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]

Hi @thiennguyen : You want to show your treatment’s proportional effect is 0.74 or more. Correct? 
So, Ha is p>=0.74. Ha is always what you want to show. Then, HO is the opposite of Ha. HO is p<0.74. So this is a one sided test. Your assumed proportion must be somewhere in Ha. After all, if you don’t think Ha is true, then why do the study? Furthermore, if your assumed p is in H0 you will have power <= 0.05 (and if you assume p=0.74, then power = alpha = 0.05). So, to answer your question, no you don’t have it right :)

[#3]

Yes, I am aware of my error. However, the JMP dialogue box with two text fields for hypothesis proportion is beyond me! I don't understand them.

I am hoping someone can help me figure out my research sample size calculation and explain the JMP dialogue box above!

[#4]

Hi @thiennguyen . Apologies for not making this more clear. The assumed proportion is H0 p < 0.74. The alternative proportion is something larger than 0.74. Apologies for confusing the inputs, I don’t have access to my computer now. 

[#5]

I want to choose Ho = 0.5. That means that the treatment method is effective with Ha > 0.5.
But my problem is how I can calculate sample size from previous research, which showed that the effect of drug was 0.74 (74% of treatment group)

[#6]

What do you want to prove (be able to claim) from this study?

[#7]

I want to prove that drugs are effective as treatments for disease.

[#8]

How do you define “effective”? p>0.5? Weren’t you wanting to demonstrate an effect size at least as large as the benchmark for p of at least 0.74?

[#9]

yes, I define the effecive is p>=0.5. And previous research showed that it's 0.74.

No, I don't need sample size for comfortable at least 0.74 result. But I don't understand how JMP work to calculate sample size with two parameters to type: Assumed Proportion and Alternative Proportion!

[#10]

There’s the (1) thing you want to prove (Ha p > 0.5), and there is (2) what you actually believe about your treatment. So, if you want to prove p > 0.5, the alternative p must be greater than 0.5. What do you believe your treatment p to be? 0.55, 0.6, 0.7, …? I.e, if you want to claim p > 0.5, it needs to actually be > 0.5. And remember, the p we are talking about is the population p, not the sample p. Do you think it is 0.74 for your treatment?