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Re: st:appropriate test

From   Steve Samuels <[email protected]>
To   [email protected]
Subject   Re: st:appropriate test
Date   Thu, 7 Oct 2010 11:18:31 -0400

I agree with Ronan that more information is necessary: are you
interested in estimating rates and changes just for the sampled PSUs,
or for the population from which they are sampled?  If you are
interested in rates just for those PSUs, then create a combo PSU-round
stratum variable, e.g. with:

egen cstratum = group(area round)

Then -svyset-  a psu variable equal to the second stage sampling unit
(ssu2) in the survey:

svyset ssu2 [pweight= ], strata(cstratum)....

If you want to estimate for the population from which the areas were sampled:

svyset area [pweight=], strata(original stratum)  || ssu2, strata(round)

For descriptive estimates of prevalence rates and their differences, I
recommend -svy: tab-, which uses a logit transformation for
proportions to avoids CIs that extend below zero.  You can add finite
population corrections if these would make a difference.
webuse nhanes2
svy: tab sex diabetes, row ci se llwald
matrix list e(b)
lincom _b[p22] - _b[p12]

But you have not given us enough details about the purpose of your
study that I can be confident of these specifications:  for example,
whether you are confining your estimates to  particular

I don't agree with Ronan's recommendation of an event-time model.  You
have cross-sectional prevalence data, not a cohort.  So you would need
 a "current status"  (or "status quo") model:  the information for
each individual is their current age and whether or not they have the
disease of interest;  other words, every individual is right-censored
or left-censored.  From this information it is possible to reconstruct
a  survival curve analogous to a current life table.  I'd recommend a
logistic model, instead.  For such regression analyses, don't use the


Steven J. Samuels
[email protected]
18 Cantine's Island
Saugerties NY 12477
Voice: 845-246-0774
Fax:    206-202-4783

On Wed, Oct 6, 2010 at 4:59 AM, Ronan Conroy <[email protected]> wrote:
> On 6 DFómh 2010, at 07:42, Rajaram Subramanian Potty wrote:
>> I have data from two rounds of survey conducted in the same areas
>> (PSUs). But the individual are selected independently in both the
>> rounds from these areas using the same statistical approaches. What
>> would be the appropriate analysis that would be carried out to test
>> the difference in some of the indicators between the two periods. For,
>> example I want to test the difference in HIV prevalence between the
>> two rounds. Is it appropriate to use the survey command by considering
>> the PSUs are the same in both the rounds and setting the survey design
>> according to our study. After that fitting svy: logistic to examine
>> the difference in two rounds, is this correct way of testing the
>> difference between the two rounds. Kindly suggest.
> My first reaction would be that the most important thing needed here is a
> sample weighting scheme that allows you to extrapolate from the sample to
> the underlying population.
> Are the areas PSUs or strata? In other words, were the areas selected at
> random or deliberately chosen? This affects your analysis.
> If you have presumed age of infection, you could consider using an
> event-time model approach, using age as the time variable. This would allow
> you to look at the shape of the hazard function. Even if you don't, the
> hazard curve will show the cumulative prevalence by age (rather than the
> incidence) but may still be of interest.
> Ronán Conroy
> Associate Professor
> Division of Population Health Sciences
> =================================
> [email protected]
> Royal College of Surgeons in Ireland
> Epidemiology Department,
> Beaux Lane House, Dublin 2, Ireland
> +353 (0)1 402 2431
> +353 (0)87 799 97 95
> +353 (0)1 402 2764 (Fax - remember them?)
> P    Before printing, think about the environment
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