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 |
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;
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.
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?
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