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A Web-based Simulator for Sample Size and Power Estimation in Animal Carcinogenicity Studies

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  • Moon, Hojin
  • Lee, J. Jack
  • Ahn, Hongshik
  • Nikolova, Rumiana G.

Abstract

A Web-based statistical tool for sample size and power estimation in animal carcinogenicity studies is presented in this paper. It can be used to provide a design with sufficient power for detecting a dose-related trend in the occurrence of a tumor of interest when competing risks are present. The tumors of interest typically are occult tumors for which the time to tumor onset is not directly observable. It is applicable to rodent tumorigenicity assays that have either a single terminal sacrifice or multiple (interval) sacrifices. The design is achieved by varying sample size per group, number of sacrifices, number of sacrificed animals at each interval, if any, and scheduled time points for sacrifice. Monte Carlo simulation is carried out in this tool to simulate experiments of rodent bioassays because no closed-form solution is available. It takes design parameters for sample size and power estimation as inputs through the World Wide Web. The core program is written in C and executed in the background. It communicates with the Web front end via a Component Object Model interface passing an Extensible Markup Language string. The proposed statistical tool is illustrated with an animal study in lung cancer prevention research.

Suggested Citation

  • Moon, Hojin & Lee, J. Jack & Ahn, Hongshik & Nikolova, Rumiana G., 2002. "A Web-based Simulator for Sample Size and Power Estimation in Animal Carcinogenicity Studies," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i13).
    • Handle: RePEc:jss:jstsof:v:007:i13
    DOI: http://hdl.handle.net/10.18637/jss.v007.i13
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    References listed on IDEAS

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    1. Ahn, Hongshik & Moon, Hojin & Kim, Sunyoung & Kodell, Ralph L., 2002. "A Newton-based approach for attributing tumor lethality in animal carcinogenicity studies," Computational Statistics & Data Analysis, Elsevier, vol. 38(3), pages 263-283, January.
    2. Firth, David, 2000. "Quasi-variances in Xlisp-Stat and on the web," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 5(i04).
    3. Banfield, Jeff, 1999. "Rweb:Web-based Statistical Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 4(i01).
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