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This vignette explains how brms.mmrm conducts posterior
inference on a fitted MMRM
model using estimated marginal means.
Throughout this vignette, we use the mmrm package’s
fev_data dataset, a simulation of a clinical trial in which
chronic obstructive pulmonary disease (COPD) patients (variable
USUBJID) were randomized to different treatment groups
(variable ARMCD) and measured across four discrete time
points (variable AVISIT). The given response variable is
forced expired volume in one second (FEV1), and we are
interested in the FEV1 change from baseline to each time
point (derived variable FEV_CHG). For this vignette, we
impute missing responses in order to simplify the discussion.
library(brms.mmrm)
library(dplyr)
library(tidyr)
data(fev_data, package = "mmrm")
data <- fev_data |>
group_by(USUBJID) |>
complete(AVISIT) |>
arrange(AVISIT) |>
fill(
any_of(c("ARMCD", "FEV1_BL", "RACE", "SEX", "WEIGHT")),
.direction = "downup"
) |>
mutate(FEV1 = na.locf(FEV1, na.rm = FALSE)) |>
mutate(FEV1 = na.locf(FEV1, na.rm = FALSE, fromLast = TRUE)) |>
ungroup() |>
filter(!is.na(FEV1)) |>
mutate(FEV1_CHG = FEV1 - FEV1_BL, USUBJID = as.character(USUBJID)) |>
select(-FEV1) |>
as_tibble() |>
arrange(USUBJID, AVISIT) |>
brm_data(
outcome = "FEV1_CHG",
baseline = "FEV1_BL",
group = "ARMCD",
patient = "USUBJID",
time = "AVISIT",
covariates = c("RACE", "SEX", "WEIGHT"),
reference_group = "PBO",
reference_time = "VIS1"
)data
#> # A tibble: 788 × 10
#> USUBJID AVISIT ARMCD RACE SEX FEV1_BL WEIGHT VISITN VISITN2 FEV1_CHG
#> <chr> <fct> <fct> <fct> <fct> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 PT10 VIS1 PBO Black or A… Fema… 57.7 0.795 1 -0.394 -12.7
#> 2 PT10 VIS2 PBO Black or A… Fema… 57.7 0.823 2 -0.0593 -12.7
#> 3 PT10 VIS3 PBO Black or A… Fema… 57.7 0.594 3 1.10 -12.7
#> 4 PT10 VIS4 PBO Black or A… Fema… 57.7 0.207 4 0.763 -12.7
#> 5 PT100 VIS1 PBO Black or A… Fema… 51.8 0.362 1 1.59 -17.2
#> 6 PT100 VIS2 PBO Black or A… Fema… 51.8 0.404 2 0.0450 -12.5
#> 7 PT100 VIS3 PBO Black or A… Fema… 51.8 0.504 3 -0.715 -11.2
#> 8 PT100 VIS4 PBO Black or A… Fema… 51.8 0.201 4 0.865 -11.2
#> 9 PT102 VIS1 PBO Asian Fema… 52.2 0.577 1 -0.416 -16.6
#> 10 PT102 VIS2 PBO Asian Fema… 52.2 0.227 2 -0.376 -8.48
#> # ℹ 778 more rowsAccording to Lenth (2016), marginal
means (formerly “least-squares means”) are predictions (usually averaged
predictions) at each point in a reference grid. The reference grid
declares combinations of levels of factors of interest. In a clinical
trial with repeated measures, we are often interested in the mean
response at each combination of treatment group and discrete time point.
For our FEV1 dataset, we are interested in the mean of
FEV1_CHG and its standard error for each combination of
treatment group and time point.1 In other words, we want to estimate the
mean FEV1_CHG for group "TRT" time
"VIS1", the mean FEV1_CHG for group
"TRT" time "VIS2", and so on.2 We represent our goals
in a reference with one row per marginal mean of interest and columns
with the levels of the factors of interest.
reference_grid <- distinct(data, ARMCD, AVISIT)
reference_grid
#> # A tibble: 8 × 2
#> ARMCD AVISIT
#> <fct> <fct>
#> 1 PBO VIS1
#> 2 PBO VIS2
#> 3 PBO VIS3
#> 4 PBO VIS4
#> 5 TRT VIS1
#> 6 TRT VIS2
#> 7 TRT VIS3
#> 8 TRT VIS4It is seldom trivial to estimate marginal means. For example, the
following parameterization includes an intercept term, additive terms
for each level of each factor, interactions to capture non-additive
relationships among factors, continuous covariates, and different
FEV1_BL slopes for different time points. Here, there is no
model coefficient that directly corresponds to a marginal mean of
interest. Even terms like AVISITVIS2:ARMCDTRT implicitly
condition on a subset of the data because of the other variables
involved.
