Help about sample size calculation

[#1]

For my study, the primary endpoint is to determine the proportion of patients who experience a beneficial effect after receiving the treatment. We have established a null hypothesis proportion (𝐻0) of 0.5, indicating our expectation that approximately 50% of the patients will exhibit a beneficial response to the treatment post-intervention. This figure serves as a clinically relevant cut-off point for effectiveness in our analysis.

Furthermore, previous research has shown a proportional effect of 0.74 for similar treatments. We have consequently chosen this figure as our alternative hypothesis (𝐻𝑎​), proposing that the new drug will demonstrate an effect size at least as large as this benchmark.

Given these parameters, we have conducted preliminary sample size calculations to ensure adequate power for our study. Nonetheless, we seek validation of our methodology and assistance to confirm that our approach aligns with best practices and that our sample size is sufficient to detect a significant difference between the null and alternative hypotheses.

I typed it to JMP (in the picture) as 0.5 and 0.74 and have a result of 35
I want to ask the community if I am doing the right thing about the sample size calculation?

[#2]

No, I don't think so, but I need, based on similar previous research, to have a proportion to type into JMP dialogue. 

[#3]

Hi @thiennguyen   Yes, your sample size depends on what truth is (along with alpha, H0, Ha, etc.). Think about it this way. Let’s suppose for the moment that there are two options for your “alternative  proportion” in the JMP dialogue:

A: p = 0.51. 
B: p = 0.95

And alpha = 0.05, with H0: p = 0.5 vs. Ha: p > 0.5.

Which, A or B, would require a larger sample size to claim Ha: p > 0.5?

[#4]

Of course that Ho and Ha are very closely, It is need the larger sample size. So 0.5 and 0.74 with a sample size of 35 are available for my research. I mean, I don't understand how JMP calculates 35.

[#5]

Hi @thiennguyen  The calculations get complicated in a hurry. But here is JMP’s description of how it’s calculated.

https://www.jmp.com/support/help/en/17.2/#page/jmp/power-for-one-sample-proportion.shtml

[#6]

I will read it. Honestly, this is an aspect of which I am very weak. I sent you a message. I hope you can respond to it

[#7]

Hi @thiennguyen   Here is an example. https://www.jmp.com/en_gb/learning-library/topics/basic-inference--proportions-and-means/sample-size...

I prefer the exact method. Others may disagree.

[#8]

Your question and analysis are based on a 'simple random sample.' is that kind of sample appropriate? How will this sample be drawn or selected? How will you control for covariates such as sex and age? For what population do you hope to conclude with this sample and study?