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Auxiliary variable-enriched biomarker-stratified design.

Publication ,  Journal Article
Wang, T; Wang, X; Zhou, H; Cai, J; George, SL
Published in: Stat Med
December 30, 2018

Clinical trials in the era of precision medicine require assessment of biomarkers to identify appropriate subgroups of patients for targeted therapy. In a biomarker-stratified design (BSD), biomarkers are measured on all patients and used as stratification variables. However, such a trial can be both inefficient and costly, especially when the prevalence of the subgroup of primary interest is low and the cost of assessing the biomarkers is high. Efficiency can be improved and costs reduced by using enriched biomarker-stratified designs, in which patients of primary interest, typically the biomarker-positive patients, are oversampled. We consider a special type of enrichment design, an auxiliary variable-enriched design (AEBSD), in which enrichment is based on some inexpensive auxiliary variable that is positively correlated with the true biomarker. The proposed AEBSD reduces the total cost of the trial compared with a standard BSD when the prevalence rate of true biomarker positivity is small and the positive predictive value (PPV) of the auxiliary biomarker is larger than the prevalence rate. In addition, for an AEBSD, we can immediately randomize the patients selected in the screening process without waiting for the result of the true biomarker test, reducing the treatment waiting time. We propose an adaptive Bayesian method to adjust the assumed PPV while the trial is ongoing. Numerical studies and an example illustrate the approach. An R package is available.

Duke Scholars

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Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

December 30, 2018

Volume

37

Issue

30

Start / End Page

4610 / 4635

Location

England

Related Subject Headings

  • Treatment Outcome
  • Statistics & Probability
  • Randomized Controlled Trials as Topic
  • Precision Medicine
  • Models, Statistical
  • Humans
  • Cost Savings
  • Biomarkers
  • Bayes Theorem
  • 4905 Statistics
 

Citation

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Wang, T., Wang, X., Zhou, H., Cai, J., & George, S. L. (2018). Auxiliary variable-enriched biomarker-stratified design. Stat Med, 37(30), 4610–4635. https://doi.org/10.1002/sim.7938
Wang, Ting, Xiaofei Wang, Haibo Zhou, Jianwen Cai, and Stephen L. George. “Auxiliary variable-enriched biomarker-stratified design.Stat Med 37, no. 30 (December 30, 2018): 4610–35. https://doi.org/10.1002/sim.7938.
Wang T, Wang X, Zhou H, Cai J, George SL. Auxiliary variable-enriched biomarker-stratified design. Stat Med. 2018 Dec 30;37(30):4610–35.
Wang, Ting, et al. “Auxiliary variable-enriched biomarker-stratified design.Stat Med, vol. 37, no. 30, Dec. 2018, pp. 4610–35. Pubmed, doi:10.1002/sim.7938.
Wang T, Wang X, Zhou H, Cai J, George SL. Auxiliary variable-enriched biomarker-stratified design. Stat Med. 2018 Dec 30;37(30):4610–4635.
Journal cover image

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

December 30, 2018

Volume

37

Issue

30

Start / End Page

4610 / 4635

Location

England

Related Subject Headings

  • Treatment Outcome
  • Statistics & Probability
  • Randomized Controlled Trials as Topic
  • Precision Medicine
  • Models, Statistical
  • Humans
  • Cost Savings
  • Biomarkers
  • Bayes Theorem
  • 4905 Statistics