Well I just realized this is definitely in the wrong forum can someone please delete this thread? I can't seem to find an option for that myself..

Sorry if this is in the wrong thread. I am trying to determine if a research project is feasible to conduct by calculating the sample size needed.

I am seeing if a new protocol is more effective than an established protocol via an randomized controlled trial. Outcomes will be measured via a definition we established for a "complication" which can be a variety of things. A previous study stated that the complication rate for the established protocol is about 92%. I am trying to see how many people I would need to follow in order to see a reduction by 20% in the complication rate, so 72% complication rate. My power is going to be 80%.

I've been trying to use PS (independent t-test) with an alpha=0.05, power=0.8, delta(difference in mean)=0.2, sigma(STD)=?, m(ratio of control to experimental)= 1

but it requires me to input a standard deviation of which I don't have since I haven't measured anything and the previous study did not give a STD. Everything else I think I have except the STD.

I'm really rusty with statistics so any help would be appreciated. Sorry if this post is confusing / not clear.

Last edited by efficientfuel; 07-16-2014 at 02:25 PM.

hi,
I would do this iteratively: do a pre-sample of a convenient size, do the test and run the power calculation to see, what would be the smallest effect size you could hope to see, using the std estimated from the sample. If you do not see an effect and the smallest effect size is interesting from a practical POV estimate a new sample size with the std from the sample and repeat.