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Incorporation of historical controls using semiparametric mixed models

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  • Helen Parise
  • M. P. Wand
  • David Ruppert
  • Louise Ryan

Abstract

The analysis of animal carcinogenicity data is complicated by various statistical issues. A topic of recent debate is how to control for the effect of the animal's body weight on the outcome of interest, the onset of tumours. We propose a method which incorporates historical information from the control animals in previously conducted experiments. We allow non‐linearity in the effects of body weight by modelling the relationship nonparametrically through a penalized spline. A simple extension of the penalized spline model allows the relationship between weight and the onset of tumour to vary from one experiment to another.

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  • Helen Parise & M. P. Wand & David Ruppert & Louise Ryan, 2001. "Incorporation of historical controls using semiparametric mixed models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(1), pages 31-42.
    • Handle: RePEc:bla:jorssc:v:50:y:2001:i:1:p:31-42
    DOI: 10.1111/1467-9876.00218
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    Cited by:

    1. Ugarte, M.D. & Goicoa, T. & Militino, A.F. & Durbán, M., 2009. "Spline smoothing in small area trend estimation and forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3616-3629, August.
    2. J. D. Opsomer & G. Claeskens & M. G. Ranalli & G. Kauermann & F. J. Breidt, 2008. "Non‐parametric small area estimation using penalized spline regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 265-286, February.
    3. Sue J. Welham & Brian R. Cullis & Michael G. Kenward & Robin Thompson, 2006. "The Analysis of Longitudinal Data Using Mixed Model L-Splines," Biometrics, The International Biometric Society, vol. 62(2), pages 392-401, June.
    4. Cai, Bo & Dunson, David B., 2007. "Bayesian Multivariate Isotonic Regression Splines: Applications to Carcinogenicity Studies," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1158-1171, December.
    5. Ali M. Mosammam & Jorge Mateu, 2018. "A penalized likelihood method for nonseparable space–time generalized additive models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(3), pages 333-357, July.
    6. Gerhard Tutz, 2003. "Generalized Semiparametrically Structured Ordinal Models," Biometrics, The International Biometric Society, vol. 59(2), pages 263-273, June.
    7. Militino, A.F. & Goicoa, T. & Ugarte, M.D., 2012. "Estimating the percentage of food expenditure in small areas using bias-corrected P-spline based estimators," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2934-2948.
    8. Rüdiger Krause & Gerhard Tutz, 2006. "Genetic algorithms for the selection of smoothing parameters in additive models," Computational Statistics, Springer, vol. 21(1), pages 9-31, March.

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