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Estimation Methods in Clinical Trials with Randomly Censored Exponential Healing Times and Rayleigh Dropout Times

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  • Neha Goel

    (Department of Statistics, Ch. Charan Singh University, India)

Abstract

Clinical trials are conducted in medical studies to study the effect of a drug, a therapy or a treatment method on a group of patients. The treatment time or healing time of these patients is usually assumed to be exponential distribution with constant healing rate. Due to impatience or social/economic reasons, many patients leave the experiment without completing the study and become dropouts. The dropout rate of patients is also considered to be constant in literature, with dropout time distribution again as exponential. But it is often observed that, the patient’s impatience increases with time and hence the dropout rate also increases with time resulting in the dropout time following the Rayleigh distribution.

Suggested Citation

  • Neha Goel, 2018. "Estimation Methods in Clinical Trials with Randomly Censored Exponential Healing Times and Rayleigh Dropout Times," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 8(3), pages 61-68, October.
    • Handle: RePEc:adp:jbboaj:v:8:y:2018:i:3:p:61-68
    DOI: 10.19080/BBOAJ.2018.08.555740
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    References listed on IDEAS

    as
    1. Muhammad Danish & Muhammad Aslam, 2013. "Bayesian estimation for randomly censored generalized exponential distribution under asymmetric loss functions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(5), pages 1106-1119.
    2. M. E. Ghitany & S. Al-Awadhi, 2002. "Maximum likelihood estimation of Burr XII distribution parameters under random censoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(7), pages 955-965.
    3. M. Ghitany, 2001. "A compound Rayleigh survival model and its application to randomly censored data," Statistical Papers, Springer, vol. 42(4), pages 437-450, October.
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