mmrm package vs proc mixed in SAS

[DrLynTaylor]

• Add more detail into SAS MMRM page (inc. lsmeans)
• Check R pages includes emmeans & contrast adjustment
• check comparison compares proc mixed vs mmrm (not just proc glimmix).

First, we fit a MMRM model.
library(mmrm)
library(emmeans)

fit <- mmrm(
 formula = FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID),
 reml = TRUE, method = "Kenward-Roger", vcov = "Kenward-Roger-Linear",
 data = fev_data
)
summary(fit)

And then compute the least-square means with corresponding CI.
ems <- emmeans(fit, ~ ARMCD | AVISIT)
confint(ems)

Suppose we want to answer the question that the null hypothesis is that treatment minus placebo equals zero. The contrast can be created below.
contr <- contrast(ems, adjust = "none", method = "pairwise")
contr

Suppose we would like to test the hypothesis that treatment is superior to placebo with a margin of 2.
test(contr, null = 2, side = ">")

proc mixed data=fev_data;

class ARMCD(ref='PBO') AVISIT RACE SEX USUBJID;

model FEV1 = RACE SEX ARMCD ARMCD*AVISIT / ddfm=KR;

repeated AVISIT / subject=USUBJID type=UN r rcorr;

lsmeans ARMCD*AVISIT / cl alpha=0.05 diff slice=AVISIT;

lsmeans ARMCD / cl alpha=0.05 diff;

lsmestimate ARMCD*AVISIT [1,1 4] [-1,2 4] / cl upper alpha=0.025 testvalue=2;


ods output lsmeans=lsm diffs=diff LSMEstimates=est;

run;