Hi Marta,

Thanks very much for your information and for the syntax. I tend to use syntax or GUI depending on what I'm doing, so will have a good look at the syntax you have sent.

If this doesn't take up too much time, would you be able to give me a brief description of (a)the Cochran test and (b) the Cochran-Armitage test that you mentioned. Nothing in-depth, but I'm just interested to know how they differ since you mentioned that they are very different. I've had a look around but can only seem to find info on the Cochran-Armitage test.

Best wishes.

Lou

On Thu, 18 May 2006 12:36:04 +0200, =?ISO-8859-1?B? TWFydGEgR2FyY+1hLUdyYW5lcm8=?= <biostatistics@terra.es> wrote:

>Hi Lou > >CB> My data is as you have described it (i.e. I know the number of >CB> people who did and did not take the test), so I will give the >CB> Cochran test a go. > >I insist: please address your questions to the whole SPSS list, not to >me only (other people might benefit from the information, and, also, >sometimes my mail server - Terra - marks legitimate mail as junk, and >stores it in a web folder I forget to watch, and your message could be >lost & left unreplied). > >Now, I have to apologyze for an error that reversed the sense of my >last message to you. It was late and I had worked a lot, my brain was >quite slow ;) > >Where I said: > >MGG> I hope you are not working with aggregated data (I mean, I hope >MGG> you don't have the individual data, but only the number of people who >MGG> undertook or not the test...). > >I SHOULD have said > >MGG> I hope you are not working with aggregated data (I mean, I hope >MGG> you have the individual data, not only the number of people who >MGG> undertook or not the test...). > >Since you say you only have the aggregated data, you have lost the >matching information, and Cochran test can't be done. You'll have to >assume independent data (it can shift your results towards non >significance) and use Cochran-Armitage test (name is quite close, but >test is VERY different). > >Marta > >---------------------------------------------------------------------- > >Do you use syntax or GUI? If you are "fluent" with syntax, then you can >adapt the following code to your data: > >************************************************** >* COCHRAN-ARMITAGE TEST FOR TREND * >************************************************** >* Ref: Alan Agresti (2002), Categorical Data * >* Analysis (2nd Ed), pp 181-2, John Wiley & Sons * >************************************************** >* (c) Marta Garcia-Granero (september 2005) * >* biostatistics@terra.es * >* Feel free to use or modify this code, but * >* acknowledge the author * >************************************************** > >* Dataset #1: scoring is OK (example provided with SAS at:) > (http://www.id.unizh.ch/software/unix/statmath/sas/sasdoc/stat/chap28/sect4 0.htm) >* (SAS results): >* Statistic (Z) 4.7918 >* Asymptotic Test >* One-sided Pr < Z <.0001 >* Two-sided Pr > |Z| <.0001 >* Sample Size = 161 > >DATA LIST LIST/dose adverse count (3 F8.0). >BEGIN DATA >0 1 26 >0 2 6 >1 1 26 >1 2 7 >2 1 23 >2 2 9 >3 1 18 >3 2 14 >4 1 9 >4 2 23 >END DATA. >VALUE LABEL adverse 1'No' 2'Yes'. >WEIGHT BY count . > >* Using SPSS linear by linear test (close enough) *. >CROSSTABS > /TABLES=dose BY adverse > /FORMAT= AVALUE TABLES > /CELLS= COUNT ROW > /STATISTIC=CHISQ. > >* Cochran-Armitage test with MATRIX *. > >* This code assumes the dependent variable is coded 0/1 (No/Yes) *. >TEMPORARY. >COMPUTE adverse=adverse-1. >AGGREGATE > /OUTFILE = 'c:\temp\aggdata.sav' > /BREAK = dose > /pi = MEAN(adverse) > /ni = N. > >MATRIX. >PRINT/TITLE='Cochran-Armitage Test for Trend (Agresti 2002)'. >GET pi /VAR=pi /FILE='c:\temp\aggdata.sav'. >GET ni /VAR=ni /FILE='c:\temp\aggdata.sav'. >* Scoring (change if required) *. >COMPUTE xi={0; 1; 2; 3; 4}. >* Reports *. >PRINT {xi,ni&*pi,ni} > /FORMAT='F8.1' > /TITLE='Statistics' > /CLABELS='Score i','Count i','N i'. >PRINT MSUM(ni) > /FORMAT='F8.0' > /TITLE='Total sample size'. >* Calculations *. >COMPUTE p=MSUM(ni&*pi)/MSUM(ni). >COMPUTE x=MSUM(xi&*ni)/MSUM(ni). >COMPUTE num=(xi-x)&*ni&*pi. >COMPUTE den=p*(1-p)*ni&*((xi-x)&**2). >COMPUTE z=MSUM(num)/SQRT(MSUM(den)). >COMPUTE zsig=2*(1-CDFNORM(ABS(z))). >* Final report *. >PRINT {z,z**2,zsig,zsig/2} > /FORMAT='F8.3' > /CLABELS='Z','Z˛','2-sig','1-sig' > /TITLE='Statistics & Significance'. >COMPUTE observed={(ni-ni&*pi),ni&*pi}. >COMPUTE expected=RSUM(observed)*CSUM(observed)/MSUM(ni). >COMPUTE chisq=MSUM((observed-expected)&**2/expected). >COMPUTE chisqp=1-CHICDF(chisq,NROW(observed)-1). >COMPUTE reschi=chisq-z**2. >COMPUTE reschip=1-CHICDF(reschi,NROW(observed)-2). >PRINT {chisq,chisqp;reschi,reschip} > /FORMAT='F8.3' > /RLABELS='Pearson','Deviat.' > /CLABELS='Chi˛','Sig.' > /TITLE='Pearson-Chi˛ & Deviation from trend (non-linearity)'. >END MATRIX. > >* As you can't specify scores for CROSSTABS, the key is recoding >* the predictor to asign the scores you want. Suppose your data >* don't reflect the scoring you want to give *. > >* Dataset #2 (Agresti 2002) *. >DATA LIST LIST /alcohol malform count (3 F8.0). >BEGIN DATA >0 1 48 >0 0 17066 >1 1 38 >1 0 14464 >2 1 5 >2 0 788 >3 1 1 >3 0 126 >4 1 1 >4 0 37 >END DATA. > >VALUE LABEL alcohol > 0 ' 0' > 1 '<1' > 2 '1-2' > 3 '3-5' > 4 '>6'. >VALUE LABEL malform 0'Absent' 1'Present'. >WEIGHT BY count . > >* Agresti gives the following scores: 0, 0.5, 1.5, 4, 7 *. > >* The following will not give the results expected (Z˛=6.57 p=0.010): *. > >CROSSTABS > /TABLES=alcohol BY malform > /FORMAT= AVALUE TABLES > /CELLS= COUNT ROW > /STATISTIC=CHISQ. /* LxL test is 1.828 (p=0.176)*. > >* Recoding and re-running *. >TEMPORARY. >RECODE alcohol (0=0) (1=0.5) (2=1.5) (3=4) (4=7) INTO alcscore . >VARIABLE LABELS alcscore 'Scores for alcohol'. >* Using SPSS linear by linear test (close enough) *. >CROSSTABS > /TABLES=alcscore BY malform > /FORMAT= AVALUE TABLES > /CELLS= COUNT ROW > /STATISTIC=CHISQ. /* LxL is now 6.570 *. > >* Cochran-Armitage test with MATRIX *. > >* This time, MALFORM takes the correct values (0/1) *. >AGGREGATE > /OUTFILE='c:\temp\aggdata.sav' > /BREAK=alcohol > /pi = MEAN(malform) > /ni=N. > >MATRIX. >PRINT/TITLE='Cochran-Armitage Test for Trend (Agresti 2002)'. >GET pi /VAR=pi /FILE='c:\temp\aggdata.sav'. >GET ni /VAR=ni /FILE='c:\temp\aggdata.sav'. >* Scoring (change if required) *. >COMPUTE xi={0; 0.5; 1.5; 4; 7}. >* Reports *. >PRINT {xi,ni&*pi,ni} > /FORMAT='F8.1' > /TITLE='Statistics' > /CLABELS='Score i','Count i','N i'. >PRINT MSUM(ni) > /FORMAT='F8.0' > /TITLE='Total sample size'. >* Calculations *. >COMPUTE p=MSUM(ni&*pi)/MSUM(ni). >COMPUTE x=MSUM(xi&*ni)/MSUM(ni). >COMPUTE num=(xi-x)&*ni&*pi. >COMPUTE den=p*(1-p)*ni&*((xi-x)&**2). >COMPUTE z=MSUM(num)/SQRT(MSUM(den)). >COMPUTE zsig=2*(1-CDFNORM(ABS(z))). >* Final report *. >PRINT {z,z**2,zsig,zsig/2} > /FORMAT='F8.3' > /CLABELS='Z','Z˛','2-sig','1-sig' > /TITLE='Statistics & Significance'. >COMPUTE observed={(ni-ni&*pi),ni&*pi}. >COMPUTE expected=RSUM(observed)*CSUM(observed)/MSUM(ni). >COMPUTE chisq=MSUM((observed-expected)&**2/expected). >COMPUTE chisqp=1-CHICDF(chisq,NROW(observed)-1). >COMPUTE reschi=chisq-z**2. >COMPUTE reschip=1-CHICDF(reschi,NROW(observed)-2). >PRINT {chisq,chisqp;reschi,reschip} > /FORMAT='F8.3' > /RLABELS='Pearson','Deviat.' > /CLABELS='Chi˛','Sig.' > /TITLE='Pearson-Chi˛ & Deviation from trend (non-linearity)'. >END MATRIX.