Testing overall and subpopulation treatment effects with measurement errors

Yanyuan Ma, Guosheng Yin

Research output: Contribution to journalArticle

Abstract

There is a growing interest in the discovery of important predictors from many potential biomarkers for therapeutic use. In particular, a biomarker has predictive value for treatment if the treatment is only effective for patients whose biomarker values exceed a certain threshold. However, biomarker expressions are often subject to measurement errors, which may blur the biomarker's predictive capability in patient classification and, as a consequence, may lead to inappropriate treatment decisions. By taking into account the measurement errors, we propose a new testing procedure for the overall and subpopulation treatment effects in the multiple testing framework. The proposed method bypasses the permutation or other resampling procedures that become computationally infeasible in the presence of measurement errors. We conduct simulation studies to examine the erformance of the proposed method, and illustrate it with a data example.

Original languageEnglish (US)
Pages (from-to)1019-1042
Number of pages24
JournalStatistica Sinica
Volume23
Issue number3
DOIs
StatePublished - Jul 1 2013

Fingerprint

Biomarkers
Treatment Effects
Measurement Error
Testing
Multiple Testing
Resampling
Predictors
Exceed
Permutation
Measurement error
Treatment effects
Simulation Study

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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Testing overall and subpopulation treatment effects with measurement errors. / Ma, Yanyuan; Yin, Guosheng.

In: Statistica Sinica, Vol. 23, No. 3, 01.07.2013, p. 1019-1042.

Research output: Contribution to journalArticle

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