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Bayesian hierarchical semiparametric modelling of longitudinal post-treatment outcomes from open enrolment therapy groups

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  • Susan M. Paddock
  • Terrance D. Savitsky

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  • Susan M. Paddock & Terrance D. Savitsky, 2013. "Bayesian hierarchical semiparametric modelling of longitudinal post-treatment outcomes from open enrolment therapy groups," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 795-808, June.
    • Handle: RePEc:bla:jorssa:v:176:y:2013:i:3:p:795-808
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    File URL: http://hdl.handle.net/10.1111/j.1467-985X.2012.12002.x
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    References listed on IDEAS

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    1. Peter W. Hill & Harvey Goldstein, 1998. "Multilevel Modeling of Educational Data With Cross-Classification and Missing Identification for Units," Journal of Educational and Behavioral Statistics, , vol. 23(2), pages 117-128, June.
    2. Jones, Galin L. & Haran, Murali & Caffo, Brian S. & Neath, Ronald, 2006. "Fixed-Width Output Analysis for Markov Chain Monte Carlo," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1537-1547, December.
    3. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    4. James S. Hodges & Bradley P. Carlin & Qiao Fan, 2003. "On the Precision of the Conditionally Autoregressive Prior in Spatial Models," Biometrics, The International Biometric Society, vol. 59(2), pages 317-322, June.
    5. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    6. Elizabeth R. Brown & Joseph G. Ibrahim, 2003. "A Bayesian Semiparametric Joint Hierarchical Model for Longitudinal and Survival Data," Biometrics, The International Biometric Society, vol. 59(2), pages 221-228, June.
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    Cited by:

    1. Maura Mezzetti & Daniele Borzelli & Andrea d’Avella, 2022. "A Bayesian approach to model individual differences and to partition individuals: case studies in growth and learning curves," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1245-1271, December.
    2. Lorena Charrier & Michela Bersia & Alessio Vieno & Rosanna Irene Comoretto & Mindaugas Štelemėkas & Paola Nardone & Tibor Baška & Paola Dalmasso & Paola Berchialla, 2022. "Forecasting Frequent Alcohol Use among Adolescents in HBSC Countries: A Bayesian Framework for Making Predictions," IJERPH, MDPI, vol. 19(5), pages 1-14, February.

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