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

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  • Jaeil Ahn
  • Sam Harper
  • Mandi Yu
  • Eric J Feuer
  • Benmei Liu

Abstract

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 (

Suggested Citation

  • Jaeil Ahn & Sam Harper & Mandi Yu & Eric J Feuer & Benmei Liu, 2019. "Improved Monte Carlo methods for estimating confidence intervals for eleven commonly used health disparity measures," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-8, July.
    • Handle: RePEc:plo:pone00:0219542
    DOI: 10.1371/journal.pone.0219542
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    Cited by:

    1. Tengfei Li & Anca D. Dragomir & George Luta, 2024. "A Comparison of Statistical Methods to Construct Confidence Intervals and Fiducial Intervals for Measures of Health Disparities," IJERPH, MDPI, vol. 21(2), pages 1-17, February.

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