```Date: Tue, 20 Apr 2004 11:23:50 -0400 Reply-To: Doc Muhlbaier Sender: "SAS(r) Discussion" From: Doc Muhlbaier Organization: Duke University, Durham, NC, USA Subject: Re: t-test proportions Jim, If your dichotomous variables are coded 0 and 1, you can use PROC TTEST to get the results that you are looking for. Note that the PAIRED statement was added to the PROCedure in 8.2, so it's easier to do the paired version than it used to be. The Z-scores that you refer to below are for the z-test (square root of the chi-squared test). The t is the analogous t-approximation similar to continuous data; it relies more on the central limit theorem here than for data that are on a rational scale. It's an approximation, for sure, but I've found it to be a pretty good one and it has a much more succinct display than PROC FREQ. Doc Muhlbaier Duke "Groeneveld, Jim" wrote in message news:81BFA8F7807F1349AD6C16AD00A1AB9BD3A8C0@AMSM1BMSGM01.ent.core.medtronic.com... > Hi friends, > > I am looking for an implantation in SAS of the hypothesis tests described below. > My design is either two dependent (paired) or two independent samples (groups). > A single dichotome variable has to be tested for differences between both groups. > The difference can be described in terms of proportions (of one of the two values) > and group sizes only. > > In the book <> a t-test for proportions between independent groups is outlined. Based on that I wrote a simple Fortran (Fortran 4 or Fortran 66 as it was called on an already extinct mainframe computer) program some 25 years ago, which calculated z-scores from both proportions (or percentages) and group sizes. The partial code, from which the used formula may be evident, is: > POOLED=(PROP1*N1+PROP2*N2)/(N1+N2) > ZSCORE=(PROP1-PROP2)/SQRT(POOLED*(1.-POOLED)*(1./N1+1./N2)) > I have used this program quite some time with the aggregated data. > > While searching the internet I came across a.o. the following sites: > http://courses.smsu.edu/nkk661f/QBA337/handout4.htm > http://www.stat.sc.edu/curricula/courses/515/515SAS.html#9p3 > Both pages give formulas for proportions, which actually are the same in both of them. Their formula is: > z = (p1 - p2) / sqrt ( (P x (1-P) / n1 ) + (P x (1-P) / n2 ) ) > where P = pooled proportion: (p1n1 + p2n2) / (n1 + n2) > This is the same formula I used to use. > > The web page > http://www.ocair.org/files/KnowledgeBase/Statistics/Proportion.htm > mentiones a similar formula for t, where the pooled proportion is replaced by the group proportions: > t = (p1 - p2) / sqrt ( (p1 x (1-p1) / n1 ) + (p2 x (1-p2) / n2 ) ) > > These sites apparently give code to calculate the p-values using data step code, but now I would like to know how I can calculate the same from the individual data using a standard SAS PROCedure. So I would like to avoid writing some algorithm in a data step, because that would have to be validated. I know I also could apply a Chi-square. > > And additional to that I also would like to know how to do it with a standard SAS PROCedure with dependent (paired) groups (repeated measures), i.e. comparing the proportions of two different dichotome variables within one sample. > > Regards - Jim. > -- > . . . . . . . . . . . . . . . . > > Jim Groeneveld, MSc. > Biostatistician > Science Team > Vitatron B.V. > Meander 1051 > 6825 MJ Arnhem > Tel: +31/0 26 376 7365 > Fax: +31/0 26 376 7305 > Jim.Groeneveld@Vitatron.com > www.vitatron.com > > My computer remains home, but I will attend SUGI 2004. > > [common disclaimer] ```

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