PMID- 31295305
OWN - NLM
STAT- MEDLINE
DCOM- 20200226
LR  - 20200309
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 14
IP  - 7
DP  - 2019
TI  - Improved Monte Carlo methods for estimating confidence intervals for eleven 
      commonly used health disparity measures.
PG  - e0219542
LID - 10.1371/journal.pone.0219542 [doi]
LID - e0219542
AB  - 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.
FAU - Ahn, Jaeil
AU  - Ahn J
AUID- ORCID: 0000-0001-7998-4759
AD  - Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown 
      University, Washington, DC, United States of America.
FAU - Harper, Sam
AU  - Harper S
AD  - Department of Epidemiology, Biostatistics, and Occupational health, McGill 
      University, Montreal, Quebec, Canada.
FAU - Yu, Mandi
AU  - Yu M
AD  - Division of Cancer Control and Population Sciences, National Cancer Institute, 
      Bethesda, MD, United States of America.
FAU - Feuer, Eric J
AU  - Feuer EJ
AD  - Division of Cancer Control and Population Sciences, National Cancer Institute, 
      Bethesda, MD, United States of America.
FAU - Liu, Benmei
AU  - Liu B
AD  - Division of Cancer Control and Population Sciences, National Cancer Institute, 
      Bethesda, MD, United States of America.
LA  - eng
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20190711
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - Computer Simulation
MH  - *Confidence Intervals
MH  - Healthcare Disparities/*statistics & numerical data
MH  - Humans
MH  - *Monte Carlo Method
MH  - Normal Distribution
MH  - Probability
MH  - Software
PMC - PMC6622532
COIS- The authors have declared that no competing interests exist.
EDAT- 2019/07/12 06:00
MHDA- 2020/02/27 06:00
PMCR- 2019/07/11
CRDT- 2019/07/12 06:00
PHST- 2019/01/15 00:00 [received]
PHST- 2019/06/14 00:00 [accepted]
PHST- 2019/07/12 06:00 [entrez]
PHST- 2019/07/12 06:00 [pubmed]
PHST- 2020/02/27 06:00 [medline]
PHST- 2019/07/11 00:00 [pmc-release]
AID - PONE-D-19-01411 [pii]
AID - 10.1371/journal.pone.0219542 [doi]
PST - epublish
SO  - PLoS One. 2019 Jul 11;14(7):e0219542. doi: 10.1371/journal.pone.0219542. 
      eCollection 2019.