brms_mmrm_formula <- brm_formula(data, correlation = "diagonal")
base_formula <- as.formula(brms_mmrm_formula[[1]])
attr(base_formula, "nl") <- NULL
attr(base_formula, "loop") <- NULLbase_formula
#> FEV1_CHG ~ FEV1_BL + FEV1_BL:AVISIT + ARMCD + ARMCD:AVISIT +
#> AVISIT + RACE + SEX + WEIGHT
#> attr(,"center")
#> [1] TRUE
#> <environment: 0x11da6fe40>colnames(model.matrix(object = base_formula, data = data))
#> [1] "(Intercept)" "FEV1_BL"
#> [3] "ARMCDTRT" "AVISITVIS2"
#> [5] "AVISITVIS3" "AVISITVIS4"
#> [7] "RACEBlack or African American" "RACEWhite"
#> [9] "SEXFemale" "WEIGHT"
#> [11] "FEV1_BL:AVISITVIS2" "FEV1_BL:AVISITVIS3"
#> [13] "FEV1_BL:AVISITVIS4" "AVISITVIS2:ARMCDTRT"
#> [15] "AVISITVIS3:ARMCDTRT" "AVISITVIS4:ARMCDTRT"To accomplish our goals, we need to carefully construct a linear transformation that maps these model coefficients to the marginal means of interest. The transformation should evaluate contrasts on the interesting parameters and average out the uninteresting parameters.
brms.mmrm::brm_model() returns a fitted brms model, and
brms already
has tools for posterior inference. Through a combination of native
functions and S3 methods, brms integrates
not only with posterior and loo, but also emmeans
for the estimation of marginal means and downstream contrasts.
Despite the existing features in brms,
brms.mmrm implements custom code to transform model
coefficients into marginal means. This is because the reference grids in
emmeans can only condition on factors explicitly declared
in the model formula supplied to brms, whereas
brms.mmrm needs more flexibility in order to support informative
prior archetypes (Bedrick et al.
(1996), Bedrick et al. (1997),
Christensen et al. (2010), Rosner et al. (2021)).
brms.mmrm estimates marginal meansTo estimate marginal means,
brms.mmrm::brm_transform_marginal() creates a special
matrix.
dim(transform)
#> [1] 8 16
transform[, 1:4]
#> b_Intercept b_FEV1_BL b_ARMCDTRT b_AVISITVIS2
#> PBO|VIS1 1 40.12532 0 0
#> PBO|VIS2 1 40.12532 0 1
#> PBO|VIS3 1 40.12532 0 0
#> PBO|VIS4 1 40.12532 0 0
#> TRT|VIS1 1 40.12532 1 0
#> TRT|VIS2 1 40.12532 1 1
#> TRT|VIS3 1 40.12532 1 0
#> TRT|VIS4 1 40.12532 1 0This special matrix encodes the equations below which map model coefficients to marginal means.3
summary(transform)
#> # This is a matrix to transform model parameters to marginal means.
#> # The following equations show the relationships between the
#> # marginal means (left-hand side) and fixed effect parameters
#> # (right-hand side).
#> #
#> # PBO:VIS1 = b_Intercept + 40.13*b_FEV1_BL + 0.38*b_RACEBlackorAfricanAmerican + 0.27*b_RACEWhite + 0.53*b_SEXFemale + 0.52*b_WEIGHT
#> # PBO:VIS2 = b_Intercept + 40.13*b_FEV1_BL + b_AVISITVIS2 + 0.38*b_RACEBlackorAfricanAmerican + 0.27*b_RACEWhite + 0.53*b_SEXFemale + 0.52*b_WEIGHT + 40.13*b_FEV1_BL:AVISITVIS2
#> # PBO:VIS3 = b_Intercept + 40.13*b_FEV1_BL + b_AVISITVIS3 + 0.38*b_RACEBlackorAfricanAmerican + 0.27*b_RACEWhite + 0.53*b_SEXFemale + 0.52*b_WEIGHT + 40.13*b_FEV1_BL:AVISITVIS3
#> # PBO:VIS4 = b_Intercept + 40.13*b_FEV1_BL + b_AVISITVIS4 + 0.38*b_RACEBlackorAfricanAmerican + 0.27*b_RACEWhite + 0.53*b_SEXFemale + 0.52*b_WEIGHT + 40.13*b_FEV1_BL:AVISITVIS4
#> # TRT:VIS1 = b_Intercept + 40.13*b_FEV1_BL + b_ARMCDTRT + 0.38*b_RACEBlackorAfricanAmerican + 0.27*b_RACEWhite + 0.53*b_SEXFemale + 0.52*b_WEIGHT
#> # TRT:VIS2 = b_Intercept + 40.13*b_FEV1_BL + b_ARMCDTRT + b_AVISITVIS2 + 0.38*b_RACEBlackorAfricanAmerican + 0.27*b_RACEWhite + 0.53*b_SEXFemale + 0.52*b_WEIGHT + 40.13*b_FEV1_BL:AVISITVIS2 + b_ARMCDTRT:AVISITVIS2
#> # TRT:VIS3 = b_Intercept + 40.13*b_FEV1_BL + b_ARMCDTRT + b_AVISITVIS3 + 0.38*b_RACEBlackorAfricanAmerican + 0.27*b_RACEWhite + 0.53*b_SEXFemale + 0.52*b_WEIGHT + 40.13*b_FEV1_BL:AVISITVIS3 + b_ARMCDTRT:AVISITVIS3
#> # TRT:VIS4 = b_Intercept + 40.13*b_FEV1_BL + b_ARMCDTRT + b_AVISITVIS4 + 0.38*b_RACEBlackorAfricanAmerican + 0.27*b_RACEWhite + 0.53*b_SEXFemale + 0.52*b_WEIGHT + 40.13*b_FEV1_BL:AVISITVIS4 + b_ARMCDTRT:AVISITVIS4Multiplying the matrix by a set of model coefficients is the same as plugging the coefficients into the equations above. Both produce estimates of marginal means.
