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From |
<Alexander.Severinsen@telenor.com> |

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

Subject |
SV: SV: st: Control groups |

Date |
Tue, 13 May 2008 11:30:15 +0200 |

Steven, thanks a lot for the advice regarding sampsi, and for additional comments. I'll definitely look into it. Best wishes, Alex -----Opprinnelig melding----- Fra: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] På vegne av Steven Samuels Sendt: 13. mai 2008 05:13 Til: statalist@hsphsun2.harvard.edu Emne: Re: SV: st: Control groups 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 >> >> * >> * For searches and help try: >> * http://www.stata.com/support/faqs/res/findit.html >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ > > * > * For searches and help try: > * http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > > * > * For searches and help try: > * http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Control groups***From:*<Alexander.Severinsen@telenor.com>

**Re: st: Control groups***From:*Steven Samuels <sjhsamuels@earthlink.net>

**SV: st: Control groups***From:*<Alexander.Severinsen@telenor.com>

**Re: SV: st: Control groups***From:*Steven Samuels <sjhsamuels@earthlink.net>

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