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| From | "MacLennan, Graeme" <g.maclennan@abdn.ac.uk> |
| To | "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu> |
| Subject | RE: st: sampsi and percentages |
| Date | Wed, 31 Aug 2011 09:14:45 +0100 |
There is indeed, see: Roula Tsonaka, Dimitris Rizopoulos and Emmanuel Lesaffre. Power and sample size calculations for discrete bounded outcome scores. Statist. Med. 2006; 25:4241-4252 DOI: 10.1002/sim.2679 Graeme. -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox Sent: 31 August 2011 09:05 To: statalist@hsphsun2.harvard.edu Subject: Re: st: sampsi and percentages A beta distribution seems a natural candidate. There may well be published work on power for betas. Nick On Tue, Aug 30, 2011 at 8:08 PM, Nick Cox <njcoxstata@gmail.com> wrote: > Sure. I was partly in jest, but as a scientist I never feel > constrained by the particular units in which data arrive, especially > if they are not even metric units, let alone natural. Your example > remains units-dependent in that numerator and denominator have quite > different units. Not important unless this is also true of your real > example. > > The best way forward for you is likely to be not looking for a canned > approach but simulating datasets of different sizes under plausible > generating processes and seeing what is or is not detectable. > > Nick > > On Tue, Aug 30, 2011 at 7:33 PM, Ricardo Ovaldia <ovaldia@yahoo.com> wrote: >> >> Thank you Nick. I was specificaly talking about how lenght and weight are recorded in the auto data. >> >> gen r= length / weight >> . sum r >> Variable | Obs Mean Std. Dev. Min >> Max >> -------------+------------------------------------------------------- >> -------------+- >> r | 74 .0647308 .0102566 .0475524 >> .0872222 >> >> The ratio is less that one in all observations! >> So the statement was not incorrect. >> >> Regarding your second point: Yes it behaves as a proportion but I cannot use a sample size calculation for proportion to power this study because there is not a true denominator. >> Which brings me back to my original issue of how to power this study. >> >> Ricardo. >> >> Ricardo Ovaldia, MS >> Statistician >> Oklahoma City, OK >> >> >> ----- Original Message ----- >> From: Nick Cox <njcoxstata@gmail.com> >> To: statalist@hsphsun2.harvard.edu >> Cc: >> Sent: Tuesday, August 30, 2011 10:02 AM >> Subject: Re: st: sampsi and percentages >> >> Two points: >> >> 1. In terms of your example, length/weight is not always < 1. The >> value of that ratio is crucially dependent on some choice of units of >> measurement. Suppose I measure my (wife's) car's length in >> centimetres and its weight (mass) in tonnes, for example. >> You can call this pedantry but I react to incorrect statements! >> >> 2. More importantly, if something is bounded by (0,1) -- can we take >> that pair of () literally as implying 0 < data < 1? -- then it will >> behave like a proportion regardless of how the calculation was done. >> For example, an average very near 0 can only be achieved if all >> values are near 0 and so the variance will be very small, and >> similarly for an average near 1. However, that may not help much. >> >> Nick >> >> On Tue, Aug 30, 2011 at 3:45 PM, Ricardo Ovaldia <ovaldia@yahoo.com> wrote: >>> >>> Thank you, but these are not proportions. They are intensity measures. You can think of them as ratios of two continous things. >>> For example with the auto data, they could be the ratio of car's length to weight (length / weight) which is always between 0 and 1. >>> Now less say that you want to compare these ratio between between foreign and domestic cars. >>> >>> Ricardo >>> >>> Ricardo Ovaldia, MS >>> Statistician >>> Oklahoma City, OK >>> >>> >>> ----- Original Message ----- >>> From: "Ariel Linden, DrPH" <ariel.linden@gmail.com> >>> To: statalist@hsphsun2.harvard.edu >>> Cc: >>> Sent: Tuesday, August 30, 2011 7:51 AM >>> Subject: re: st: sampsi and percentages >>> >>> Ricardo, >>> >>> I may be mistaken here, but it seems you have two proportions (if >>> it's bounded between 0,1 then you have a numerator and a denominator >>> for each group). >>> >>> If that is truly the case, you can use sampsi for proportions: >>> >>> . sampsi 0.25 0.4 >>> >>> Estimated sample size for two-sample comparison of proportions >>> >>> Test Ho: p1 = p2, where p1 is the proportion in population 1 >>> and p2 is the proportion in population 2 >>> Assumptions: >>> >>> alpha = 0.0500 (two-sided) >>> power = 0.9000 >>> p1 = 0.2500 >>> p2 = 0.4000 >>> n2/n1 = 1.00 >>> >>> Estimated required sample sizes: >>> >>> n1 = 216 >>> n2 = 216 >>> >>> I hope this helps >>> >>> Ariel >>> >>> Date: Mon, 29 Aug 2011 11:40:33 -0700 (PDT) >>> From: Ricardo Ovaldia <ovaldia@yahoo.com> >>> Subject: st: sampsi and percentages >>> >>> >>> >>> I need to compute sample size and power for a study comparing two >>> group on a measurement bounded by (0,1), (a measure of intensity). >>> I was thinking about using -sampsi- to power on the difference of means. >>> However, this seems strange to me, is there another way to power >>> such comparison? >>> > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ The University of Aberdeen is a charity registered in Scotland, No SC013683. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/