model <- lm(formula = base_formula, data = data)
marginals_custom <- transform %*% coef(model)
marginals_custom
#> [,1]
#> PBO|VIS1 -4.5998295
#> PBO|VIS2 -2.5445943
#> PBO|VIS3 0.9841880
#> PBO|VIS4 5.6013241
#> TRT|VIS1 -1.2858526
#> TRT|VIS2 0.8466639
#> TRT|VIS3 3.8011416
#> TRT|VIS4 10.0521521This technique is similar to emmeans::emmeans(weights = "proportional")4 (Lenth (2016), Searle et
al. (1980)) and produces similar estimates.
library(emmeans)
#> Warning: package 'emmeans' was built under R version 4.4.1
#> Welcome to emmeans.
#> Caution: You lose important information if you filter this package's results.
#> See '? untidy'
marginals_emmeans <- emmeans(
object = model,
specs = ~ARMCD:AVISIT,
weights = "proportional",
nuisance = c("USUBJID", "RACE", "SEX")
) |>
as.data.frame() |>
as_tibble() |>
select(ARMCD, AVISIT, emmean) |>
arrange(ARMCD, AVISIT)marginals_emmeans
#> # A tibble: 8 × 3
#> ARMCD AVISIT emmean
#> <fct> <fct> <dbl>
#> 1 PBO VIS1 -4.60
#> 2 PBO VIS2 -2.54
#> 3 PBO VIS3 0.984
#> 4 PBO VIS4 5.60
#> 5 TRT VIS1 -1.29
#> 6 TRT VIS2 0.847
#> 7 TRT VIS3 3.80
#> 8 TRT VIS4 10.1marginals_custom - marginals_emmeans$emmean
#> [,1]
#> PBO|VIS1 4.440892e-15
#> PBO|VIS2 3.996803e-15
#> PBO|VIS3 3.552714e-15
#> PBO|VIS4 4.440892e-15
#> TRT|VIS1 3.774758e-15
#> TRT|VIS2 3.552714e-15
#> TRT|VIS3 4.440892e-15
#> TRT|VIS4 3.552714e-15For our Bayesian MMRMs in brms.mmrm, the transformation
from brm_transform_marginal() operates on each individual
draw from the joint posterior distribution. The transformation matrix
produced by brm_transform_marginal() is the value of the
transform argument of brm_marginal_draws().
That way, brm_marginal_draws() produces an entire estimated
posterior of each marginal mean, rather than point estimates that assume
a normal or Student-t distribution.5
brm_marginal_draws() worksLet us take a closer look at the equations that map model parameters to marginal means.
summary(transform)
#> # This is a matrix to transform model parameters to marginal means.
#> # The following equations show the relationships between the
#> # marginal means (left-hand side) and fixed effect parameters
#> # (right-hand side).
#> #
#> # PBO:VIS1 = b_Intercept + 40.13*b_FEV1_BL + 0.38*b_RACEBlackorAfricanAmerican + 0.27*b_RACEWhite + 0.53*b_SEXFemale + 0.52*b_WEIGHT
#> # PBO:VIS2 = b_Intercept + 40.13*b_FEV1_BL + b_AVISITVIS2 + 0.38*b_RACEBlackorAfricanAmerican + 0.27*b_RACEWhite + 0.53*b_SEXFemale + 0.52*b_WEIGHT + 40.13*b_FEV1_BL:AVISITVIS2
#> # PBO:VIS3 = b_Intercept + 40.13*b_FEV1_BL + b_AVISITVIS3 + 0.38*b_RACEBlackorAfricanAmerican + 0.27*b_RACEWhite + 0.53*b_SEXFemale + 0.52*b_WEIGHT + 40.13*b_FEV1_BL:AVISITVIS3
#> # PBO:VIS4 = b_Intercept + 40.13*b_FEV1_BL + b_AVISITVIS4 + 0.38*b_RACEBlackorAfricanAmerican + 0.27*b_RACEWhite + 0.53*b_SEXFemale + 0.52*b_WEIGHT + 40.13*b_FEV1_BL:AVISITVIS4
#> # TRT:VIS1 = b_Intercept + 40.13*b_FEV1_BL + b_ARMCDTRT + 0.38*b_RACEBlackorAfricanAmerican + 0.27*b_RACEWhite + 0.53*b_SEXFemale + 0.52*b_WEIGHT
#> # TRT:VIS2 = b_Intercept + 40.13*b_FEV1_BL + b_ARMCDTRT + b_AVISITVIS2 + 0.38*b_RACEBlackorAfricanAmerican + 0.27*b_RACEWhite + 0.53*b_SEXFemale + 0.52*b_WEIGHT + 40.13*b_FEV1_BL:AVISITVIS2 + b_ARMCDTRT:AVISITVIS2
#> # TRT:VIS3 = b_Intercept + 40.13*b_FEV1_BL + b_ARMCDTRT + b_AVISITVIS3 + 0.38*b_RACEBlackorAfricanAmerican + 0.27*b_RACEWhite + 0.53*b_SEXFemale + 0.52*b_WEIGHT + 40.13*b_FEV1_BL:AVISITVIS3 + b_ARMCDTRT:AVISITVIS3
#> # TRT:VIS4 = b_Intercept + 40.13*b_FEV1_BL + b_ARMCDTRT + b_AVISITVIS4 + 0.38*b_RACEBlackorAfricanAmerican + 0.27*b_RACEWhite + 0.53*b_SEXFemale + 0.52*b_WEIGHT + 40.13*b_FEV1_BL:AVISITVIS4 + b_ARMCDTRT:AVISITVIS4These equations include terms not only for the fixed effects of
interest, but also for nuisance variables FEV1_BL,
SEX, RACE, and WEIGHT. These
nuisance variables were originally part of the model formula, which
means each marginal mean can only be interpreted relative to a fixed
value of FEV1_BL, a fixed proportion of female patients,
etc. For example, if we dropped 40.13*b_FEV1_BL, then
PBO:VIS1 would be the placebo mean at visit 1 for patients
with FEV1_BL = 0: in other words, patients who cannot
breathe out any air from their lungs at the beginning of the study.
Similarly, if we dropped 0.53*b_SEXFemale, then we would
have to interpret PBO:VIS1 as the visit 1 placebo mean for
male patients only. Fixed values 40.13 and
0.53 are averages over the data to ensure our marginal
means apply to the entire patient population as a whole.
The major challenge of brm_transform_marginal() is to
condition on nuisance values that represent appropriate averages over
the data. To calculate these nuisance values,
brm_transform_marginal() uses a technique similar to
weights = "proportional" in
emmeans::emmeans().
To replicate brm_transform_marginal(), we first create a
reference grid to define the factor levels of interest and the means of
continuous variables to condition on.
grid <- data |>
mutate(FEV1_BL = mean(FEV1_BL), WEIGHT = mean(WEIGHT)) |>
distinct(ARMCD, AVISIT, FEV1_BL, WEIGHT) |>
arrange(ARMCD, AVISIT)
grid
#> # A tibble: 8 × 4
#> ARMCD AVISIT FEV1_BL WEIGHT
#> <fct> <fct> <dbl> <dbl>
#> 1 PBO VIS1 40.1 0.519
#> 2 PBO VIS2 40.1 0.519
#> 3 PBO VIS3 40.1 0.519
#> 4 PBO VIS4 40.1 0.519
#> 5 TRT VIS1 40.1 0.519
#> 6 TRT VIS2 40.1 0.519
#> 7 TRT VIS3 40.1 0.519
#> 8 TRT VIS4 40.1 0.519We use the grid to construct a model matrix with the desired interactions between continuous variables and factors of interest. Each column represents a model coefficient, and each row represents a marginal mean of interest.
