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+#Acknowledgement: This material is based on a workshop organized by CausalLab Karolinska Institutet with Anthony Matthews and Conor MacDonald  
+
+library(data.table)
+library(MASS)
+#Loading the data
+library(haven)
+library(here)
+
+
+### load in example data
+targetpop <- read.csv2(here("data", "targetpop.csv")) 
+View(targetpop)
+
+studypop <- read.csv2(here("data", "studypop.csv"))
+View(studypop)
+summary(studypop)
+
+
+table(studypop$treatment, studypop$Y)
+prop.table(table(studypop$treatment, studypop$Y))
+table(studypop$treatment, studypop$Y)
+YA = 4698/(4698+85343)
+YB = 6321/(6321+84129)
+RD = YB - YA
+RR = YB / YA
+
+#risk difference according to hypertension status
+# no hypertension
+studypop_hta0 = subset(studypop, hypertension == 0)
+prop.table(table(studypop_hta0$treatment, studypop_hta0$Y))
+table(studypop_hta0$treatment, studypop_hta0$Y)
+YA0 = 915/(915+31519)
+YB0 = 1122/(1122+31453)
+RD0 = YB0 - YA0
+RR0 = YB0 / YA0
+
+
+# with hypertension
+studypop_hta1 = subset(studypop, hypertension == 1)
+prop.table(table(studypop_hta1$treatment, studypop_hta1$Y))
+table(studypop_hta1$treatment, studypop_hta1$Y)
+YA1 = 3783/(3783+53824)
+YB1 = 5199/(5199+52676)
+RD1 = YB1 - YA1
+RR1 = YB1 / YA1
+
+# distribution of hypertension in the trial and target population
+summary(studypop$hypertension)
+summary(targetpop$hypertension)
+
+#Fitting our model in the trial data, including interactions between treatment and effect modifiers
+logistic_model <- glm('Y ~ treatment*(Sex + age + hypertension + heartdisease)', data = studypop, family = binomial())
+
+#Making events occur in the target population under each treatment and assign them the correct treatment
+### explain a little here
+targetpopA = targetpop
+targetpopA$treatment = "A"
+targetpopA$predA <- predict(logistic_model, type = "response", newdata = targetpopA)
+
+targetpopB = targetpop
+targetpopB$treatment = "B"
+targetpopB$predB <- predict(logistic_model, type = "response", newdata = targetpopB)
+
+#estimate the average risk under each treatment 
+YA = mean(targetpopA$predA)
+YB = mean(targetpopB$predB)
+
+# estimate the average treatment effects
+RD = YB - YA
+RR = YB/YA
+
+RD
+RR