Bayesian Enrichment Strategies for Randomized Discontinuation Trials

Lorenzo Trippa, Gary Rosner, Peter Müller

Research output: Contribution to journalArticle

Abstract

We propose optimal choice of the design parameters for random discontinuation designs (RDD) using a Bayesian decision-theoretic approach. We consider applications of RDDs to oncology phase II studies evaluating activity of cytostatic agents. The design consists of two stages. The preliminary open-label stage treats all patients with the new agent and identifies a possibly sensitive subpopulation. The subsequent second stage randomizes, treats, follows, and compares outcomes among patients in the identified subgroup, with randomization to either the new or a control treatment. Several tuning parameters characterize the design: the number of patients in the trial, the duration of the preliminary stage, and the duration of follow-up after randomization. We define a probability model for tumor growth, specify a suitable utility function, and develop a computational procedure for selecting the optimal tuning parameters.

Original languageEnglish (US)
Pages (from-to)203-211
Number of pages9
JournalBiometrics
Volume68
Issue number1
DOIs
StatePublished - Mar 2012

Fingerprint

Randomized Trial
Parameter Tuning
Randomisation
Random Allocation
Random Design
Oncology
Tumor Growth
Probability Model
Parameter Design
Utility Function
Tuning
Cytostatic Agents
Subgroup
utility functions
duration
Labels
Tumors
Growth
neoplasms
Design

Keywords

  • Clinical trials
  • Enrichment designs
  • Randomized discontinuation design
  • Tumor growth models

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability
  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Medicine(all)

Cite this

Bayesian Enrichment Strategies for Randomized Discontinuation Trials. / Trippa, Lorenzo; Rosner, Gary; Müller, Peter.

In: Biometrics, Vol. 68, No. 1, 03.2012, p. 203-211.

Research output: Contribution to journalArticle

Trippa, Lorenzo ; Rosner, Gary ; Müller, Peter. / Bayesian Enrichment Strategies for Randomized Discontinuation Trials. In: Biometrics. 2012 ; Vol. 68, No. 1. pp. 203-211.
@article{cf09a4e9417c429293f98c5838841036,
title = "Bayesian Enrichment Strategies for Randomized Discontinuation Trials",
abstract = "We propose optimal choice of the design parameters for random discontinuation designs (RDD) using a Bayesian decision-theoretic approach. We consider applications of RDDs to oncology phase II studies evaluating activity of cytostatic agents. The design consists of two stages. The preliminary open-label stage treats all patients with the new agent and identifies a possibly sensitive subpopulation. The subsequent second stage randomizes, treats, follows, and compares outcomes among patients in the identified subgroup, with randomization to either the new or a control treatment. Several tuning parameters characterize the design: the number of patients in the trial, the duration of the preliminary stage, and the duration of follow-up after randomization. We define a probability model for tumor growth, specify a suitable utility function, and develop a computational procedure for selecting the optimal tuning parameters.",
keywords = "Clinical trials, Enrichment designs, Randomized discontinuation design, Tumor growth models",
author = "Lorenzo Trippa and Gary Rosner and Peter M{\"u}ller",
year = "2012",
month = "3",
doi = "10.1111/j.1541-0420.2011.01623.x",
language = "English (US)",
volume = "68",
pages = "203--211",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "1",

}

TY - JOUR

T1 - Bayesian Enrichment Strategies for Randomized Discontinuation Trials

AU - Trippa, Lorenzo

AU - Rosner, Gary

AU - Müller, Peter

PY - 2012/3

Y1 - 2012/3

N2 - We propose optimal choice of the design parameters for random discontinuation designs (RDD) using a Bayesian decision-theoretic approach. We consider applications of RDDs to oncology phase II studies evaluating activity of cytostatic agents. The design consists of two stages. The preliminary open-label stage treats all patients with the new agent and identifies a possibly sensitive subpopulation. The subsequent second stage randomizes, treats, follows, and compares outcomes among patients in the identified subgroup, with randomization to either the new or a control treatment. Several tuning parameters characterize the design: the number of patients in the trial, the duration of the preliminary stage, and the duration of follow-up after randomization. We define a probability model for tumor growth, specify a suitable utility function, and develop a computational procedure for selecting the optimal tuning parameters.

AB - We propose optimal choice of the design parameters for random discontinuation designs (RDD) using a Bayesian decision-theoretic approach. We consider applications of RDDs to oncology phase II studies evaluating activity of cytostatic agents. The design consists of two stages. The preliminary open-label stage treats all patients with the new agent and identifies a possibly sensitive subpopulation. The subsequent second stage randomizes, treats, follows, and compares outcomes among patients in the identified subgroup, with randomization to either the new or a control treatment. Several tuning parameters characterize the design: the number of patients in the trial, the duration of the preliminary stage, and the duration of follow-up after randomization. We define a probability model for tumor growth, specify a suitable utility function, and develop a computational procedure for selecting the optimal tuning parameters.

KW - Clinical trials

KW - Enrichment designs

KW - Randomized discontinuation design

KW - Tumor growth models

UR - http://www.scopus.com/inward/record.url?scp=84858864984&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84858864984&partnerID=8YFLogxK

U2 - 10.1111/j.1541-0420.2011.01623.x

DO - 10.1111/j.1541-0420.2011.01623.x

M3 - Article

C2 - 21714780

AN - SCOPUS:84858864984

VL - 68

SP - 203

EP - 211

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 1

ER -