transform <- model.matrix(
object = ~ FEV1_BL * AVISIT + ARMCD * AVISIT + WEIGHT,
data = grid
)
rownames(transform) <- paste(grid$ARMCD, grid$AVISIT)
transform
#> (Intercept) FEV1_BL AVISITVIS2 AVISITVIS3 AVISITVIS4 ARMCDTRT
#> PBO VIS1 1 40.12532 0 0 0 0
#> PBO VIS2 1 40.12532 1 0 0 0
#> PBO VIS3 1 40.12532 0 1 0 0
#> PBO VIS4 1 40.12532 0 0 1 0
#> TRT VIS1 1 40.12532 0 0 0 1
#> TRT VIS2 1 40.12532 1 0 0 1
#> TRT VIS3 1 40.12532 0 1 0 1
#> TRT VIS4 1 40.12532 0 0 1 1
#> WEIGHT FEV1_BL:AVISITVIS2 FEV1_BL:AVISITVIS3 FEV1_BL:AVISITVIS4
#> PBO VIS1 0.5185461 0.00000 0.00000 0.00000
#> PBO VIS2 0.5185461 40.12532 0.00000 0.00000
#> PBO VIS3 0.5185461 0.00000 40.12532 0.00000
#> PBO VIS4 0.5185461 0.00000 0.00000 40.12532
#> TRT VIS1 0.5185461 0.00000 0.00000 0.00000
#> TRT VIS2 0.5185461 40.12532 0.00000 0.00000
#> TRT VIS3 0.5185461 0.00000 40.12532 0.00000
#> TRT VIS4 0.5185461 0.00000 0.00000 40.12532
#> AVISITVIS2:ARMCDTRT AVISITVIS3:ARMCDTRT AVISITVIS4:ARMCDTRT
#> PBO VIS1 0 0 0
#> PBO VIS2 0 0 0
#> PBO VIS3 0 0 0
#> PBO VIS4 0 0 0
#> TRT VIS1 0 0 0
#> TRT VIS2 1 0 0
#> TRT VIS3 0 1 0
#> TRT VIS4 0 0 1
#> attr(,"assign")
#> [1] 0 1 2 2 2 3 4 5 5 5 6 6 6
#> attr(,"contrasts")
#> attr(,"contrasts")$AVISIT
#> [1] "contr.treatment"
#>
#> attr(,"contrasts")$ARMCD
#> [1] "contr.treatment"We want to predict at the “average” of SEX and
RACE across all the data. Since SEX and
RACE are factors, we cannot simply take the means of the
variables themselves. Rather, we construct a model matrix to turn each
factor level into a dummy variable, and then average those
dummy variables across the entire dataset. This process accounts for the
observed frequencies of these levels in the data (ideal for passive
variables that the experiment does not directly control), while guarding
against hidden confounding with the factors of interest (which can lead
to Simpson’s paradox).6
proportional_factors <- data |>
model.matrix(object = ~ 0 + SEX + RACE) |>
colMeans() |>
t()
proportional_factors
#> SEXMale SEXFemale RACEBlack or African American RACEWhite
#> [1,] 0.4670051 0.5329949 0.3756345 0.2690355transform <- transform |>
bind_cols(proportional_factors) |>
as.matrix()
transform <- transform[, names(coef(model))]
rownames(transform) <- paste(grid$ARMCD, grid$AVISIT)
transform
#> (Intercept) FEV1_BL ARMCDTRT AVISITVIS2 AVISITVIS3 AVISITVIS4
#> PBO VIS1 1 40.12532 0 0 0 0
#> PBO VIS2 1 40.12532 0 1 0 0
#> PBO VIS3 1 40.12532 0 0 1 0
#> PBO VIS4 1 40.12532 0 0 0 1
#> TRT VIS1 1 40.12532 1 0 0 0
#> TRT VIS2 1 40.12532 1 1 0 0
#> TRT VIS3 1 40.12532 1 0 1 0
#> TRT VIS4 1 40.12532 1 0 0 1
#> RACEBlack or African American RACEWhite SEXFemale WEIGHT
#> PBO VIS1 0.3756345 0.2690355 0.5329949 0.5185461
#> PBO VIS2 0.3756345 0.2690355 0.5329949 0.5185461
#> PBO VIS3 0.3756345 0.2690355 0.5329949 0.5185461
#> PBO VIS4 0.3756345 0.2690355 0.5329949 0.5185461
#> TRT VIS1 0.3756345 0.2690355 0.5329949 0.5185461
#> TRT VIS2 0.3756345 0.2690355 0.5329949 0.5185461
#> TRT VIS3 0.3756345 0.2690355 0.5329949 0.5185461
#> TRT VIS4 0.3756345 0.2690355 0.5329949 0.5185461
#> FEV1_BL:AVISITVIS2 FEV1_BL:AVISITVIS3 FEV1_BL:AVISITVIS4
#> PBO VIS1 0.00000 0.00000 0.00000
#> PBO VIS2 40.12532 0.00000 0.00000
#> PBO VIS3 0.00000 40.12532 0.00000
#> PBO VIS4 0.00000 0.00000 40.12532
#> TRT VIS1 0.00000 0.00000 0.00000
#> TRT VIS2 40.12532 0.00000 0.00000
#> TRT VIS3 0.00000 40.12532 0.00000
#> TRT VIS4 0.00000 0.00000 40.12532
#> AVISITVIS2:ARMCDTRT AVISITVIS3:ARMCDTRT AVISITVIS4:ARMCDTRT
#> PBO VIS1 0 0 0
#> PBO VIS2 0 0 0
#> PBO VIS3 0 0 0
#> PBO VIS4 0 0 0
#> TRT VIS1 0 0 0
#> TRT VIS2 1 0 0
#> TRT VIS3 0 1 0
#> TRT VIS4 0 0 1Finally, we use this transformation matrix to map estimated model coefficients to estimated marginal means.
