I am performing analysis on data of ~8,000 patients who have COVID. This is my scenario. I have information of each patient being classified as either severe or non-severe in how COVID-19 affected them. I also have a set of all the mutations that each patient had.
Thus, I generated frequencies as such: Frequency = P(Having Mutation X and a Severe Clinical Response) divided by P(Mutation X). I did this for all mutations that have at least 30 patients to satisfy proper conditions for the statistical test.
From my original dataset of 8,000 patients, I know that about 1,000 had a severe clinical response. Thus, my null hypothesis is that for any given mutation the frequency, as calculated above, should be equal to 1/8 if it has no correlation to a severe response to COVID. What is the best way to perform a statistical test that can compare the severity frequency of each mutation to the population proportion (1/8) and also account for sample size (i.e. if one mutation has a frequency of 0.9, but only 30 patients have it, it's likely not as significant as a mutation which 5,000 patients have even if the frequency is lower, at 0.5).
I am thinking of doing a chi-squared test for homogeneity but am not sure about how to go about it because I don't think it compares to the proportion of the entire dataset and I'm not sure if it accounts for sample size either.
I've already performed feature selection with machine learning so I am solely looking to analyze the data with a statistical technique. Thanks.