CDC STACKS serves as an archival repository of CDC-published products including scientific findings, journal articles, guidelines, recommendations, or other public health information authored or co-authored by CDC or funded partners.
As a repository, CDC STACKS retains documents in their original published format to ensure public access to scientific information.
i
More accurate semiparametric regression in pharmacogenomics*
-
2018
-
-
Source: Stat Interface. 11(4):573-580
Details:
-
Alternative Title:Stat Interface
-
Personal Author:
-
Description:A key step in pharmacogenomic studies is the development of accurate prediction models for drug response based on individuals' genomic information. Recent interest has centered on semiparametric models based on kernel machine regression, which can flexibly model the complex relationships between gene expression and drug response. However, performance suffers if irrelevant covariates are unknowingly included when training the model. We propose a new semiparametric regression procedure, based on a novel penalized garrotized kernel machine (PGKM), which can better adapt to the presence of irrelevant covariates while still allowing for a complex nonlinear model and gene-gene interactions. We study the performance of our approach in simulations and in a pharmacogenomic study of the renal carcinoma drug temsirolimus. Our method predicts plasma concentration of temsirolimus as well as standard kernel machine regression when no irrelevant covariates are included in training, but has much higher prediction accuracy when the truly important covariates are not known in advance. Supplemental materials, including R code used in this manuscript, are available online.
-
Keywords:
-
Source:
-
Pubmed ID:30815051
-
Pubmed Central ID:PMC6388693
-
Document Type:
-
Funding:
-
Volume:11
-
Issue:4
-
Collection(s):
-
Main Document Checksum:
-
Download URL:
-
File Type: