anilbattalahalli / smartutils Goto Github PK

Right now, it seems as if beta[2] is providing the effect estimate of the the effect of A_2 given non-response rather than just the effect of A_2. If you run 1000 simulations, then the mean effect estimate of beta[2] = true beta[2]/(1 - r) where r denotes the probability of response. I can't seem to figure out why this is occurring. The other three coefficients seem to be correct.
Code:

Results:

True Betas:

I'm currently trying to specify a particular variable, y_adj, as my outcome variable in cSMART.mm() and I think there's something off with how the function handles the argument Y = "y_adj". I specify it as follows:

report <- cSMART.mm(Y ~ A1 + A2 + I(A1 * A2), df1, verbose = TRUE, covstr = 'IND', i = "school", Y = "y_adj")

but that provides the same output as when I use:

report <- cSMART.mm(Y ~ A1 + A2 + I(A1 * A2), df1, verbose = TRUE, covstr = 'IND', i = "school", Y = "Y")

(and note that it provides an error when I start with cSMART.mm(y_adj ~ ...). When I instead recode y_adj as simply Y, I obtain different (and presumably, correct) output.

How an end-user should approach specifying the formula argument in the cSMART.mm function might need to be revisited.

      if (R == 1) {
        treat_code <- "-1,1,."
        A2 <- 1
      }

and

     if (R == 1) {
        treat_code <- "1,1,."
        A2 <- 1
      }