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From |
"Tiago V. Pereira" <tiago.pereira@incor.usp.br> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Does Stata have an "exact likelihood approach " to estimate the variance of a proportion? |

Date |
Wed, 18 Mar 2009 11:19:22 -0300 (BRT) |

Hello, stalisters! I would like to explore the method mentioned by Hamza et al (2008). The binomial distribution of meta-analysis was preferred to model within-study variability. Journal of Clinical Epidemiology, Volume 61, Issue 1, January 2008, Pages 41-51 http://linkinghub.elsevier.com/retrieve/pii/S0895435607001503 The authors compared an exact likelihood approach to the standard method that approximates the within-study variability of a proportion by a normal distribution They comment that main packages are able to do that, indluign SAS, R AND S-plus. Hence, is there a way to perform such analysis using Stata? If you need more info, please, let me know. all the best, Tiago "Now the model is a GLMM, and the parameters can be estimated by standard likelihood procedures. The practical disadvantage is that software is much more scarce and not yet available in all statistical packages. We used the NLMIXED procedure from the SAS package [17]. It is also possible to use the recently included GLIMMIX procedure in the SAS package, which is still experimental in SAS version 9.1. The GLIMMIX procedure allows more random effects, but it has the disadvantage that it uses an approximation instead of the true log likelihood." "In this paper,we compared the use of the approximate normal within-study likelihood that is used in practice with the alternative exact binomial likelihood. Calculation of the exact binomial likelihood involves an approximation of the integral. In NLMIXED, the method of Gaussian quadrature is used, with the number of quadrature points to be specified by the user or automatically by SAS. The larger that number is chosen, the better the approximation, but at the cost of more computational time. For example, Carlin et al. [34] have shownthat for binary outcome longitudinal data, a reasonably large number of quadrature points (i.e., 20) is required to ensure convergence on model parameter estimates. In our data example, to study the impact of the number of quadrature points we fitted the model for varying number of quadrature points. It turned out the estimates (SE) of sensitivity and specificity did not change for a number of quadrature points greater than or equal to 10 and 15, respectively. We used 20 quadrature points for our simulation study." * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**st: RE: Does Stata have an "exact likelihood approach " to estimate the variance of a proportion?***From:*"Joseph Coveney" <jcoveney@bigplanet.com>

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