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Evaluation of the treatment time-lag effect for survival data

Author

Listed:
  • Kayoung Park

    (Old Dominion University)

  • Peihua Qiu

    (University of Florida)

Abstract

Medical treatments often take a period of time to reveal their impact on subjects, which is the so-called time-lag effect in the literature. In the survival data analysis literature, most existing methods compare two treatments in the entire study period. In cases when there is a substantial time-lag effect, these methods would not be effective in detecting the difference between the two treatments, because the similarity between the treatments during the time-lag period would diminish their effectiveness. In this paper, we develop a novel modeling approach for estimating the time-lag period and for comparing the two treatments properly after the time-lag effect is accommodated. Theoretical arguments and numerical examples show that it is effective in practice.

Suggested Citation

  • Kayoung Park & Peihua Qiu, 2018. "Evaluation of the treatment time-lag effect for survival data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(2), pages 310-327, April.
    • Handle: RePEc:spr:lifeda:v:24:y:2018:i:2:d:10.1007_s10985-017-9390-7
    DOI: 10.1007/s10985-017-9390-7
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    References listed on IDEAS

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    1. Gregg E. Dinse & Walter W. Piegorsch & Dennis D. Boos, 1993. "Confidence Statements About the Time Range Over Which Survival Curves Differ," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 42(1), pages 21-30, March.
    2. Peihua Qiu & Jun Sheng, 2008. "A two‐stage procedure for comparing hazard rate functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 191-208, February.
    3. Zhongxue Chen & Hanwen Huang & Peihua Qiu, 2016. "Comparison of multiple hazard rate functions," Biometrics, The International Biometric Society, vol. 72(1), pages 39-45, March.
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    Cited by:

    1. Erkmen G. Aslim & Murat C. Mungan & Han Yu, 2024. "A welfare analysis of Medicaid and recidivism," Health Economics, John Wiley & Sons, Ltd., vol. 33(11), pages 2463-2507, November.

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