marginals_custom <- transform %*% coef(model)
marginals_custom
#> [,1]
#> PBO VIS1 -4.5998295
#> PBO VIS2 -2.5445943
#> PBO VIS3 0.9841880
#> PBO VIS4 5.6013241
#> TRT VIS1 -1.2858526
#> TRT VIS2 0.8466639
#> TRT VIS3 3.8011416
#> TRT VIS4 10.0521521These results are extremely close to the estimated marginal means
from emmeans.
marginals_emmeans |>
bind_cols(custom = as.numeric(marginals_custom)) |>
mutate(difference = custom - emmean)
#> # A tibble: 8 × 5
#> ARMCD AVISIT emmean custom difference
#> <fct> <fct> <dbl> <dbl> <dbl>
#> 1 PBO VIS1 -4.60 -4.60 1.78e-15
#> 2 PBO VIS2 -2.54 -2.54 1.78e-15
#> 3 PBO VIS3 0.984 0.984 1.78e-15
#> 4 PBO VIS4 5.60 5.60 1.78e-15
#> 5 TRT VIS1 -1.29 -1.29 1.78e-15
#> 6 TRT VIS2 0.847 0.847 1.78e-15
#> 7 TRT VIS3 3.80 3.80 1.78e-15
#> 8 TRT VIS4 10.1 10.1 1.78e-15brms.mmrm follows the procedure above, but in a Bayesian
context. The brm_transform_marginal() creates the matrix
above, and brm_marginal_draws() uses it to transform
posterior draws of brms model coefficients into posterior
draws of marginal means. These posterior draws of marginal means then
support estimation of treatment effects (via
brm_marginal_draws() and
brm_marginal_summaries()) and posterior probabilities on
those treatment effects (via brm_marginal_probabilities()).
To fine-tune the marginal mean estimation procedure for niche use cases,
you can modify the transformation returned from
brm_transform_marginal() and then supply it to the
transform argument of
brm_marginal_draws().
Subgroup analysis raises important questions about how nuisance
variables are averaged, and you as the user are responsible for choosing
the approach that best suits the situation. To illustrate, suppose
SEX is a pre-specified subgroup. When estimating marginal
means, we now wish to condition on "Female" vs
"Male" while averaging over RACE across the
whole dataset. In emmeans, this is similar to how we
calculated marginals_emmeans above, but we now move
SEX from nuisance to specs:
emmeans(
object = model,
specs = ~SEX:ARMCD:AVISIT,
weights = "proportional",
nuisance = c("USUBJID", "RACE")
)
#> SEX ARMCD AVISIT emmean SE df lower.CL upper.CL
#> Male PBO VIS1 -5.014 0.752 772 -6.490 -3.538
#> Female PBO VIS1 -4.237 0.743 772 -5.696 -2.778
#> Male TRT VIS1 -1.700 0.800 772 -3.270 -0.130
#> Female TRT VIS1 -0.923 0.786 772 -2.466 0.620
#> Male PBO VIS2 -2.959 0.752 772 -4.435 -1.482
#> Female PBO VIS2 -2.182 0.743 772 -3.640 -0.723
#> Male TRT VIS2 0.433 0.800 772 -1.137 2.003
#> Female TRT VIS2 1.209 0.786 772 -0.333 2.752
#> Male PBO VIS3 0.570 0.752 772 -0.906 2.046
#> Female PBO VIS3 1.347 0.743 772 -0.112 2.806
#> Male TRT VIS3 3.387 0.800 772 1.816 4.958
#> Female TRT VIS3 4.164 0.786 772 2.621 5.707
#> Male PBO VIS4 5.187 0.752 772 3.712 6.663
#> Female PBO VIS4 5.964 0.743 772 4.506 7.422
#> Male TRT VIS4 9.638 0.800 772 8.067 11.209
#> Female TRT VIS4 10.415 0.786 772 8.872 11.958
#>
#> Results are averaged over the levels of: 1 nuisance factors
#> Confidence level used: 0.95This may be reasonable in some cases, and it mitigates the kind of
hidden confounding between the subgroup and other variables which may
otherwise cause Simpson’s paradox. However, for subgroup-specific
marginal means, it may not be realistic to condition on a single point
estimate for all levels of the reference grid. For example, if the model
were to regress on a pregnancy variable, then the marginal
means for SEX = "Male" should always condition on
pregnancy = 0 instead of mean(data$pregnancy).
And in general, it may be more reasonable to condition on
subgroup-specific averages of nuisance variables. However, if you do
this, it is your responsibility to investigate and understand the hidden
interactions and confounding in your dataset. https://cran.r-project.org/package=emmeans/vignettes/interactions.html
is an edifying vignette on this topic.
To opt into subgroup-specific averages of nuisance variables in
brms.mmrm, set average_within_subgroup = TRUE
in brm_transform_marginal(), then supply the output to the
transform argument of
brm_marginal_draws().
