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

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

Subject |
SV: st: Control groups |

Date |
Mon, 12 May 2008 22:10:36 +0200 |

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/

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

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

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

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