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On the Conditional Power in Survival Time Analysis Considering Cure Fractions

Author

Listed:
  • Kuehnapfel Andreas

    (Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Haertelstrasse 16-18, 04107Leipzig, Germany)

  • Schwarzenberger Fabian

    (Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Haertelstrasse 16-18, 04107Leipzig, Germany)

  • Scholz Markus

    (Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Haertelstrasse 16-18, 04107Leipzig, Germany)

Abstract
Conditional power of survival endpoints at interim analyses can support decisions on continuing a trial or stopping it for futility. When a cure fraction becomes apparent, conditional power cannot be calculated accurately using simple survival models, e.g. the exponential model. Non-mixture models consider such cure fractions. In this paper, we derive conditional power functions for non-mixture models, namely the non-mixture exponential, the non-mixture Weibull, and the non-mixture Gamma models. Formulae were implemented in the R package CP. For an example data set of a clinical trial, we calculated conditional power under the non-mixture models and compared results with those under the simple exponential model.

Suggested Citation

  • Kuehnapfel Andreas & Schwarzenberger Fabian & Scholz Markus, 2017. "On the Conditional Power in Survival Time Analysis Considering Cure Fractions," The International Journal of Biostatistics, De Gruyter, vol. 13(1), pages 1-19, May.
    • Handle: RePEc:bpj:ijbist:v:13:y:2017:i:1:p:19:n:2
    DOI: 10.1515/ijb-2015-0073
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