To replicate
brm_transform_marginal(average_within_subgroup = TRUE) from
scratch, first create a reference grid which includes subgroup
levels.
grid <- data |>
distinct(ARMCD, SEX, AVISIT) |>
arrange(ARMCD, SEX, AVISIT)
grid
#> # A tibble: 16 × 3
#> ARMCD SEX AVISIT
#> <fct> <fct> <fct>
#> 1 PBO Male VIS1
#> 2 PBO Male VIS2
#> 3 PBO Male VIS3
#> 4 PBO Male VIS4
#> 5 PBO Female VIS1
#> 6 PBO Female VIS2
#> 7 PBO Female VIS3
#> 8 PBO Female VIS4
#> 9 TRT Male VIS1
#> 10 TRT Male VIS2
#> 11 TRT Male VIS3
#> 12 TRT Male VIS4
#> 13 TRT Female VIS1
#> 14 TRT Female VIS2
#> 15 TRT Female VIS3
#> 16 TRT Female VIS4For each continuous variable, append the corresponding subgroup-specific averages to the grid.
means <- data |>
group_by(SEX) |>
summarize(FEV1_BL = mean(FEV1_BL), WEIGHT = mean(WEIGHT), .groups = "drop")
grid <- left_join(x = grid, y = means, by = "SEX")
grid
#> # A tibble: 16 × 5
#> ARMCD SEX AVISIT FEV1_BL WEIGHT
#> <fct> <fct> <fct> <dbl> <dbl>
#> 1 PBO Male VIS1 40.3 0.516
#> 2 PBO Male VIS2 40.3 0.516
#> 3 PBO Male VIS3 40.3 0.516
#> 4 PBO Male VIS4 40.3 0.516
#> 5 PBO Female VIS1 39.9 0.521
#> 6 PBO Female VIS2 39.9 0.521
#> 7 PBO Female VIS3 39.9 0.521
#> 8 PBO Female VIS4 39.9 0.521
#> 9 TRT Male VIS1 40.3 0.516
#> 10 TRT Male VIS2 40.3 0.516
#> 11 TRT Male VIS3 40.3 0.516
#> 12 TRT Male VIS4 40.3 0.516
#> 13 TRT Female VIS1 39.9 0.521
#> 14 TRT Female VIS2 39.9 0.521
#> 15 TRT Female VIS3 39.9 0.521
#> 16 TRT Female VIS4 39.9 0.521Begin creating the variable transformation matrix using this new grid. Be sure to include the subgroup in the formula below exactly as it appears in the formula used to fit the model.
transform <- model.matrix(
object = ~ FEV1_BL * AVISIT + ARMCD * AVISIT + SEX + WEIGHT,
data = grid
)Append subgroup-specific averages of the levels of nuisance factors
(in this case, just RACE).
proportions <- data |>
model.matrix(object = ~ 0 + RACE) |>
as.data.frame() |>
mutate(SEX = data$SEX) |>
group_by(SEX) |>
summarize(across(everything(), mean), .groups = "drop")
transform <- transform |>
as.data.frame() |>
mutate(SEX = grid$SEX) |>
left_join(y = proportions, by = "SEX") |>
select(-SEX) |>
as.matrix()Complete the transformation matrix by assigning the correct row names and aligning the column order with that of the model coefficients.
rownames(transform) <- paste(grid$ARMCD, grid$SEX, grid$AVISIT)
transform <- transform[, names(coef(model))]
transform
#> (Intercept) FEV1_BL ARMCDTRT AVISITVIS2 AVISITVIS3 AVISITVIS4
#> PBO Male VIS1 1 40.34215 0 0 0 0
#> PBO Male VIS2 1 40.34215 0 1 0 0
#> PBO Male VIS3 1 40.34215 0 0 1 0
#> PBO Male VIS4 1 40.34215 0 0 0 1
#> PBO Female VIS1 1 39.93534 0 0 0 0
#> PBO Female VIS2 1 39.93534 0 1 0 0
#> PBO Female VIS3 1 39.93534 0 0 1 0
#> PBO Female VIS4 1 39.93534 0 0 0 1
#> TRT Male VIS1 1 40.34215 1 0 0 0
#> TRT Male VIS2 1 40.34215 1 1 0 0
#> TRT Male VIS3 1 40.34215 1 0 1 0
#> TRT Male VIS4 1 40.34215 1 0 0 1
#> TRT Female VIS1 1 39.93534 1 0 0 0
#> TRT Female VIS2 1 39.93534 1 1 0 0
#> TRT Female VIS3 1 39.93534 1 0 1 0
#> TRT Female VIS4 1 39.93534 1 0 0 1
#> RACEBlack or African American RACEWhite SEXFemale WEIGHT
#> PBO Male VIS1 0.4239130 0.2826087 0 0.5161276
#> PBO Male VIS2 0.4239130 0.2826087 0 0.5161276
#> PBO Male VIS3 0.4239130 0.2826087 0 0.5161276
#> PBO Male VIS4 0.4239130 0.2826087 0 0.5161276
#> PBO Female VIS1 0.3333333 0.2571429 1 0.5206653
#> PBO Female VIS2 0.3333333 0.2571429 1 0.5206653
#> PBO Female VIS3 0.3333333 0.2571429 1 0.5206653
#> PBO Female VIS4 0.3333333 0.2571429 1 0.5206653
