Evaluating marker-guided treatment selection strategies.

dc.contributor.author

Matsouaka, Roland A

dc.contributor.author

Li, Junlong

dc.contributor.author

Cai, Tianxi

dc.coverage.spatial

United States

dc.date.accessioned

2015-12-03T20:29:14Z

dc.date.issued

2014-09

dc.description.abstract

A potential venue to improve healthcare efficiency is to effectively tailor individualized treatment strategies by incorporating patient level predictor information such as environmental exposure, biological, and genetic marker measurements. Many useful statistical methods for deriving individualized treatment rules (ITR) have become available in recent years. Prior to adopting any ITR in clinical practice, it is crucial to evaluate its value in improving patient outcomes. Existing methods for quantifying such values mainly consider either a single marker or semi-parametric methods that are subject to bias under model misspecification. In this article, we consider a general setting with multiple markers and propose a two-step robust method to derive ITRs and evaluate their values. We also propose procedures for comparing different ITRs, which can be used to quantify the incremental value of new markers in improving treatment selection. While working models are used in step I to approximate optimal ITRs, we add a layer of calibration to guard against model misspecification and further assess the value of the ITR non-parametrically, which ensures the validity of the inference. To account for the sampling variability of the estimated rules and their corresponding values, we propose a resampling procedure to provide valid confidence intervals for the value functions as well as for the incremental value of new markers for treatment selection. Our proposals are examined through extensive simulation studies and illustrated with the data from a clinical trial that studies the effects of two drug combinations on HIV-1 infected patients.

dc.identifier

http://www.ncbi.nlm.nih.gov/pubmed/24779731

dc.identifier.eissn

1541-0420

dc.identifier.uri

https://hdl.handle.net/10161/11065

dc.language

eng

dc.relation.ispartof

Biometrics

dc.relation.isversionof

10.1111/biom.12179

dc.subject

Biomarker‐analysis design

dc.subject

Counterfactual outcome

dc.subject

Personalized medicine

dc.subject

Perturbation‐resampling

dc.subject

Predictive biomarkers

dc.subject

Subgroup analysis

dc.subject

Acquired Immunodeficiency Syndrome

dc.subject

Anti-HIV Agents

dc.subject

Antigens, CD4

dc.subject

Biomarkers

dc.subject

Data Interpretation, Statistical

dc.subject

Drug Combinations

dc.subject

Humans

dc.subject

Outcome Assessment (Health Care)

dc.subject

Prevalence

dc.subject

Prognosis

dc.subject

Reproducibility of Results

dc.subject

Sensitivity and Specificity

dc.subject

Treatment Outcome

dc.title

Evaluating marker-guided treatment selection strategies.

dc.type

Journal article

duke.contributor.orcid

Matsouaka, Roland A|0000-0002-0271-5400

pubs.author-url

http://www.ncbi.nlm.nih.gov/pubmed/24779731

pubs.begin-page

489

pubs.end-page

499

pubs.issue

3

pubs.organisational-group

Basic Science Departments

pubs.organisational-group

Biostatistics & Bioinformatics

pubs.organisational-group

Duke

pubs.organisational-group

Duke Clinical Research Institute

pubs.organisational-group

Institutes and Centers

pubs.organisational-group

School of Medicine

pubs.publication-status

Published

pubs.volume

70

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
published_paper.pdf
Size:
225.92 KB
Format:
Adobe Portable Document Format
Description:
Published version