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Improved Monte Carlo methods for estimating confidence intervals for eleven commonly used health disparity measures

Health disparities are commonplace and of broad interest to policy makers, but are also challenging to measure and communicate. The Health Disparity Calculator software (HD*Calc, v1.2.4) offers Monte Carlo simulation (MCS)-based confidence interval (CI) estimation of eleven disparity measures. The M...

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Autores principales: Ahn, Jaeil, Harper, Sam, Yu, Mandi, Feuer, Eric J., Liu, Benmei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6622532/
https://www.ncbi.nlm.nih.gov/pubmed/31295305
http://dx.doi.org/10.1371/journal.pone.0219542
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author Ahn, Jaeil
Harper, Sam
Yu, Mandi
Feuer, Eric J.
Liu, Benmei
author_facet Ahn, Jaeil
Harper, Sam
Yu, Mandi
Feuer, Eric J.
Liu, Benmei
author_sort Ahn, Jaeil
collection PubMed
description Health disparities are commonplace and of broad interest to policy makers, but are also challenging to measure and communicate. The Health Disparity Calculator software (HD*Calc, v1.2.4) offers Monte Carlo simulation (MCS)-based confidence interval (CI) estimation of eleven disparity measures. The MCS approach provides accurate CI estimation, except when data are scarce (e.g., rare cancers). To address sparse data challenges to CI estimation, we propose two solutions: 1) employing the gamma distribution in the MCS and 2) utilizing a zero-inflated Poisson estimate for Poisson sampling in simulation experiments. We evaluate each solution through simulation studies using female breast, female brain, lung, and cervical cancer data from the Surveillance, Epidemiology, and End Results (SEER) program. We compare the coverage probabilities (CPs) of eleven health disparity measures based on simulated datasets. The truncated normal distribution implemented in the MCS with the standard Poisson samples (the default setting of HD*Calc) leads to less-than-optimal coverage probabilities (<95%). When both the gamma distribution and the estimated mean from the zero-inflated Poisson are used for the MCS, the coverage probabilities are close to the nominal level of 95%. Simulation studies also demonstrate that collapsing age categories for better CI estimation is not a pragmatic solution.
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spelling pubmed-66225322019-07-25 Improved Monte Carlo methods for estimating confidence intervals for eleven commonly used health disparity measures Ahn, Jaeil Harper, Sam Yu, Mandi Feuer, Eric J. Liu, Benmei PLoS One Research Article Health disparities are commonplace and of broad interest to policy makers, but are also challenging to measure and communicate. The Health Disparity Calculator software (HD*Calc, v1.2.4) offers Monte Carlo simulation (MCS)-based confidence interval (CI) estimation of eleven disparity measures. The MCS approach provides accurate CI estimation, except when data are scarce (e.g., rare cancers). To address sparse data challenges to CI estimation, we propose two solutions: 1) employing the gamma distribution in the MCS and 2) utilizing a zero-inflated Poisson estimate for Poisson sampling in simulation experiments. We evaluate each solution through simulation studies using female breast, female brain, lung, and cervical cancer data from the Surveillance, Epidemiology, and End Results (SEER) program. We compare the coverage probabilities (CPs) of eleven health disparity measures based on simulated datasets. The truncated normal distribution implemented in the MCS with the standard Poisson samples (the default setting of HD*Calc) leads to less-than-optimal coverage probabilities (<95%). When both the gamma distribution and the estimated mean from the zero-inflated Poisson are used for the MCS, the coverage probabilities are close to the nominal level of 95%. Simulation studies also demonstrate that collapsing age categories for better CI estimation is not a pragmatic solution. Public Library of Science 2019-07-11 /pmc/articles/PMC6622532/ /pubmed/31295305 http://dx.doi.org/10.1371/journal.pone.0219542 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
spellingShingle Research Article
Ahn, Jaeil
Harper, Sam
Yu, Mandi
Feuer, Eric J.
Liu, Benmei
Improved Monte Carlo methods for estimating confidence intervals for eleven commonly used health disparity measures
title Improved Monte Carlo methods for estimating confidence intervals for eleven commonly used health disparity measures
title_full Improved Monte Carlo methods for estimating confidence intervals for eleven commonly used health disparity measures
title_fullStr Improved Monte Carlo methods for estimating confidence intervals for eleven commonly used health disparity measures
title_full_unstemmed Improved Monte Carlo methods for estimating confidence intervals for eleven commonly used health disparity measures
title_short Improved Monte Carlo methods for estimating confidence intervals for eleven commonly used health disparity measures
title_sort improved monte carlo methods for estimating confidence intervals for eleven commonly used health disparity measures
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6622532/
https://www.ncbi.nlm.nih.gov/pubmed/31295305
http://dx.doi.org/10.1371/journal.pone.0219542
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