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From |
Austin Nichols <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: Question regarding meta-analysis for proportions. |

Date |
Sat, 30 Jul 2011 12:01:17 -0400 |

Nora Trabulsi <nora.trabulsi@mail.mcgill.ca>: I said log odds, not OR. The log odds for a proportion p is logit(p) in Stata, and is undefined for zero and one. Why prefer log odds? Quoting from http://www.stata-journal.com/sjpdf.html?articlenum=gr0010 "MacKay (2003, 316) asserts that, if we transform beta distributions of variables P between 0 and 1 to the corresponding densities over logit P = ln[P/(1 - P)], then we find always pleasant bell-shaped densities. In contrast, densities over P may have singularities at P = 0 and P = 1." This (in part) is why log odds, and differences in them, are used as a measure of "effect size" for proportions. On the relevance of the Beta distribution, see e.g. http://books.google.com/books?id=kMs5AAAAIAAJ&pg=141#v=onepage&q&f=false On Sat, Jul 30, 2011 at 11:28 AM, Nora Trabulsi <nora.trabulsi@mail.mcgill.ca> wrote: > Thanks Austin > The problem in my case is that I cannot use OR as there are no "unexposed" group. It is a meta analysis of phase 2 trials, in which all patients receive the intervention of interest and then the response(yes/no) rates are calculated, and that's why I thought of choosing proportions as the effect estimate. > > I have no experience with bayesian analysis in stata, however your approach sounds interesting and challenging! I must read about Bayesian in stata and give it a try and let you know. > > Thanks again > > Nora > > Sent from my iPhone > > On 2011-07-30, at 9:34 AM, "Austin Nichols" <austinnichols@gmail.com> wrote: > >> Nora Trabulsi <nora.trabulsi@mail.mcgill.ca> : >> If you are working on a log odds scale as you should for meta-analysis >> of proportions, you will have problems with the point estimate, not >> just the standard error. One way forward would be to use the mean and >> variance of the posterior distribution in a Bayesian framework, with a >> uniform prior in each study. Probably true Bayesians would object to >> this miscegenation of Bayesian and frequentist approaches, but I am >> betting that if you simulate the approach, it dominates others in >> terms of MSE. It does not seem justifiable to remove the 2 studies >> with the highest outcome from the analysis since you will introduce >> bias by selecting on the outcome. >> >> On Thu, Jul 28, 2011 at 3:48 PM, Nora Trabulsi >> <nora.trabulsi@mail.mcgill.ca> wrote: >>> Thanks for your response >>> >>> Yes, this is with using binomial exact. When I generated the proportions and their standard errors, the results shown in the the stata window shows "binomial exact". <snip>>>> >>>> I am doing a meta analysis on proportions of patients responding to specific treatment. I generated p(proportions) and se(standard errors). Then , I used the metan command: >>>> >>>> metan p se, random >>>> >>>> The problem that I have encountered is that two of the studies that are included in the analysis had a response rate of 100%, however, they were small in size, 4 and 5 patients only. So this generated a problem as they had standard errors = zero and they were excluded form the analysis and forest plot. >>>> >>>> I tried to use the inverse weight command before running metan: >>>> >>>> gen cons=1 >>>> vwls p cons, sd(se) >>>> >>>> but it would still address the same problem, that std error theta cannot be negative or zero. >>>> >>>> Any idea how to solve this problem, or is it justifiable to remove those 2 studies from the analysis? * * 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**:**st: Question regarding meta-analysis for proportions.***From:*Nora Trabulsi <nora.trabulsi@mail.mcgill.ca>

**st: RE: Question regarding meta-analysis for proportions.***From:*"Forshee, Richard" <Richard.Forshee@fda.hhs.gov>

**Re: st: RE: Question regarding meta-analysis for proportions.***From:*Nora Trabulsi <nora.trabulsi@mail.mcgill.ca>

**Re: st: RE: Question regarding meta-analysis for proportions.***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: RE: Question regarding meta-analysis for proportions.***From:*Nora Trabulsi <nora.trabulsi@mail.mcgill.ca>

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