2022
DOI: 10.48550/arxiv.2211.00407
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Missing data interpolation in integrative multi-cohort analysis with disparate covariate information

Abstract: Integrative analysis of datasets generated by multiple cohorts is a widely-used approach for increasing sample size, precision of population estimators, and generalizability of analysis results in epidemiological studies. However, often each individual cohort dataset does not have all variables of interest for an integrative analysis collected as a part of an original study. Such cohort-level missingness poses methodological challenges to the integrative analysis since missing variables have traditionally: (1)… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 6 publications
(6 reference statements)
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?