The method used for sample size estimation must match the method you will eventually use for analyzing the data.  What type of analysis do you intend to carry out?  I assume the outcome variable is waiting time. (Note that this could be thought of as a time-to-event analysis, aka. survival analysis.)   What are the explanatory variables?  How large an effect (i.e., difference, ratio or whatever the measure of effect is) would be considered practically important? 

HTH.

Hi, Good Afternoon,

For sample si\e calculation, you can find like below.

One of the most common questions asked of a statistician about design is the number of patients to include. It is an important question, because if a study is too small it will not be able to answer the question posed, and would be a waste of time and money. It could also be deemed unethical because patients may be put at risk with no apparent benefit. However, studies should not be too large because resources would be wasted if fewer patients would have sufficed. The sample size depends on four critical quantities: the type I and type II error rates α and β(discussed in Chapter 5), the variability of the data σ², and the effect size d. In a trial the effect size is the amount by which we would expect the two treatments to differ, or is the difference that would be clinically worthwhile.

Usually α and β are fixed at 5% and 20% (or 10%), respectively. A simple formula for a two group parallel trial with a continuous outcome is that the required sample size per group is given by for two sided α of 5% and β of 20%.

For example: In a trial to reduce blood pressure, if a clinically worthwhile effect for diastolic blood pressure is 5 mmHg and the between subjects standard deviation is 10 mmHg,

we would require,

                 n = 16 x 100/25

                   = 64 patients per group in the study.

The sample size goes up as the square of the standard deviation of the data (the variance) and goes down inversely as the square of the effect size. Doubling the effect size reduces the sample size by four.  It is much easier to detect large effects! In practice, the sample size is often fixed by other criteria, such as finance or resources, and the formula is used to determine a realistic effect size. If this is too large, then the study will have to be abandoned or increased in size. Machin et al. give advice on a sample size calculations for a wide variety of study designs.

Thank you,  Any help you want regarding this sample size kindly contact me.

You can use G*Power in order to determine α, β, N and the effect size. G*Power can compute any one of these four values given the other three. It is a free software.

I don't know, I usually use parametric tests, and the normlity of the distribtuion is a main assumption in this case.Maybe we will find more information in the G*Power manual. Here is the link

http://www.gpower.hhu.de/en.html

None of the replies provide specific answer  to my query, Please Help ???

@ Fausto Galetto · 23.94 · 2.26 · Politecnico di Torino 

If you re-read my earlier post, you'll notice that I did not actually suggest a method based on something other than waiting time.  I simply asked for additional information.  ;-)

@ Saurabh Bobdey · 1.83 · Indian Navy 

I repeat my earlier questions: