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A comparison of some confidence intervals for the mean quality-adjusted lifetime with censored data

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  • Wang, Hongkun
  • Zhao, Yichuan

Abstract

Quality-adjusted lifetime (QAL) has been playing an important role in clinical trials to evaluate treatments for chronic diseases. Much research has been focused on estimating the mean QAL when the data are subject to right censoring. The estimated mean QAL is often presented with a confidence interval, obtained by using either the available variance estimator or the bootstrapping method. However, no research has shown which method yields confidence intervals with better coverage probability and shorter length, especially when the sample size is not big. In this paper, we examine the confidence intervals obtained by some available methods for the mean QAL with censored data. Simulation studies are employed to compare the performance of these methods with various sample sizes and different censoring rates. Methods with the best performance will be identified. A data example from a breast cancer clinical trial study is used to illustrate the application of these methods.

Suggested Citation

  • Wang, Hongkun & Zhao, Yichuan, 2009. "A comparison of some confidence intervals for the mean quality-adjusted lifetime with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2733-2739, May.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:7:p:2733-2739
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    References listed on IDEAS

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    1. Mark J. Laan & Alan Hubbard, 1999. "Locally Efficient Estimation of the Quality-Adjusted Lifetime Distribution with Right-Censored Data and Covariates," Biometrics, The International Biometric Society, vol. 55(2), pages 530-536, June.
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    Cited by:

    1. Zhao, Yichuan & Su, Yueju & Yang, Hanfang, 2020. "Jackknife empirical likelihood inference for the Pietra ratio," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    2. de Peretti, Christian & Siani, Carole, 2010. "Graphical methods for investigating the finite-sample properties of confidence regions," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 262-271, February.

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