sample size calculation based on an RxC table

[superbibi]

Hi Friends,

I have this table from a previous study showing the questionnaire outcomes of two groups of people with chi-square test.

The table shows the percentage of participant response at each score. 

How can I use this information to calculation the sample size for a new study in order to gain a significant different between two randomized groups with 1:1 allocation ratio, type I error = 0.05 and power = 0.8? 

Thank you.

	score	0	1	2	3	4	5	6	7	8	9	10
study  (n= 25)	percentage of participant of that score	0	0	0	0	0	0.8	0.2	0	0	0	0
control (n= 25)	percentage of participant of that score	0	0	0	0	0	0	0.1	0	0.3	0.2	0.4

[Reeza]

I think you want PROC POWER.

https://www.lexjansen.com/wuss/2016/105_Final_Paper_PDF.pdf

[superbibi]

Thank you for the response.
I also know this link is great for RxC table. http://support.sas.com/kb/25/013.html
But it assumes not exactly the method I want. I am wondering how can use all the information in this RxC table to do the sample size calculation.

[Reeza]

That link is a teenager (2005?). If you want to use all the information, then in my experience, no proc will work, you have to simulate the data and CI and power you want.

[superbibi]

Thank you. Do you think I should assume a meaningful difference of probability regarding this kind of sample size assumption? For example, in this case I use the information in question 6, which showed 0.2 and 0.1 at study and control group, and I wrote the code as below. 

Do you think this method appropriate?

proc power;
      twosamplefreq test=pchi
         groupproportions = (.20 .10)
         nullproportiondiff = .10 to 0.40 by 0.1
         npergroup=.
         alpha = 0.05
         power = 0.8;
   run;

[FreelanceReinh]

If you asked me I'd say now you go in the opposite direction and use less information from the table. Then your sample sizes will tend to be larger than before.

[FreelanceReinh]

Hi @superbibi,

Many thanks for the link. Apparently I wasn't aware of this macro when I posted Re: proc power for multiple groups in 2016 (includes sample size calculation and a simulation). In that thread the RxC table was transposed (two proportions, more than two groups) and the likelihood-ratio chi-square test was used. It seems to me (at first glance) that the formula from Chow et al. (2008) I applied there doesn't use more information from the RxC table than the powerRxC macro. Couldn't it be too restrictive to use more information?