Multiple Imputation and Random Forests (MIRF) for Unobservable, High-Dimensional Data

Nonyane Bareng A. S. and Foulkes Andrea S.
Additional contact information
Nonyane Bareng A. S.: University of Massachusetts, Amherst
Foulkes Andrea S.: University of Massachusetts, Amherst

The International Journal of Biostatistics, 2007, vol. 3, issue 1, 19

Abstract: Understanding the genetic underpinnings to complex diseases requires consideration of sophisticated analytical methods designed to uncover intricate associations across multiple predictor variables. At the same time, knowledge of whether single nucleotide polymorphisms within a gene are on the same (in cis) or on different (in trans) chromosomal copies, may provide crucial information about measures of disease progression. In association studies of unrelated individuals, allelic phase is generally unobservable, generating an additional analytical challenge. In this manuscript, we describe a novel approach that combines multiple imputation and random forests for this high-dimensional, unobservable data setting. An application to a cohort of HIV-1 infected individuals receiving anti-retroviral therapies is presented. A simulation study is also presented to characterize method performance.

Keywords: recursive partitioning; random forests; haplotype; genotype; phase; HIV-1; lipids (search for similar items in EconPapers)
Date: 2007
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://doi.org/10.2202/1557-4679.1049 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:ijbist:v:3:y:2007:i:1:n:12

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/ijb/html

DOI: 10.2202/1557-4679.1049

Access Statistics for this article

The International Journal of Biostatistics is currently edited by Antoine Chambaz, Alan E. Hubbard and Mark J. van der Laan

More articles in The International Journal of Biostatistics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().