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Estimating a unitary effect summary based on combined survival and quantitative outcomes

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  • Lin, Huazhen
  • Li, Yi
  • Tan, Ming T.

Abstract

In practice, when both survival and quantitative outcomes are of interest, we encounter outcomes of mixed type: a censored outcome and a quantitative outcome. Joint modeling of the survival and quantitative outcomes rather than analyzing the outcomes separately has become a method of choice for analyzing mixed outcome data because of improved efficiency. However, the joint modeling provides two separate indexes for measuring the covariate (e.g., treatment) effect, making its interpretation difficult when the covariate inconsistently affects the quantitative and survival outcomes. By assigning a single rank to each outcome to represent the disease severity, this paper provides a unitary effect summary of the covariates on mixed outcome data while accounting for censoring. The method is applied to an analysis of the AIDS Clinical Trials Group protocol 175 (ACTG 175) data.

Suggested Citation

  • Lin, Huazhen & Li, Yi & Tan, Ming T., 2013. "Estimating a unitary effect summary based on combined survival and quantitative outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 129-139.
    • Handle: RePEc:eee:csdana:v:66:y:2013:i:c:p:129-139
    DOI: 10.1016/j.csda.2013.03.028
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    References listed on IDEAS

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    1. Peter Diggle & Daniel Farewell & Robin Henderson, 2007. "Analysis of longitudinal data with drop‐out: objectives, assumptions and a proposal," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(5), pages 499-550, November.
    2. Mel‐Ling Ting Lee & Victor DeGruttola & David Schoenfeld, 2000. "A model for markers and latent health status," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 747-762.
    3. Sin-Ho Jung, 2003. "Rank-based regression with repeated measurements data," Biometrika, Biometrika Trust, vol. 90(3), pages 732-740, September.
    4. Han, Aaron K., 1987. "Non-parametric analysis of a generalized regression model : The maximum rank correlation estimator," Journal of Econometrics, Elsevier, vol. 35(2-3), pages 303-316, July.
    5. Khan, Shakeeb & Tamer, Elie, 2007. "Partial rank estimation of duration models with general forms of censoring," Journal of Econometrics, Elsevier, vol. 136(1), pages 251-280, January.
    6. Jane Xu & Scott L. Zeger, 2001. "Joint analysis of longitudinal data comprising repeated measures and times to events," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 375-387.
    7. You-Gan Wang & Min Zhu, 2006. "Rank-based regression for analysis of repeated measures," Biometrika, Biometrika Trust, vol. 93(2), pages 459-464, June.
    8. Sherman, Robert P, 1993. "The Limiting Distribution of the Maximum Rank Correlation Estimator," Econometrica, Econometric Society, vol. 61(1), pages 123-137, January.
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    Cited by:

    1. Yuan, Ao & Zheng, Yanxun & Huang, Peng & Tan, Ming T., 2016. "A nonparametric test for the evaluation of group sequential clinical trials with covariate information," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 82-99.

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