A multiple testing procedure to proteomic biomarker for disease status

When hundreds of hypotheses are tested simultaneously, the chance of false positives is greatly increased. The article first removed the proteins that contain more than 5 missing values for the active group and 11 for the inactive group. Applying the random seed = 20 and 1,000 bootstrap iterations t...

Full description

Saved in:
Bibliographic Details
Published in:Canadian journal of statistics 2011-06, Vol.39 (2), p.208-209
Main Authors: Zhihui (Amy) LIU, MALIK, Rajat
Format: Article
Language:English
Subjects:
Applied statistics
Biological markers
Biomarkers
Case studies in data analysis
Diagnosis
Diseases
Error correction models
Mathematical procedures
Pathology
Patients
Probability
Proteins
Proteomics
Studies
Test methods
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:When hundreds of hypotheses are tested simultaneously, the chance of false positives is greatly increased. The article first removed the proteins that contain more than 5 missing values for the active group and 11 for the inactive group. Applying the random seed = 20 and 1,000 bootstrap iterations to the imputed data, the article found that the results from the four imputation methods are similar and they agree with those without imputation. The results from the multiple testing procedures before the imputation agree more or less with those after the imputation. The reason for this is unclear, it could mean either that the imputation works very well or that it is not very helpful. There is no evidence that race, sex or age is associated with disease status.
ISSN:0319-5724
1708-945X
DOI:10.1002/cjs