TY - CONF
T1 - Sparse Data in Meta-Analysis: Parametric and Semiparametric Models and Parameterization Issues
AU - Chatterjee, Arpita
AU - Basu, Sanjib
N1 - 2010 JSM Online Program Home For information, contact [email protected] or phone (888) 231-3473. If you have questions about the Continuing Education program, please contact the Education Department.
Arpita Chatterjee. "Sparse Data in Meta-Analysis: Parametric and Semiparametric Models and Parameterization Issues" Topic contributed talk, Joint Statistical Meetings. Vancouver, Canada. Aug. 2010.
source:https://www.amstat.org/meetings/jsm/2010/onlineprogram/AbstractDetails.cfm?abstractid=308022
PY - 2010/8/4
Y1 - 2010/8/4
N2 - Adverse events are of serious concern to drug manufacturers, regulatory agencies such as the Food and Drug Administration (FDA), patients and the general public. In this article, we investigate the methodological issues in Meta analysis with sparse data. Our results show that the parameterization and the choice of priors play crucial roles in the statistical analysis. We propose parametric as well as Dirichlet process based semi parametric models for analysis of such data. In extensive simulation studies, we find that our proposed Bayesian estimates, in particular the semi parametric model based estimates, perform significantly better than the continuity corrected and other estimates proposed in the literature. We illustrate the proposed methods in a motivating example of Meta analysis of suicidal tendencies in children, based on 24 studies on the use of antidepressants in children.
AB - Adverse events are of serious concern to drug manufacturers, regulatory agencies such as the Food and Drug Administration (FDA), patients and the general public. In this article, we investigate the methodological issues in Meta analysis with sparse data. Our results show that the parameterization and the choice of priors play crucial roles in the statistical analysis. We propose parametric as well as Dirichlet process based semi parametric models for analysis of such data. In extensive simulation studies, we find that our proposed Bayesian estimates, in particular the semi parametric model based estimates, perform significantly better than the continuity corrected and other estimates proposed in the literature. We illustrate the proposed methods in a motivating example of Meta analysis of suicidal tendencies in children, based on 24 studies on the use of antidepressants in children.
KW - Adverse events
KW - Bayesian Analysis
KW - Markov chain simulation
UR - https://www.amstat.org/meetings/jsm/2010/onlineprogram/AbstractDetails.cfm?abstractid=308022
M3 - Presentation
T2 - Joint Statistical Meetings (JSM)
Y2 - 12 August 2015
ER -
