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Re: SV: st: Control groups


From   Steven Samuels <sjhsamuels@earthlink.net>
To   statalist@hsphsun2.harvard.edu
Subject   Re: SV: st: Control groups
Date   Mon, 12 May 2008 23:12:41 -0400

Alexander, you can use -sampsi- as a rough guide for stratified sampling, provided that the relative sampling fractions (treatment/ control) do not differ much between strata.

I don't know this area, but do you really want to do a hypothesis test? It sounds like you want to estimate proportions of people who respond to a campaign over time or at all. The control group provides the proportion who freely respond in the absence of a campaign. A test of zero campaign effect does not seem relevant. Wouldn't the more pertinent questions be: how big an effect, answered with confidence intervals; or a null hypothesis that the effect is at most some quantity "D", with alternative effect>D. The CI approach (but not a hypothesis test) can incorporate finite-population corrections. See any sampling book for details.

-Steven


On May 12, 2008, at 4:10 PM, <Alexander.Severinsen@telenor.com> <Alexander.Severinsen@telenor.com> wrote:


Carlo, thanks for pointing me to sampsi!

Steven, sorry about being sparse with information. I actually had many different study designs in mind. Sometimes I will be using simple random sampling, and don't intend to generalize my findings. Then all I am planning to do is testing whether proportions in treated versus control are different. Also, my control group is internal.

However, from time to time I would like to draw a stratified sample, otherwise using the same approach as above. The way I understand you sampsi would not be appropriate?

Also, one particular study I will try to estimate is Lo (2002). This is about the same as uplift modeling, uplift being another way of saying "proportional hazards modelling". For this analysis I have come across the Schoenfeld (1983), and the Stata program to estimate sample sizes, stpower (findit stpower). Unfortunately, I don't have access to Biometrics. So I am just guessing from the title that I am on the right track!

Lo, V.S.Y. (2002) "The True Lift Model - A Novel Data Mining Approach to Response Modeling in Database Marketing." 4(2), p- 78-86.

Schoenfeld, D. 1983. Sample-size formula for the proportional-hazards
regression model. Biometrics 39: 499-503.

Best wishes,
Alexander


-----Opprinnelig melding-----
Fra: owner-statalist@hsphsun2.harvard.edu [mailto:owner- statalist@hsphsun2.harvard.edu] På vegne av Steven Samuels
Sendt: 11. mai 2008 19:03
Til: statalist@hsphsun2.harvard.edu
Emne: Re: st: Control groups

Alexander,

Without more information, I cannot tell if -samnpsi- or any of the other programs that you tried will give you proper answers. They will be okay, for example, if 1) you measure responses without sampling or by simple random sampling; and 2) you don't intend to generalize your findings beyond the two particular populations you study. Different designs other than aimple two-group cross- sectional comparison might reduce the needed sample size and strengthen your conclusions. Whether your control group is internal (same
population) or external (another population) matters, too.

In any case, more details would be helpful.

Steven

On May 9, 2008, at 5:52 PM, <Alexander.Severinsen@telenor.com>
<Alexander.Severinsen@telenor.com> wrote:


Dear Statalisters,

Say I have a population of 500 000. I would like to treat this
population with some sort of communication, and to be able to measure
the effect of this treatment I have the opportunity to draw a control
group. Based on earlier experiences the effects between the treated
and the controlgroup could be as small as 0.5%.

I want to be able to detect such a small effects, and I am wondering
how large my control groups ideally would be to track these changes,
say at an alpha level of 0.05.

I have tried to use the fpower (findit fpower) and the simpower
(findit
simpower) to determine optimal control group sizes. I am curious
whether there are other alternatives in Stata?

Thanks! And have a nice weekend.

Best wishes,
Alexander

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