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A Bayesian analysis for longitudinal semicontinuous data with an application to an acupuncture clinical trial

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  • Ghosh, Pulak
  • Albert, Paul S.

Abstract

In many biomedical applications, researchers encounter semicontinuous data where data are either continuous or zero. When the data are collected over time the observations may be correlated. Analysis of this kind of longitudinal semicontinuous data is challenging due to the presence of strong skewness in the data. A flexible class of zero-inflated models in a longitudinal setting is developed. A Bayesian approach is used to analyze longitudinal data from an acupuncture clinical trial, in which the effects of active acupuncture, sham acupuncture and standard medical care is compared on chemotherapy-induced nausea in patients who were treated for advanced breast cancer. A spline model is introduced into the linear predictor of the model to explore the possibility of a nonlinear treatment effect. Possible serial correlation between successive observations is also accounted using the Brownian motion. Thus, the approach taken in this paper provides for a more flexible modeling framework and, with the use of WinBUGS, provides for a computationally simpler approach than direct maximum-likelihood. The Bayesian methodology is illustrated with the acupuncture clinical trial data.

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  • Ghosh, Pulak & Albert, Paul S., 2009. "A Bayesian analysis for longitudinal semicontinuous data with an application to an acupuncture clinical trial," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 699-706, January.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:3:p:699-706
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    1. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    2. Zhang, Min & Strawderman, Robert L. & Cowen, Mark E. & Wells, Martin T., 2006. "Bayesian Inference for a Two-Part Hierarchical Model: An Application to Profiling Providers in Managed Health Care," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 934-945, September.
    3. Sturtz, Sibylle & Ligges, Uwe & Gelman, Andrew, 2005. "R2WinBUGS: A Package for Running WinBUGS from R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i03).
    4. Duan, Naihua, et al, 1983. "A Comparison of Alternative Models for the Demand for Medical Care," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 115-126, April.
    5. Robinson, John W. & Zeger, Scott L. & Forrest, Christopher B., 2006. "A Hierarchical Multivariate Two-Part Model for Profiling Providers' Effects on Health Care Charges," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 911-923, September.
    6. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, November.
    7. 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.
    8. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, November.
    9. Paul S. Albert & Joannie Shen, 2005. "Modelling longitudinal semicontinuous emesis volume data with serial correlation in an acupuncture clinical trial," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(4), pages 707-720, August.
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    Cited by:

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    3. Yang, Yan & Simpson, Douglas, 2010. "Unified computational methods for regression analysis of zero-inflated and bound-inflated data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1525-1534, June.

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