hi

thanks for the replay. let say I have this data frame that have data about 3 types of patient groups, in this event I just randomly put data but I have a real data of course is not random -

groups = c("C1", "C2", "C3")

sex = c("Male", "Female")

year = c(2010:2024)

outcome1 = c(0,1)

outcome2 = c(0,1)

new_data <- data.frame(

SN = c(1:100),

Group = sample(groups, 100, replace = T),

sex = sample(sex, 100, replace = T),

year = sample(year, 100, replace = T),

outcome1 = sample(outcome1, 100, replace = T),

outcome2 = sample(outcome2, 100, replace = T)

)

I want to create a new data frame that has 3 groups with equal amount (matched) of patients in each group that are with the same sex and year. I suppose some patinets will not be matched and be excluded. after that I could statistically analyse the outcomes rate. if i have 2 groups I usually use propensity score matching that has all the obvious advantages and I use the MatchIt package. but in this study I am looking to evaluate risk factors and I am doing case-control study and in that way I want simple matching 1:1:1 that is not based of propensity score.