schliessen

Filtern

 

Bibliotheken

On Bayesian methods of exploring qualitative interactions for targeted treatment

Providing personalized treatments designed to maximize benefits and minimizing harms is of tremendous current medical interest. One problem in this area is the evaluation of the interaction between the treatment and other predictor variables. Treatment effects in subgroups having the same direction... Full description

Journal Title: Statistics in Medicine 10 December 2012, Vol.31(28), pp.3693-3707
Main Author: Chen, Wei
Other Authors: Ghosh, Debashis , Raghunathan, Trivellore E. , Norkin, Maxim , Sargent, Daniel J. , Bepler, Gerold
Format: Electronic Article Electronic Article
Language:
Subjects:
ID: ISSN: 0277-6715 ; E-ISSN: 1097-0258 ; DOI: 10.1002/sim.5429
Zum Text:
SendSend as email Add to Book BagAdd to Book Bag
Staff View
recordid: wj10.1002/sim.5429
title: On Bayesian methods of exploring qualitative interactions for targeted treatment
format: Article
creator:
  • Chen, Wei
  • Ghosh, Debashis
  • Raghunathan, Trivellore E.
  • Norkin, Maxim
  • Sargent, Daniel J.
  • Bepler, Gerold
subjects:
  • Interaction
  • Subgroup
  • Predictive Marker
  • Prognostic Marker
  • Clinical Trial
ispartof: Statistics in Medicine, 10 December 2012, Vol.31(28), pp.3693-3707
description: Providing personalized treatments designed to maximize benefits and minimizing harms is of tremendous current medical interest. One problem in this area is the evaluation of the interaction between the treatment and other predictor variables. Treatment effects in subgroups having the same direction but different magnitudes are called quantitative interactions, whereas those having opposite directions in subgroups are called qualitative interactions (QIs). Identifying QIs is challenging because they are rare and usually unknown among many potential biomarkers. Meanwhile, subgroup analysis reduces the power of hypothesis testing and multiple subgroup analyses inflate the type I error rate. We propose a new Bayesian approach to search for QI in a multiple regression setting with adaptive decision rules. We consider various regression models for the outcome. We illustrate this method in two examples of phase III clinical trials. The algorithm is straightforward and easy to implement using existing software packages. We provide a sample code in Appendix A. Copyright © 2012 John Wiley & Sons, Ltd.
language:
source:
identifier: ISSN: 0277-6715 ; E-ISSN: 1097-0258 ; DOI: 10.1002/sim.5429
fulltext: fulltext
issn:
  • 0277-6715
  • 02776715
  • 1097-0258
  • 10970258
url: Link


