I want to double-check my proposal sample size and power calculation methods
- aim 1, I have 335 participants, cross-sectional study, linear regression, the estimated effect size is 0.45, single-side test, which ways to calculate achieved power? My method is using t-test to calculate.
- aim 2, retrospective cohort study, the estimated effect size is 0.45, single-sided test, which ways will be appropriate to calculate achieved power?
Thank you!
See the PROC POWER documentation for code to do these.
SteveDenham
Hi Steve, thank you for your reply! I have PROC POWER code. However, I guess my question is more related to logic for calculating instead of calculation tools. For example, if the independent variables and dependent variables are continuous. Would it be appropriate if I calculate the achieved power in this way: categorizing independent variables into tertiles --> estimated effect size is 0.45 between 3rd tertile and 1st tertile for dependent variable --> get power around 90% based on sample size = 335?
Well, you did the analysis, so you should have everything you need to calculate a retrospective power (and don't get me started on why that is a bad idea.). I would avoid categorizing a continuous variable for this. This is actually a.ONECORR analysis, since regression is just fancy correlation. You mention an effect size of 0.45. If that is an f-squared estimate, what happens when you plug into this code:
proc power;
onecorr dist=fisherz
nullcorr = 0
corr = 0.557186 /* this is the square root of 0.45/(1+0.45), in other words, the square root of R-squared obtained by rearranging f-squared */
ntotal = 335
power = .;
sides = 1
run;
If your effect size is obtained some other way (omega squared, for instance), then ONEWAYMEANS is probably better suited to the task.
SteveDenham
Thank you so much, Steve! I was asked to show the achieved power by professor lol, although 335 is all I have.
Given the various effect sizes I plugged in, 28 is going to give at least 80% power for a one-sided test, at an effect size of 0.45 (R squared, or.0.55 F squared) and alpha=0.05.
SteveDenham
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