TY - JOUR
T1 - Meta-analysis methods for multiple related markers
T2 - Applications to microbiome studies with the results on multiple α-diversity indices
AU - Koh, Hyunwook
AU - Tuddenham, Susan
AU - Sears, Cynthia L.
AU - Zhao, Ni
N1 - Funding Information:
Johns Hopkins University Center for AIDS Research, 1P30AI094189; National Institute of Health, U24OD023382; National Research Foundation of Korea, NRF‐2021R1C1C1013861 Funding information
Funding Information:
information Johns Hopkins University Center for AIDS Research, 1P30AI094189; National Institute of Health, U24OD023382; National Research Foundation of Korea, NRF-2021R1C1C1013861This study was supported in part by the National Institute of Health (NIH) grants, U24OD023382 (Environmental Influences of Child Health Outcomes [ECHO] Data Analysis Center) and 1P30AI094189 (Johns Hopkins University Center for AIDS Research), and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1C1C1013861). The data used in this study are from work that was supported by the HIV Microbiome Re-analysis Consortium. The contents of the paper are solely the responsibility of the authors and do not necessarily represent the official views of NIH.
Funding Information:
This study was supported in part by the National Institute of Health (NIH) grants, U24OD023382 (Environmental Influences of Child Health Outcomes [ECHO] Data Analysis Center) and 1P30AI094189 (Johns Hopkins University Center for AIDS Research), and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF‐2021R1C1C1013861). The data used in this study are from work that was supported by the HIV Microbiome Re‐analysis Consortium. The contents of the paper are solely the responsibility of the authors and do not necessarily represent the official views of NIH.
Publisher Copyright:
© 2021 John Wiley & Sons, Ltd.
PY - 2021/5/30
Y1 - 2021/5/30
N2 - Meta-analysis is a practical and powerful analytic tool that enables a unified statistical inference across the results from multiple studies. Notably, researchers often report the results on multiple related markers in each study (eg, various α-diversity indices in microbiome studies). However, univariate meta-analyses are limited to combining the results on a single common marker at a time, whereas existing multivariate meta-analyses are limited to the situations where marker-by-marker correlations are given in each study. Thus, here we introduce two meta-analysis methods, multi-marker meta-analysis (mMeta) and adaptive multi-marker meta-analysis (aMeta), to combine multiple studies throughout multiple related markers with no priori results on marker-by-marker correlations. mMeta is a statistical estimator for a pooled estimate and its SE across all the studies and markers, whereas aMeta is a statistical test based on the test statistic of the minimum P-value among marker-specific meta-analyses. mMeta conducts both effect estimation and hypothesis testing based on a weighted average of marker-specific pooled estimates while estimating marker-by-marker correlations non-parametrically via permutations, yet its power is only moderate. In contrast, aMeta closely approaches the highest power among marker-specific meta-analyses, yet it is limited to hypothesis testing. While their applications can be broader, we illustrate the use of mMeta and aMeta to combine microbiome studies throughout multiple α-diversity indices. We evaluate mMeta and aMeta in silico and apply them to real microbiome studies on the disparity in α-diversity by the status of human immunodeficiency virus (HIV) infection. The R package for mMeta and aMeta is freely available at https://github.com/hk1785/mMeta.
AB - Meta-analysis is a practical and powerful analytic tool that enables a unified statistical inference across the results from multiple studies. Notably, researchers often report the results on multiple related markers in each study (eg, various α-diversity indices in microbiome studies). However, univariate meta-analyses are limited to combining the results on a single common marker at a time, whereas existing multivariate meta-analyses are limited to the situations where marker-by-marker correlations are given in each study. Thus, here we introduce two meta-analysis methods, multi-marker meta-analysis (mMeta) and adaptive multi-marker meta-analysis (aMeta), to combine multiple studies throughout multiple related markers with no priori results on marker-by-marker correlations. mMeta is a statistical estimator for a pooled estimate and its SE across all the studies and markers, whereas aMeta is a statistical test based on the test statistic of the minimum P-value among marker-specific meta-analyses. mMeta conducts both effect estimation and hypothesis testing based on a weighted average of marker-specific pooled estimates while estimating marker-by-marker correlations non-parametrically via permutations, yet its power is only moderate. In contrast, aMeta closely approaches the highest power among marker-specific meta-analyses, yet it is limited to hypothesis testing. While their applications can be broader, we illustrate the use of mMeta and aMeta to combine microbiome studies throughout multiple α-diversity indices. We evaluate mMeta and aMeta in silico and apply them to real microbiome studies on the disparity in α-diversity by the status of human immunodeficiency virus (HIV) infection. The R package for mMeta and aMeta is freely available at https://github.com/hk1785/mMeta.
KW - adaptive meta-analysis
KW - meta-analysis for microbiome studies
KW - meta-analysis for α-diversity indices
KW - multi-marker meta-analysis
KW - non-parametric meta-analysis
KW - random effects meta-analysis
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U2 - 10.1002/sim.8940
DO - 10.1002/sim.8940
M3 - Article
C2 - 33768631
AN - SCOPUS:85103203760
VL - 40
SP - 2859
EP - 2876
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
IS - 12
ER -