Although studies often use initial treatment outcomes to estimate treatment effects, this may violate the non-informative censoring assumption of the Cox proportional hazards model and result in biased treatment effect estimates. Using predicted vital status at the end of the cohort period may reduce this bias in the analyses of MDR-TB treatment cohorts, yielding more accurate, and likely larger, treatment effect estimates. Further, these larger effect sizes can have downstream impacts on future study design by increasing power and reducing sample size needs.
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spelling pubmed-62905102018-12-17 Use of predicted vital status to improve survival analysis of multidrug-resistant tuberculosis cohorts Brooks, Meredith B. Keshavjee, Salmaan Gelmanova, Irina Zemlyanaya, Nataliya A. Mitnick, Carole D. Manjourides, Justin BMC Med Res Methodol Research Article BACKGROUND: Multidrug-resistant tuberculosis (MDR-TB) cohorts often lack long-term survival data, and are summarized instead by initial treatment outcomes. When using Cox proportional hazards models to analyze these cohorts, this leads to censoring subjects at the time of the initial treatment outcome, instead of them providing full survival data. This may violate the non-informative censoring assumption of the model and may produce biased effect estimates. To address this problem, we develop a tool to predict vital status at the end of a cohort period using the initial treatment outcome and assess its ability to reduce bias in treatment effect estimates. METHODS: We derive and apply a logistic regression model to predict vital status at the end of the cohort period and modify the unobserved survival outcomes to better match the predicted survival experience of study subjects. We compare hazard ratio estimates for effect of an aggressive treatment regimen from Cox proportional hazards models using time to initial treatment outcome, predicted vital status, and true vital status at the end of the cohort period. RESULTS: Models fit from initial treatment outcomes underestimate treatment effects by up to 22.1%, while using predicted vital status reduced this bias by 5.4%. Models utilizing the predicted vital status produce effect estimates consistently stronger and closer to the true treatment effect than estimates produced by models using the initial treatment outcome. CONCLUSIONS: Although studies often use initial treatment outcomes to estimate treatment effects, this may violate the non-informative censoring assumption of the Cox proportional hazards model and result in biased treatment effect estimates. Using predicted vital status at the end of the cohort period may reduce this bias in the analyses of MDR-TB treatment cohorts, yielding more accurate, and likely larger, treatment effect estimates. Further, these larger effect sizes can have downstream impacts on future study design by increasing power and reducing sample size needs. BioMed Central 2018-12-11 /pmc/articles/PMC6290510/ /pubmed/30537944 http://dx.doi.org/10.1186/s12874-018-0637-0 Text en © The Author(s). 2018 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
Brooks, Meredith B.
Keshavjee, Salmaan
Gelmanova, Irina
Zemlyanaya, Nataliya A.
Mitnick, Carole D.
Manjourides, Justin
Use of predicted vital status to improve survival analysis of multidrug-resistant tuberculosis cohorts
title Use of predicted vital status to improve survival analysis of multidrug-resistant tuberculosis cohorts
title_full Use of predicted vital status to improve survival analysis of multidrug-resistant tuberculosis cohorts
title_fullStr Use of predicted vital status to improve survival analysis of multidrug-resistant tuberculosis cohorts
title_full_unstemmed Use of predicted vital status to improve survival analysis of multidrug-resistant tuberculosis cohorts
title_short Use of predicted vital status to improve survival analysis of multidrug-resistant tuberculosis cohorts
title_sort use of predicted vital status to improve survival analysis of multidrug-resistant tuberculosis cohorts
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6290510/
https://www.ncbi.nlm.nih.gov/pubmed/30537944
http://dx.doi.org/10.1186/s12874-018-0637-0
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