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From |
"Tiago V. Pereira" <tiago.pereira@mbe.bio.br> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
RE: st: Meta analysis in single group, |

Date |
Thu, 5 Jan 2012 14:36:09 -0200 (BRST) |

David, If I am not wrong, within-study variances are usually assumed to be known, but are estimated from the data. So, once the combined studies are of approximately equal size, bias in the summary estimate is likely to be small, if any. The only problem I see for the `pre-pos' case is regarding the correlation estimate between time points (i.e. the correlation between pre and pos) if one does not have the raw data. Assumption of zero correlation will provide a conservative Wald test (Z test) if the true correlation is >0, but an anti-conservative Z test otherwise. Again, if the studies to be combined are approximately of equal size, bias in point estimates will be small. Tiago ----------------------------------- What should he do about the possibility of substantial bias when estimated variances are used in inverse-variance weighting and the DerSimonian-Laird method? David Hoaglin ------------------------------------ Dear Asad, I solved that problem writing my own code (both fixed-effects 'inverse variance' and DerSimonian-Laird methods). They are easy to implement, and I think that in your case this is the only way out. -metan- has an option to perform analyses using the effect and its standard error. However, that approach will not be suitable when data is continuous, since the normal approximation may not be valid depending on the sample size. To address your question, you need to estimate the mean difference (pos vs pre) for each study. Then, an appropriate variance estimate should be computed. To do that, a proper correlation estimate has to be known (or estimated from the data). Once you have the mean effect and its variance, a variance-weighted analysis can be performed. Let me know if you need help. Tiago * * 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**:**Re: st: Meta analysis in single group,***From:*David Hoaglin <dchoaglin@gmail.com>

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