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Re: st: Re: "Meta-analysis" of Proportions
kathryn dennick <firstname.lastname@example.org>
Re: st: Re: "Meta-analysis" of Proportions
Sun, 21 Apr 2013 07:10:25 -0700
Thank you, both. I don't use Stata, I know very little about it and was lead to believe by a course tutor that my query was not possible and hence would not be covered by FAQs and the like. Instead, I was advised to post here. I also used columns in an attempt to be explicit about the way in which I was entering data. Yes, variables are more appropriate. Thank you.
I also didn't realise my message would go straight to the list, rather I thought the text relating to my query might be extracted as I submitted it to an email address which I presumed might be for for moderation or the like. My apologies, had I realised it would have been tidier and clearly marked etc.
Many thanks for your help. I very much appreciate it.
Sent from my iPhone
On 21 Apr 2013, at 06:38, "Steve Samuels" <email@example.com> wrote:
> Welcome to Statalist, Kathryn.
> You'd do much better if you:
> 1. Read the FAQ
> 2. Put a subject into your emails
> 3. Spelled Stata correctly -not "STATA" (see the FAQ)
> 4. Use Stata, not spreadsheet, terms: you have "variables" that you are inputting, not "columns"
> Unlike a meta-analysis of a causal effect, there is no reason to
> believe that proportions from the different studies will be identical, or
> even similar. So it is a bad idea to pool studies (add numerators &
> denominators) to get an overall rate. The studies almost certainly have
> different sample sizes and represent different populations. In the
> example below, the largest study has the lowest rate and dominates the
> pooled estimate. Although you don't mention it, you also need confidence
> Here is model code:
> *********CODE BEGINS************
> /* Save this as "prop01.do" */
> capture log close
> set more off
> log using prop01, replace
> input study events ntot
> 1 60 300
> 2 60 150
> 3 35 100
> 4 20 60
> gen prop = events/ntot
> /* Convert to a full data set */
> gen no_events = ntot - events
> save d1, replace
> keep study events
> gen event = 1
> rename events count
> tempfile t1
> save `t1'
> keep study no_events
> gen event = 0
> rename no_events count
> append using `t1'
> sort study event
> expand count //expand to individual records
> save d2, replace
> /* Get separate CIs */
> bys study: ci event, binomial agresti
> /* Overall Proportion */
> /* 1. Pool studies: bad idea
> Weights studies by sample size
> Incorrect idependence assumption for CI
> mean event
> ci event, binomial agresti //same
> /* 2. Arithmetic mean of study proportions:
> probably best idea*/
> use d1, clear
> ci prop
> /*3. Random Effects Model: assumes normality of study
> effects, which must be checked by plots. Don't use
> it unless you understand it and do the checks. Here, it
> is to the arithmetic mean of the 4 proportions
> and I would use just that and its CI
> use d2, clear
> xtmelogit event || study:
> nlcom invlogit(_b[_cons])
> *****************CODE ENDS********************
> On Apr 19, 2013, at 12:14 PM, kathryn dennick wrote:
> I am a registered user of STATA and I have a query that I would really like to post in statalist if that's possible at all, please?
> I am currently undertaking a systematic review of cross sectional studies investigating the prevalence of distress in diabetes. The data I have to synthesise is a simple proportion/percentage (those with distress as a proportion of the total diabetes sample). I have to to synthesise this data in a meta-analysis, and I recently attended a course where I learnt about using the metan code from the online STATA journal to conduct meta-analysis of RD, RR and OR data. I'm obviously not able to use this to undertake the meta-analysis as I don't have this type of data. I am know that in entering the data that I have as 2 columns (i.e. as those with depression and then the total sample size), STATA will not read this as a proportion as I need it to. My query is whether you are aware of any STATA command that exists which would enable me to synthesise the data I have and derive an overall proportion/percentage across the studies being reviewed.
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