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Cost-effective designs for trials with discrete-time survival endpoints

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  • Jóźwiak, Katarzyna
  • Moerbeek, Mirjam
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    Abstract

    In studies on event occurrence, the timing of events may be measured continuously using thin precise units or discretely using time periods. The design of trials with continuous-time survival endpoints has been studied for years, but very little is known about the design of trials with discrete-time survival endpoints. The optimal designs for trials where observations are recorded at discrete points in time is calculated using the generalized linear model and Weibull distribution. Applying a cost function, the optimal number of subjects and time periods are found in such a way that a sufficient power level is achieved at a minimal cost or the power level is maximized for a fixed budget. Taking the budget for a trial and the cost ratio between recruiting a new subject and obtaining a measurement per subject into account, it is observed that the cost ratio and the shape of the survival function have the greatest influence on the optimal design.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0167947311004531
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 56 (2012)
    Issue (Month): 6 ()
    Pages: 2086-2096

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    Handle: RePEc:eee:csdana:v:56:y:2012:i:6:p:2086-2096

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    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: Discrete-time longitudinal data; Survival analysis; Optimal design; Cost function;

    References

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    1. Tekle, Fetene B. & Tan, Frans E.S. & Berger, Martijn P.F., 2008. "Maximin D-optimal designs for binary longitudinal responses," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5253-5262, August.
    2. Lima Passos, Valéria & Tan, Frans E.S. & Berger, Martijn P.F., 2011. "Cost-efficiency considerations in the choice of a microarray platform for time course experimental designs," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 944-954, January.
    3. Hashimoto, Elizabeth M. & Ortega, Edwin M.M. & Paula, Gilberto A. & Barreto, Mauricio L., 2011. "Regression models for grouped survival data: Estimation and sensitivity analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 993-1007, February.
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    Cited by:
    1. Safarkhani, Maryam & Moerbeek, Mirjam, 2014. "The influence of a covariate on optimal designs in longitudinal studies with discrete-time survival endpoints," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 217-226.

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