JK,
Different opinions and approaches on the subject of statistical significance: the sample size depends on the number of variables. As you can see below if there are three 30 would be adequate for regression analysis (your example).
https://www.statisticssolutions.com/sample-size-formula/
However, normally we use the p statistics based on effect size. For an alpha of 0.05 and an effect size of 0.1, 20 patients would suffice. Of course, if one wants a different effect the number should be changed (increased or decreased) and what you propose or other number will be necessary.
The importance of more patients go straight to this point: we are building the irrefutable proof that Leronlimab has therapeutic benefits ( we all know this by now , however FDA needs to be shown that with numbers).
Regarding the progression free survival, as I understand, patients #1 and #2 (m-i-l) where emergency patients and, as far as we know, thankfully for heir families, both are alive. Of course, as far as FDA is concerned, they need numbers and tumor reduction and CTC counts are proof of improved PFS. That is why this is so important in other patients (including #5) and thereafter 6 and 7.
Every patient that shows a low CTC count adds a large weight in the burden of proof that we need to clear with FDA.
Different opinions and approaches on the subject of statistical significance: the sample size depends on the number of variables. As you can see below if there are three 30 would be adequate for regression analysis (your example).
https://www.statisticssolutions.com/sample-size-formula/
Quote:One of the most frequent problems in statistical analysis is the determination of the appropriate sample size. One may ask why sample size is so important. The answer to this is that an appropriate sample size is required for validity. If the sample size it too small, it will not yield valid results. An appropriate sample size can produce accuracy of results. Moreover, the results from the small sample size will be questionable. A sample size that is too large will result in wasting money and time. It is also unethical to choose too large a sample size. There is no certain rule of thumb to determine the sample size. Some researchers do, however, support a rule of thumb when using the sample size. For example, in regression analysis, many researchers say that there should be at least 10 observations per variable. If we are using three independent variables, then a clear rule would be to have a minimum sample size of 30. Some researchers follow a statistical formula to calculate the sample size.
However, normally we use the p statistics based on effect size. For an alpha of 0.05 and an effect size of 0.1, 20 patients would suffice. Of course, if one wants a different effect the number should be changed (increased or decreased) and what you propose or other number will be necessary.
The importance of more patients go straight to this point: we are building the irrefutable proof that Leronlimab has therapeutic benefits ( we all know this by now , however FDA needs to be shown that with numbers).
Regarding the progression free survival, as I understand, patients #1 and #2 (m-i-l) where emergency patients and, as far as we know, thankfully for heir families, both are alive. Of course, as far as FDA is concerned, they need numbers and tumor reduction and CTC counts are proof of improved PFS. That is why this is so important in other patients (including #5) and thereafter 6 and 7.
Every patient that shows a low CTC count adds a large weight in the burden of proof that we need to clear with FDA.
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