Selecting optimal treatments based on predictive factors

Eric Polley, Mark J. van der Laan

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)

Abstract

With the increasing interest in individualized medicine there is a greater need for robust statistical methods for prediction of optimal treatment based on the patient’s characteristics. When evaluating two treatments, one treatment may not be uniformly superior to the other treatment for all patients. A patient characteristic may interact with one of the treatments and change the effect of the treatment on the response. Clinical trials are also collecting more information on the patient. This additional information on the patients combined with the state of the art in model selection allows researchers to build better optimal treatment algorithms. In this chapter we introduce a methodology for predicting optimal treatment. The methodology is demonstrated first on a simulation and then on a phase III clinical trial in neuro-oncology.

Original languageEnglish (US)
Title of host publicationDesign and Analysis of Clinical Trials with Time-to-Event Endpoints
PublisherCRC Press
Pages441-454
Number of pages14
ISBN (Electronic)9781420066401
ISBN (Print)9781420066395
StatePublished - Jan 1 2009
Externally publishedYes

Fingerprint

Therapeutics
Clinical Trials
Oncology
Phase III Clinical Trials
Precision Medicine
Methodology
Robust Methods
Model Selection
Statistical method
Medicine
Research Personnel
Prediction
Simulation

ASJC Scopus subject areas

  • Mathematics(all)
  • Medicine(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)

Cite this

Polley, E., & van der Laan, M. J. (2009). Selecting optimal treatments based on predictive factors. In Design and Analysis of Clinical Trials with Time-to-Event Endpoints (pp. 441-454). CRC Press.

Selecting optimal treatments based on predictive factors. / Polley, Eric; van der Laan, Mark J.

Design and Analysis of Clinical Trials with Time-to-Event Endpoints. CRC Press, 2009. p. 441-454.

Research output: Chapter in Book/Report/Conference proceedingChapter

Polley, E & van der Laan, MJ 2009, Selecting optimal treatments based on predictive factors. in Design and Analysis of Clinical Trials with Time-to-Event Endpoints. CRC Press, pp. 441-454.
Polley E, van der Laan MJ. Selecting optimal treatments based on predictive factors. In Design and Analysis of Clinical Trials with Time-to-Event Endpoints. CRC Press. 2009. p. 441-454
Polley, Eric ; van der Laan, Mark J. / Selecting optimal treatments based on predictive factors. Design and Analysis of Clinical Trials with Time-to-Event Endpoints. CRC Press, 2009. pp. 441-454
@inbook{c75a5aa0d4c4438ba302fe3c7ad56b03,
title = "Selecting optimal treatments based on predictive factors",
abstract = "With the increasing interest in individualized medicine there is a greater need for robust statistical methods for prediction of optimal treatment based on the patient’s characteristics. When evaluating two treatments, one treatment may not be uniformly superior to the other treatment for all patients. A patient characteristic may interact with one of the treatments and change the effect of the treatment on the response. Clinical trials are also collecting more information on the patient. This additional information on the patients combined with the state of the art in model selection allows researchers to build better optimal treatment algorithms. In this chapter we introduce a methodology for predicting optimal treatment. The methodology is demonstrated first on a simulation and then on a phase III clinical trial in neuro-oncology.",
author = "Eric Polley and {van der Laan}, {Mark J.}",
year = "2009",
month = "1",
day = "1",
language = "English (US)",
isbn = "9781420066395",
pages = "441--454",
booktitle = "Design and Analysis of Clinical Trials with Time-to-Event Endpoints",
publisher = "CRC Press",

}

TY - CHAP

T1 - Selecting optimal treatments based on predictive factors

AU - Polley, Eric

AU - van der Laan, Mark J.

PY - 2009/1/1

Y1 - 2009/1/1

N2 - With the increasing interest in individualized medicine there is a greater need for robust statistical methods for prediction of optimal treatment based on the patient’s characteristics. When evaluating two treatments, one treatment may not be uniformly superior to the other treatment for all patients. A patient characteristic may interact with one of the treatments and change the effect of the treatment on the response. Clinical trials are also collecting more information on the patient. This additional information on the patients combined with the state of the art in model selection allows researchers to build better optimal treatment algorithms. In this chapter we introduce a methodology for predicting optimal treatment. The methodology is demonstrated first on a simulation and then on a phase III clinical trial in neuro-oncology.

AB - With the increasing interest in individualized medicine there is a greater need for robust statistical methods for prediction of optimal treatment based on the patient’s characteristics. When evaluating two treatments, one treatment may not be uniformly superior to the other treatment for all patients. A patient characteristic may interact with one of the treatments and change the effect of the treatment on the response. Clinical trials are also collecting more information on the patient. This additional information on the patients combined with the state of the art in model selection allows researchers to build better optimal treatment algorithms. In this chapter we introduce a methodology for predicting optimal treatment. The methodology is demonstrated first on a simulation and then on a phase III clinical trial in neuro-oncology.

UR - http://www.scopus.com/inward/record.url?scp=85056505255&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85056505255&partnerID=8YFLogxK

M3 - Chapter

AN - SCOPUS:85056505255

SN - 9781420066395

SP - 441

EP - 454

BT - Design and Analysis of Clinical Trials with Time-to-Event Endpoints

PB - CRC Press

ER -