Analysis of multiple diverse phenotypes via semiparametric canonical correlation analysis

Denis Agniel and Tianxi Cai

Biometrics, 2017, vol. 73, issue 4, 1254-1265

Abstract: Studying multiple outcomes simultaneously allows researchers to begin to identify underlying factors that affect all of a set of diseases (i.e., shared etiology) and what may give rise to differences in disorders between patients (i.e., disease subtypes). In this work, our goal is to build risk scores that are predictive of multiple phenotypes simultaneously and identify subpopulations at high risk of multiple phenotypes. Such analyses could yield insight into etiology or point to treatment and prevention strategies. The standard canonical correlation analysis (CCA) can be used to relate multiple continuous outcomes to multiple predictors. However, in order to capture the full complexity of a disorder, phenotypes may include a diverse range of data types, including binary, continuous, ordinal, and censored variables. When phenotypes are diverse in this way, standard CCA is not possible and no methods currently exist to model them jointly. In the presence of such complications, we propose a semi‐parametric CCA method to develop risk scores that are predictive of multiple phenotypes. To guard against potential model mis‐specification, we also propose a nonparametric calibration method to identify subgroups that are at high risk of multiple disorders. A resampling procedure is also developed to account for the variability in these estimates. Our method opens the door to synthesizing a wide array of data sources for the purposes of joint prediction.

Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/biom.12690

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:bla:biomet:v:73:y:2017:i:4:p:1254-1265

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0006-341X

Access Statistics for this article

More articles in Biometrics from The International Biometric Society
Bibliographic data for series maintained by Wiley Content Delivery ().