Semi-parametric hybrid empirical likelihood inference for two-sample comparison with censored data

Haiyan Su, Mai Zhou, Hua Liang

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Two-sample comparison problems are often encountered in practical projects and have widely been studied in literature. Owing to practical demands, the research for this topic under special settings such as a semiparametric framework have also attracted great attentions. Zhou and Liang (Biometrika 92:271-282, 2005) proposed an empirical likelihood-based semi-parametric inference for the comparison of treatment effects in a two-sample problem with censored data. However, their approach is actually a pseudo-empirical likelihood and the method may not be fully efficient. In this study, we develop a new empirical likelihood-based inference under more general framework by using the hazard formulation of censored data for two sample semi-parametric hybrid models. We demonstrate that our empirical likelihood statistic converges to a standard chi-squared distribution under the null hypothesis. We further illustrate the use of the proposed test by testing the ROC curve with censored data, among others. Numerical performance of the proposed method is also examined.

Original languageEnglish
Pages (from-to)533-551
Number of pages19
JournalLifetime Data Analysis
Volume17
Issue number4
DOIs
StatePublished - 1 Oct 2011

Fingerprint

Likelihood Inference
Empirical Likelihood
Censored Data
Hazards
Statistics
Testing
ROC Curve
Semiparametric Inference
Two-sample Problem
Chi-squared distribution
Semiparametric Model
Receiver Operating Characteristic Curve
Hybrid Model
Treatment Effects
Research
Null hypothesis
Hazard
Statistic
Converge
Formulation

Keywords

  • Equality of medians
  • Log-rank test
  • Profile likelihood
  • ROC curves
  • Treatment effects

Cite this

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Semi-parametric hybrid empirical likelihood inference for two-sample comparison with censored data. / Su, Haiyan; Zhou, Mai; Liang, Hua.

In: Lifetime Data Analysis, Vol. 17, No. 4, 01.10.2011, p. 533-551.

Research output: Contribution to journalArticleResearchpeer-review

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