Sampling out of tables depending on other variables (R) - r

Question

I am a physician just who just started working in R and appreciate any help in this question:
i have 2 tables (A, B) with the variables age (continous), sex (binary) and test_value (binary). Each table has a different age and sex distribution.
set.seed(10)
AgeA <- round(rnorm(100, mean = 40, sd = 15))
SexA <- sample(c("M","F"), 100, replace = TRUE, prob = c(0.5, 0.5))
Test_ValueA <- rbinom(100, 1, 0.3)
set.seed(20)
AgeB <- round(rnorm(1000, mean = 50, sd = 15))
SexB <- sample(c("M","F"), 1000, replace = TRUE, prob = c(0.5, 0.5))
Test_ValueB <- rbinom(1000, 1, 0.4)
A <- data.frame(Age = AgeA, Sex = SexA, Test = Test_ValueA)
B <- data.frame(Age = AgeB, Sex = SexB, Test = Test_ValueB)
genderA<-(prop.table(table(A[,2])))
TestA<-(prop.table(table(A[,3])))
paste("median age in group A is",median(A[,1]), "percentage female in group A is",genderA[1], "percentage of test positive in A is", TestA[2])
genderB<-(prop.table(table(B[,2])))
TestB<-(prop.table(table(B[,3])))
paste("median age in group A is",median(B[,1]), "percentage female in group B is",genderB[1], "percentage of test positive in A is", TestB[2])
The difference in test-proportion is now confounded by age and sex.
now i would like to match the patients from table A with table B to adjust for age and sex. because B is the smaller cohort i would prefer to sample out of A and match to B. is the match package an option? any other ideas
hopefully I was able to explain my problem.
any hints to which functions this may point?

Answer 1

Hello i have a possible answer, I will build two populations of a 100 people with the characteristics you said
set.seed(10)
AgeA <- rnorm(100, mean = 30, sd = 10)
#population A is 0.8 percent male
SexA <- sample(c("M","F"), 100, replace = TRUE, prob = c(0.5, 0.5))
Test_ValueA <- rbinom(100, 1, 0.5)
set.seed(20)
AgeB <- rnorm(100, mean = 30, sd = 10)
#population B is 0.8 percent male
SexB <- sample(c("M","F"), 100, replace = TRUE, prob = c(0.8, 0.2))
Test_ValueB <- rbinom(100, 1, 0.3)
A <- data.frame(Age = AgeA, Sex = SexA, Test = Test_ValueA)
B <- data.frame(Age = AgeB, Sex = SexB, Test = Test_ValueB)
Then using dplyr you can summarise population B parameters:
library(dplyr)
Bsummary <- group_by(B,Sex)
Bsummary <- summarise(Bsummary, PercenteagePositive = sum(Test == 1)/length(Test == 1), PercenteageSex = n()/100)
Bsummary
If you look at the results of this B is 76% male and 24% female, if you sampled 20 people from A you would have to sample 15 males and 5 females. First you separate the population of A on males and females:
Amale <- filter(A, Sex == "M")
Afemale <- filter(A, Sex == "F")
And from that you sample 15 males and 5 females:
SampleAMale <- Amale[sample(nrow(Amale), 15), ]
SampleAFemale <-Afemale[sample(nrow(Afemale), 5), ]
Then join them and you can summarise Them
sampleA <- rbind(SampleAMale, SampleAFemale)
ASampleSummary <- group_by(sampleA,Sex)
ASampleSummary <- summarise(ASampleSummary, PercenteagePositive = sum(Test == 1)/length(Test == 1), PercenteageSex = n()/100)

Answer 2

OK Fank I think you will like this answer a little better, the first part is the same, exept that the AGE IS ROUNDED:
set.seed(10)
AgeA <- round(rnorm(100, mean = 30, sd = 2))
#population A is 0.8 percent male
SexA <- sample(c("M","F"), 100, replace = TRUE, prob = c(0.5, 0.5))
Test_ValueA <- rbinom(100, 1, 0.5)
set.seed(20)
AgeB <- round(rnorm(100, mean = 30, sd = 2))
#population B is 0.8 percent male
SexB <- sample(c("M","F"), 100, replace = TRUE, prob = c(0.8, 0.2))
Test_ValueB <- rbinom(100, 1, 0.3)
A <- data.frame(Age = AgeA, Sex = SexA, Test = Test_ValueA)
B <- data.frame(Age = AgeB, Sex = SexB, Test = Test_ValueB)
now you just use prop.table to get the proportions of your population. Lets say you want to sample a 1000 individuals from B in the same proportion as A in terms of AGE and SEX you do this.
1000*(prop.table(table(A[,1:2])))
then by applying filters you can sample within groups:
for example if you want to get only the males age 30 in group B you could go
BMale30 <- filter(B, Sex == "M" & Age == 30)