Report the estimates and standard errors of marginal means
at each study arm and visit. The assumed visit times should have been
given in the marginals argument of the model-fitting function.
Use the type argument to choose
marginal means of the outcomes, marginal estimates of change from
baseline, and marginal estimates of treatment effects.
Usage
pmrm_marginals(fit, type = c("outcome", "change", "effect"), confidence = 0.95)Arguments
- fit
A
pmrmfitted model object returned by a model-fitting function.- type
Character string.
"outcome"reports marginal means on the outcome scale,"change"reports estimates of change from baseline, and"effect"reports estimates of treatment effects (change from baseline of each active arm minus that of the control arm.)- confidence
A numeric from 0 to 1 with the confidence level for confidence intervals.
Value
A tibble with one row per marginal mean and columns with the
estimate, standard error, 2-sided confidence bounds, and indicator
columns.
Some estimates, standard errors, and confidence bounds may be NA_real_
if they correspond to the reference level subtracted out in
change-from-baseline or treatment effect calculations.
See also
Other estimates:
VarCorr.pmrm_fit(),
coef.pmrm_fit(),
tidy.pmrm_fit(),
vcov.pmrm_fit()
Examples
set.seed(0L)
simulation <- pmrm_simulate_decline_proportional(
visit_times = seq_len(5L) - 1,
gamma = c(1, 2)
)
fit <- pmrm_model_decline_proportional(
data = simulation,
outcome = "y",
time = "t",
patient = "patient",
visit = "visit",
arm = "arm",
covariates = ~ w_1 + w_2
)
pmrm_marginals(fit)
#> # A tibble: 15 × 7
#> arm visit time estimate standard_error lower upper
#> <ord> <ord> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 arm_1 visit_1 0 0.000599 0.0580 -0.113 0.114
#> 2 arm_1 visit_2 1 0.700 0.0683 0.566 0.834
#> 3 arm_1 visit_3 2 1.04 0.0732 0.893 1.18
#> 4 arm_1 visit_4 3 1.39 0.0769 1.24 1.54
#> 5 arm_1 visit_5 4 1.64 0.0834 1.48 1.81
#> 6 arm_2 visit_1 0 0.000599 0.0580 -0.113 0.114
#> 7 arm_2 visit_2 1 0.591 0.0586 0.477 0.706
#> 8 arm_2 visit_3 2 0.876 0.0679 0.743 1.01
#> 9 arm_2 visit_4 3 1.17 0.0716 1.03 1.31
#> 10 arm_2 visit_5 4 1.39 0.0786 1.23 1.54
#> 11 arm_3 visit_1 0 0.000599 0.0580 -0.113 0.114
#> 12 arm_3 visit_2 1 0.544 0.0549 0.437 0.652
#> 13 arm_3 visit_3 2 0.807 0.0639 0.681 0.932
#> 14 arm_3 visit_4 3 1.08 0.0695 0.942 1.21
#> 15 arm_3 visit_5 4 1.28 0.0775 1.13 1.43