Optimal two-stage enrichment design correcting for biomarker misclassification

Yong Zang, Beibei Guo

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The enrichment design is an important clinical trial design to detect the treatment effect of the molecularly targeted agent (MTA) in personalized medicine. Under this design, patients are stratified into marker-positive and marker-negative subgroups based on their biomarker statuses and only the marker-positive patients are enrolled into the trial and randomized to receive either the MTA or a standard treatment. As the biomarker plays a key role in determining the enrollment of the trial, a misclassification of the biomarker can induce substantial bias, undermine the integrity of the trial, and seriously affect the treatment evaluation. In this paper, we propose a two-stage optimal enrichment design that utilizes the surrogate marker to correct for the biomarker misclassification. The proposed design is optimal in the sense that it maximizes the probability of correctly classifying each patient’s biomarker status based on the surrogate marker information. In addition, after analytically deriving the bias caused by the biomarker misclassification, we develop a likelihood ratio test based on the EM algorithm to correct for such bias. We conduct comprehensive simulation studies to investigate the operating characteristics of the optimal design and the results confirm the desirable performance of the proposed design.

Original languageEnglish (US)
Pages (from-to)35-47
Number of pages13
JournalStatistical Methods in Medical Research
Volume27
Issue number1
DOIs
StatePublished - Jan 1 2018
Externally publishedYes

Fingerprint

Misclassification
Biomarkers
Surrogate Markers
Operating Characteristics
Treatment Effects
Likelihood Ratio Test
EM Algorithm
Clinical Trials
Integrity
Medicine
Precision Medicine
Design
Maximise
Simulation Study
Subgroup
Therapeutics
Evaluation

Keywords

  • Biomarker
  • Clinical trial
  • Enrichment design
  • Measurement error
  • Optimal design
  • Personalized medicine

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

Cite this

Optimal two-stage enrichment design correcting for biomarker misclassification. / Zang, Yong; Guo, Beibei.

In: Statistical Methods in Medical Research, Vol. 27, No. 1, 01.01.2018, p. 35-47.

Research output: Contribution to journalArticle

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