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From |
Nora Trabulsi <nora.trabulsi@mail.mcgill.ca> |

To |
"<statalist@hsphsun2.harvard.edu>" <statalist@hsphsun2.harvard.edu> |

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

Date |
Sat, 30 Jul 2011 15:28:35 +0000 |

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". >> Here is the output: >> >> -- Binomial Exact -- >> Variable Obs Mean Std. Err. [95% Conf. Interval] >> >> 5 1 0 .4781762 1* >> >> (*) one-sided, 97.5% confidence interval >> >> -- Binomial Exact -- >> Variable Obs Mean Std. Err. [95% Conf. Interval] >> >> 4 1 0 .3976354 1* >> >> >> >> So what do you think? >> >> Nora >> >> >> >> >> >> On 2011-07-28, at 3:38 PM, Forshee, Richard wrote: >> >>> Have you considered using exact binomial confidence intervals instead of the approximation to the Normal distribution? >>> >>> >>> Richard A. Forshee >>> >>> -----Original Message----- >>> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nora Trabulsi >>> Sent: Thursday, July 28, 2011 2:36 PM >>> To: statalist@hsphsun2.harvard.edu >>> Subject: st: Question regarding meta-analysis for proportions. >>> >>> Hi >>> >>> 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? >>> >>> Thanks >>> >>> Nora Trabulsi > > * > * 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/ * * 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: RE: Question regarding meta-analysis for proportions.***From:*Austin Nichols <austinnichols@gmail.com>

**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>

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