500 patients had an operation to remove stomach cancer. There exists a clinical guideline that suggests that a minimum of ten lymph nodes should be removed during the operation. I am therefore interested in the proportion of patients who had at least ten nodes removed. The variable
Fewer_or_more_than_ten contains this information. I am using R.
# Example data set.seed(10) study_data <- data.frame( Gender = sample(c("Male","Female"),500,T), Fewer_or_more_than_ten = sample(c("Fewer","More"),500,T))
I can tabulate these data to see how many people got an adequate lymph node dissection (i.e., had more than ten nodes removed).
# Tabulate data table(study_data$Fewer_or_more_than_ten)
I want to know whether this varies with gender, so I can type:
# Stratify by gender table(study_data$Gender, study_data$Fewer_or_more_than_ten)
What I want to do is to report the percentage of women and men who got an adequate lymph node dissection. So for that I would report the percentage shown under the "More" column in the below table.
# Calculate frequencies prop.table(table(study_data$Gender, study_data$Fewer_or_more_than_ten), 1) * 100
My question is:
I would also like to report a p value with these numbers to show whether or not there appears to be a significant difference between the proportion of men versus women who got an adequate dissection. How should I do this?
I wasn't sure whether I should try to calculate confidence intervals for the percentages and then compare them to see if they overlap, or if I should apply a Chi-square test or Fisher's exact test to the tabulated data, etc.
I am also curious, if gender could take on three values rather than two (e.g., Male, Female, and Unknown), and I wanted to extend my hypothesis test to these three groups, how would that change my approach?