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Likelihood ratio tests for a dose-response effect using multiple nonlinear regression models

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  • Georg Gutjahr
  • Björn Bornkamp

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  • Georg Gutjahr & Björn Bornkamp, 2017. "Likelihood ratio tests for a dose-response effect using multiple nonlinear regression models," Biometrics, The International Biometric Society, vol. 73(1), pages 197-205, March.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:1:p:197-205
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    File URL: http://hdl.handle.net/10.1111/biom.12563
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    References listed on IDEAS

    as
    1. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    2. Leeb, Hannes & Pötscher, Benedikt M., 2005. "Model Selection And Inference: Facts And Fiction," Econometric Theory, Cambridge University Press, vol. 21(1), pages 21-59, February.
    3. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258, September.
    4. Andrews, Donald W K, 1996. "Admissibility of the Likelihood Ratio Test When the Parameter Space Is Restricted under the Alternative," Econometrica, Econometric Society, vol. 64(3), pages 705-718, May.
    5. C. Baayen & P. Hougaard & C. B. Pipper, 2015. "Testing effect of a drug using multiple nested models for the dose–response," Biometrics, The International Biometric Society, vol. 71(2), pages 417-427, June.
    6. repec:bla:biomet:v:71:y:2015:i:4:p:996-1008 is not listed on IDEAS
    7. F. Bretz & J. C. Pinheiro & M. Branson, 2005. "Combining Multiple Comparisons and Modeling Techniques in Dose-Response Studies," Biometrics, The International Biometric Society, vol. 61(3), pages 738-748, September.
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    Cited by:

    1. Frank Schaarschmidt & Christian Ritz & Ludwig A. Hothorn, 2022. "The Tukey trend test: Multiplicity adjustment using multiple marginal models," Biometrics, The International Biometric Society, vol. 78(2), pages 789-797, June.
    2. Francesco De Pretis & Barbara Osimani, 2019. "New Insights in Computational Methods for Pharmacovigilance: E-Synthesis , a Bayesian Framework for Causal Assessment," IJERPH, MDPI, vol. 16(12), pages 1-19, June.

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