#> TRT Male VIS1 0.4239130 0.2826087 0 0.5161276
#> TRT Male VIS2 0.4239130 0.2826087 0 0.5161276
#> TRT Male VIS3 0.4239130 0.2826087 0 0.5161276
#> TRT Male VIS4 0.4239130 0.2826087 0 0.5161276
#> TRT Female VIS1 0.3333333 0.2571429 1 0.5206653
#> TRT Female VIS2 0.3333333 0.2571429 1 0.5206653
#> TRT Female VIS3 0.3333333 0.2571429 1 0.5206653
#> TRT Female VIS4 0.3333333 0.2571429 1 0.5206653
#> FEV1_BL:AVISITVIS2 FEV1_BL:AVISITVIS3 FEV1_BL:AVISITVIS4
#> PBO Male VIS1 0.00000 0.00000 0.00000
#> PBO Male VIS2 40.34215 0.00000 0.00000
#> PBO Male VIS3 0.00000 40.34215 0.00000
#> PBO Male VIS4 0.00000 0.00000 40.34215
#> PBO Female VIS1 0.00000 0.00000 0.00000
#> PBO Female VIS2 39.93534 0.00000 0.00000
#> PBO Female VIS3 0.00000 39.93534 0.00000
#> PBO Female VIS4 0.00000 0.00000 39.93534
#> TRT Male VIS1 0.00000 0.00000 0.00000
#> TRT Male VIS2 40.34215 0.00000 0.00000
#> TRT Male VIS3 0.00000 40.34215 0.00000
#> TRT Male VIS4 0.00000 0.00000 40.34215
#> TRT Female VIS1 0.00000 0.00000 0.00000
#> TRT Female VIS2 39.93534 0.00000 0.00000
#> TRT Female VIS3 0.00000 39.93534 0.00000
#> TRT Female VIS4 0.00000 0.00000 39.93534
#> AVISITVIS2:ARMCDTRT AVISITVIS3:ARMCDTRT AVISITVIS4:ARMCDTRT
#> PBO Male VIS1 0 0 0
#> PBO Male VIS2 0 0 0
#> PBO Male VIS3 0 0 0
#> PBO Male VIS4 0 0 0
#> PBO Female VIS1 0 0 0
#> PBO Female VIS2 0 0 0
#> PBO Female VIS3 0 0 0
#> PBO Female VIS4 0 0 0
#> TRT Male VIS1 0 0 0
#> TRT Male VIS2 1 0 0
#> TRT Male VIS3 0 1 0
#> TRT Male VIS4 0 0 1
#> TRT Female VIS1 0 0 0
#> TRT Female VIS2 1 0 0
#> TRT Female VIS3 0 1 0
#> TRT Female VIS4 0 0 1Finally, use the custom transform matrix to estimate
subgroup-specific marginal means. Because we averaged
FEV1_BL, WEIGHT, and RACE within
subgroup levels, the results will differ from those of
emmeans.
transform %*% coef(model)
#> [,1]
#> PBO Male VIS1 -5.0812041
#> PBO Male VIS2 -3.0267423
#> PBO Male VIS3 0.5040980
#> PBO Male VIS4 5.1144252
#> PBO Female VIS1 -4.1780538
#> PBO Female VIS2 -2.1221408
#> PBO Female VIS3 1.4048382
#> PBO Female VIS4 6.0279403
#> TRT Male VIS1 -1.7672272
#> TRT Male VIS2 0.3645159
#> TRT Male VIS3 3.3210517
#> TRT Male VIS4 9.5652532
#> TRT Female VIS1 -0.8640769
#> TRT Female VIS2 1.2691174
#> TRT Female VIS3 4.2217919
#> TRT Female VIS4 10.4787682For a subgroup analysis where the subgroup factor is identified in advance, we would be interested in each combination of treatment group, subgroup level, and time point.↩︎
Other quantities of interest are downstream of these
marginal means. For example, the difference between TRT and
PBO at time point VIS4, and the difference
between VIS4 and VIS4 for the TRT
group, are simple contrasts of the upstream marginal means.↩︎
summary() also invisibly returns a simple
character vector with the equations below.↩︎
Unlike emmeans, brms.mmrm
completes the grid of patient visits to add rows for implicitly missing
responses. Completing the grid of patient visits ensures all patients
are represented equally when averaging over baseline covariates,
regardless of patients who drop out early.↩︎
You can then estimate the posterior of any function of
marginal means by simply applying that function to the individual
posterior draws from brm_marginal_draws().
brm_marginal_grid() helps identify column names for this
kind of custom inference/post-processing.↩︎
For more context, please refer to the emmeans
basics vignette, as well as discussion forums here
and here.↩︎
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.