@attributes
ID1627189656
RANK0.07
NO1
SEARCH_ENGINEprimo_central_multiple_fe
SEARCH_ENGINE_TYPEPrimo Central Search Engine
LOCALfalse
PrimoNMBib
record
control
sourcerecordid10.1002/sim.5429
sourceidwj
recordidTN_wj10.1002/sim.5429
sourcesystemOther
pqid1208789878
galeid310512888
display
typearticle
titleOn Bayesian methods of exploring qualitative interactions for targeted treatment
creatorChen, Wei ; Ghosh, Debashis ; Raghunathan, Trivellore E. ; Norkin, Maxim ; Sargent, Daniel J. ; Bepler, Gerold
ispartofStatistics in Medicine, 10 December 2012, Vol.31(28), pp.3693-3707
identifier
subjectInteraction ; Subgroup ; Predictive Marker ; Prognostic Marker ; Clinical Trial
descriptionProviding personalized treatments designed to maximize benefits and minimizing harms is of tremendous current medical interest. One problem in this area is the evaluation of the interaction between the treatment and other predictor variables. Treatment effects in subgroups having the same direction but different magnitudes are called quantitative interactions, whereas those having opposite directions in subgroups are called qualitative interactions (QIs). Identifying QIs is challenging because they are rare and usually unknown among many potential biomarkers. Meanwhile, subgroup analysis reduces the power of hypothesis testing and multiple subgroup analyses inflate the type I error rate. We propose a new Bayesian approach to search for QI in a multiple regression setting with adaptive decision rules. We consider various regression models for the outcome. We illustrate this method in two examples of phase III clinical trials. The algorithm is straightforward and easy to implement using existing software packages. We provide a sample code in Appendix A. Copyright © 2012 John Wiley & Sons, Ltd.
source
version7
lds50peer_reviewed
links
openurl$$Topenurl_article
openurlfulltext$$Topenurlfull_article
search
creatorcontrib
0Chen, Wei
1Ghosh, Debashis
2Raghunathan, Trivellore E.
3Norkin, Maxim
4Sargent, Daniel J.
5Bepler, Gerold
titleOn Bayesian methods of exploring qualitative interactions for targeted treatment
descriptionProviding personalized treatments designed to maximize benefits and minimizing harms is of tremendous current medical interest. One problem in this area is the evaluation of the interaction between the treatment and other predictor variables. Treatment effects in subgroups having the same direction but different magnitudes are called quantitative interactions, whereas those having opposite directions in subgroups are called qualitative interactions (QIs). Identifying QIs is challenging because they are rare and usually unknown among many potential biomarkers. Meanwhile, subgroup analysis reduces the power of hypothesis testing and multiple subgroup analyses inflate the type I error rate. We propose a new Bayesian approach to search for QI in a multiple regression setting with adaptive decision rules. We consider various regression models for the outcome. We illustrate this method in two examples of phase III clinical trials. The algorithm is straightforward and easy to implement using existing software packages. We provide a sample code in Appendix A. Copyright © 2012 John Wiley & Sons, Ltd.
subject
0Interaction
1Subgroup
2Predictive Marker
3Prognostic Marker
4Clinical Trial
general
0John Wiley & Sons, Ltd
110.1002/sim.5429
2Wiley Online Library
sourceidwj
recordidwj10.1002/sim.5429
issn
00277-6715
102776715
21097-0258
310970258
rsrctypearticle
creationdate2012
addtitle
0Statistics in Medicine
1Statist. Med.
searchscope
0wj
1wiley
scope
0wj
1wiley
lsr30VSR-Enriched:[pqid, galeid, pages]
sort
titleOn Bayesian methods of exploring qualitative interactions for targeted treatment
authorChen, Wei ; Ghosh, Debashis ; Raghunathan, Trivellore E. ; Norkin, Maxim ; Sargent, Daniel J. ; Bepler, Gerold
creationdate20121210
facets
frbrgroupid7928511316576004981
frbrtype5
creationdate2012
topic
0Interaction
1Subgroup
2Predictive Marker
3Prognostic Marker
4Clinical Trial
collectionWiley Online Library
prefilterarticles
rsrctypearticles
creatorcontrib
0Chen, Wei
1Ghosh, Debashis
2Raghunathan, Trivellore E.
3Norkin, Maxim
4Sargent, Daniel J.
5Bepler, Gerold
jtitleStatistics in Medicine
toplevelpeer_reviewed
delivery
delcategoryRemote Search Resource
fulltextfulltext
addata
aulast
0Chen
1Ghosh
2Raghunathan
3Norkin
4Sargent
5Bepler
aufirst
0Wei
1Debashis
2Trivellore E.
3Maxim
4Daniel J.
5Gerold
au
0Chen, Wei
1Ghosh, Debashis
2Raghunathan, Trivellore E.
3Norkin, Maxim
4Sargent, Daniel J.
5Bepler, Gerold
atitleOn Bayesian methods of exploring qualitative interactions for targeted treatment
jtitleStatistics in Medicine
risdate20121210
volume31
issue28
spage3693
epage3707
issn0277-6715
eissn1097-0258
genrearticle
ristypeJOUR
abstractProviding personalized treatments designed to maximize benefits and minimizing harms is of tremendous current medical interest. One problem in this area is the evaluation of the interaction between the treatment and other predictor variables. Treatment effects in subgroups having the same direction but different magnitudes are called quantitative interactions, whereas those having opposite directions in subgroups are called qualitative interactions (QIs). Identifying QIs is challenging because they are rare and usually unknown among many potential biomarkers. Meanwhile, subgroup analysis reduces the power of hypothesis testing and multiple subgroup analyses inflate the type I error rate. We propose a new Bayesian approach to search for QI in a multiple regression setting with adaptive decision rules. We consider various regression models for the outcome. We illustrate this method in two examples of phase III clinical trials. The algorithm is straightforward and easy to implement using existing software packages. We provide a sample code in Appendix A. Copyright © 2012 John Wiley & Sons, Ltd.
copChichester, UK
pubJohn Wiley & Sons, Ltd
doi10.1002/sim.5429
pages3693-707
date2012-12-10