10.6084/m9.figshare.4488191.v1
Lu C.
Lu
C.
O'Connor G.T.
O'Connor
G.T.
Dupuis J.
Dupuis
J.
Kolaczyk E.D.
Kolaczyk
E.D.
Supplementary Material for: Meta-Analysis for Penalized Regression Methods with Multi-Cohort Genome-Wide Association Studies
Karger Publishers
2016
Meta-analysis
Multi-cohorts
Penalized regression
Data splitting
2016-12-21 16:28:47
Dataset
https://karger.figshare.com/articles/dataset/Supplementary_Material_for_Meta-Analysis_for_Penalized_Regression_Methods_with_Multi-Cohort_Genome-Wide_Association_Studies/4488191
<p><strong><em>Objective:</em></strong> Penalized regression has been successfully
applied in genome-wide association studies. While meta-analysis is often
conducted to increase power and protect patients' confidentiality,
methods for meta-analyzing results of penalized regression in
multi-cohort setting are still under development. <b><i>Methods:</i></b>
We propose to use a data-splitting method to obtain valid p values (or
equivalently, coefficient estimates and standard errors) for
meta-analysis across multiple cohorts. We examine two ways of splitting
data in multi-cohort setting and propose three methods to conduct
meta-analysis based on p values. We compare the three meta-analysis
methods to mega-analysis, which consists of pooling individual level
data. We also apply our proposed meta-analysis approaches to the
Framingham Heart Study data, where we divide the original dataset into
four parts to create a multi-cohort scenario. <b><i>Results:</i></b> The
simulations suggest that splitting cohorts has better performance than
splitting data within each cohort. The real data application also shows
that this method provides results that are similar to the mega-analysis.
<b><i>Conclusion:</i></b> After comparing the three methods that we
proposed to conduct meta-analysis, we recommend splitting cohorts rather
than datasets to obtain valid p values for meta-analysis of results
from penalized regression in multi-cohort setting.</p>