About JanetSultana

JanetSultana · ‎10-02-2018

Hi, Thanks for your feedback. I've tried using: Code data test; infile datalines; input year case n; output test; datalines; 2000 100 50000 2005 75 60000 2010 50 75000; run; data test; set test; perc=round(((case/n)*100),0.01); run; proc freq data = test; table perc*year / exact trend; run; The p-value that I'm looking for seems to be the Mantel-Haenszel Chi-Square in the output (attached). I'm curious as to why the p-value using the proc genmod is significant while the p-value from the proc freq is not: Code: data test(keep=year case log_n); infile datalines; input year case n; log_n=log(n); output test; datalines; 2000 100 50000 2005 75 60000 2010 50 75000 ; run; proc genmod data=test; model case=year / dist=poisson link=log offset=log_n; run; SAS Output for proc genmod attached. Do you have any idea why the p-values are different? I would like to be sure which one is correct to use / what the correct interpretation is. Thanks, Jane Proc freq Proc genmod

JanetSultana · ‎09-27-2018

Hello! I need to conduct a test for trend on percentages to get a p-value. I found the following code in a SAS community website which is similar to the data I have: data test(keep=year case log_n); infile datalines; input year case n; log_n=log(n); output test; datalines; 2000 100 50000 2005 75 60000 2010 50 75000; run; proc genmod data=test; model case=year / dist=poisson link=log offset=log_n; run; A screenshot of the output is attached. Is it correct to say that there is a significant difference between the proportions (cases/log_n) in the 3 years based on the Pr>ChisSq <0.001? I'm not convinced about this output because the DF for year is 1. Also, I don't understand why one would want to get the log of the denominator but not of the cases. Many thanks! Janet

JanetSultana · ‎03-23-2016

Thank you very much for your reply- its exactly what I needed! I do have some final questions about how to interpret the output: I imagine that I have to use the results from the Likelihood Ratio Chi-Square rather than the Chi-Square since I’m interested in the ratios, correct? (Although they were really similar) The default null hypothesis is be that there is no significant difference between the ratios, right? SAS warns me that 63% of my data is missing- this would make sense because of the unequal sample sizes. I wonder if this is a problem as far as the reliability of the results are concerned or how cautious I should be in interpreting the results? Thanks again for your quick reply, I learned something new which I can apply elsewhere !! 🙂

JanetSultana · ‎03-22-2016

Hi, I’m trying to compare sex distributions among different data collection centres using the chi squared (chisq) option in SAS (9.2) but there are different numbers of observations for every data collection centre, which the resulting cross-tabs can’t account for. I’m still a beginner in statistics in SAS, but the only procedure that I know of which allows you to account for unequal sample sizes is proc anova (lines statement) and this is not the right test for this comparison. Would anyone be kind enough to share their insight on this? Thank you! The following code creates an example of my data. DATA example; INFILE datalines; INPUT centre_A centre_B centre_C centre_D centre_E; DATALINES; 1 1 0 1 1 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 1 1 1 1 1 1 1 0 1 . 1 0 0 . . 1 0 . . . 1 1 . . . 0 0 . . . 0 1 . . . ; run;

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Date Last Visited	‎10-04-2018 09:18 AM

Re: Is it correct to use proc genmod when testing for trends?

Is it correct to use proc genmod when testing for trends?

Re: Chi squared test for unequal sample sizes

Chi squared test for unequal sample sizes

Re: Is it correct to use proc genmod when testing for trends?

Is it correct to use proc genmod when testing for trends?

Re: Chi squared test for unequal sample sizes

Chi squared test for unequal sample sizes