Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
David Hoaglin <dchoaglin@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Fri, 6 Jan 2012 17:45:28 -0500 |

Tiago, That is the usual situation: the estimated variances are used as if they were the true variances. The sizes of the studies may not be the major issue. I would be interested in seeing empirical evidence that having approximately equal sample sizes in the studies leads to small bias in the summary estimate. The behavior of the inverse-variance-weighted mean will generally depend on the effect measure. It's a good idea to pay attention to a warning (applicable to fixed-effect analyses) given by Yates and Cochran in 1938 and repeated by Cochran in 1954 that, if the estimates (here of the effect) and the estimates of their variances are correlated, the summary estimate may be biased. Bias can definitely be a problem when the effect measure is the risk difference, and the risk ratio and the odds ratio are likely to have similar difficulties. To be safe, one should avoid using inverse-variance weights with those effect measures. David On Thu, Jan 5, 2012 at 11:36 AM, Tiago V. Pereira <tiago.pereira@mbe.bio.br> wrote: > 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 * * 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/

**References**:**RE: st: Meta analysis in single group,***From:*"Tiago V. Pereira" <tiago.pereira@mbe.bio.br>

- Prev by Date:
**Re: st: RE: xtreg - saving transformed variables** - Next by Date:
**st: How to refer to different time periods when writing code** - Previous by thread:
**RE: st: Meta analysis in single group,** - Next by thread:
**st: xtreg - saving transformed variables** - Index(es):