## Generating the treatment/exposure

Treatment assignment can be accomplished through the original data generation process, using defData and genData. However, the functions trtAssign and trtObserve provide more options to generate treatment assignment.

### Assigned treatment

Treatment assignment can simulate how treatment is made in a randomized study. Assignment to treatment groups can be (close to) balanced (as would occur in a block randomized trial); this balancing can be done without or without strata. Alternatively, the assignment can be left to chance without blocking; in this case, balance across treatment groups is not guaranteed, particularly with small sample sizes.

First, create the data definition:

def <- defData(varname = "male", dist = "binary", formula = 0.5, id = "cid")
def <- defData(def, varname = "over65", dist = "binary", formula = "-1.7 + .8*male",
def <- defData(def, varname = "baseDBP", dist = "normal", formula = 70, variance = 40)

dtstudy <- genData(330, def)

Balanced treatment assignment, stratified by gender and age category (not blood pressure)

study1 <- trtAssign(dtstudy, n = 3, balanced = TRUE, strata = c("male", "over65"),
grpName = "rxGrp")

study1
##      cid rxGrp male over65  baseDBP
##   1:   1     2    0      0 79.41260
##   2:   2     1    0      0 65.07555
##   3:   3     1    1      0 63.93037
##   4:   4     3    1      0 64.90775
##   5:   5     3    1      0 69.86494
##  ---
## 326: 326     3    0      0 59.88214
## 327: 327     2    0      1 70.78260
## 328: 328     2    1      0 76.29750
## 329: 329     3    1      0 65.01489
## 330: 330     3    0      1 71.29769

Balanced treatment assignment (without stratification)

study2 <- trtAssign(dtstudy, n = 3, balanced = TRUE, grpName = "rxGrp")

Random (unbalanced) treatment assignment

study3 <- trtAssign(dtstudy, n = 3, balanced = FALSE, grpName = "rxGrp")

Comparison of three treatment assignment mechanisms

### Observed treatment

If exposure or treatment is observed (rather than randomly assigned), use trtObserved to generate groups. There may be any number of possible exposure or treatment groups, and the probability of exposure to a specific level can depend on covariates already in the data set. In this case, there are three exposure groups that vary by gender and age:

formula1 <- c("-2 + 2*male - .5*over65", "-1 + 2*male + .5*over65")
dtExp <- trtObserve(dtstudy, formulas = formula1, logit.link = TRUE, grpName = "exposure")

Here are the exposure distributions by gender and age:

Here is a second case of three exposures where the exposure is independent of any covariates. Note that specifying the formula as c(.35, .45) is the same as specifying it is c(.35, .45, .20). Also, when referring to probabilities, the identity link is used:

formula2 <- c(0.35, 0.45)

dtExp2 <- trtObserve(dtstudy, formulas = formula2, logit.link = FALSE, grpName = "exposure")