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Model formula

Source: R/brm_formula.R
brm_formula.Rd

Build a model formula for an MMRM, either for a generic brm_data() dataset or an informative prior archetype.

Usage

brm_formula(
  data,
  model_missing_outcomes = FALSE,
  check_rank = TRUE,
  sigma = brms.mmrm::brm_formula_sigma(data = data, check_rank = check_rank),
  correlation = "unstructured",
  autoregressive_order = 1L,
  moving_average_order = 1L,
  residual_covariance_arma_estimation = FALSE,
  ...
)

# Default S3 method
brm_formula(
  data,
  model_missing_outcomes = FALSE,
  check_rank = TRUE,
  sigma = brms.mmrm::brm_formula_sigma(data = data, check_rank = check_rank),
  correlation = "unstructured",
  autoregressive_order = 1L,
  moving_average_order = 1L,
  residual_covariance_arma_estimation = FALSE,
  intercept = TRUE,
  baseline = !is.null(attr(data, "brm_baseline")),
  baseline_subgroup = !is.null(attr(data, "brm_baseline")) && !is.null(attr(data,
    "brm_subgroup")),
  baseline_subgroup_time = !is.null(attr(data, "brm_baseline")) && !is.null(attr(data,
    "brm_subgroup")),
  baseline_time = !is.null(attr(data, "brm_baseline")),
  covariates = TRUE,
  group = TRUE,
  group_subgroup = !is.null(attr(data, "brm_subgroup")),
  group_subgroup_time = !is.null(attr(data, "brm_subgroup")),
  group_time = TRUE,
  subgroup = !is.null(attr(data, "brm_subgroup")),
  subgroup_time = !is.null(attr(data, "brm_subgroup")),
  time = TRUE,
  center = TRUE,
  ...,
  effect_baseline = NULL,
  effect_group = NULL,
  effect_time = NULL,
  interaction_baseline = NULL,
  interaction_group = NULL
)

# S3 method for class 'brms_mmrm_archetype'
brm_formula(
  data,
  model_missing_outcomes = FALSE,
  check_rank = TRUE,
  sigma = brms.mmrm::brm_formula_sigma(data = data, check_rank = check_rank),
  correlation = "unstructured",
  autoregressive_order = 1L,
  moving_average_order = 1L,
  residual_covariance_arma_estimation = FALSE,
  ...,
  warn_ignored = TRUE
)

Arguments

data

A classed data frame from brm_data(), or an informative prior archetype from a function like brm_archetype_successive_cells().

model_missing_outcomes

Logical of length 1, TRUE to impute missing outcomes during model fitting as described in the "Imputation during model fitting" section of https://paulbuerkner.com/brms/articles/brms_missings.html. Specifically, if the outcome variable is y, then the formula will begin with y | mi() ~ ... instead of simply y ~ .... Set to FALSE (default) to forgo this kind of imputation and discard missing observations from the data just prior to fitting the model inside brm_model(). See https://opensource.nibr.com/bamdd/src/02h_mmrm.html#what-estimand-does-mmrm-address #nolint to understand the standard assumptions and decisions regarding MMRMs and missing outcomes.

check_rank

TRUE to check the rank of the model matrix and throw an error if rank deficiency is detected. FALSE to skip this check. Rank-deficient models may have non-identifiable parameters and it is recommended to choose a full-rank mapping.

sigma

A formula produced by brm_formula_sigma(). The formula is a base R formula with S3 class "brms_mmrm_formula_sigma", and it controls the parameterization of the residual standard deviations sigma.

correlation

Character of length 1, name of the correlation structure. The correlation matrix is a square T x T matrix, where T is the number of discrete time points in the data. This matrix describes the correlations between time points in the same patient, as modeled in the residuals. Different patients are modeled as independent. The correlation argument controls how this matrix is parameterized, and the choices given by brms are listed at https://paulbuerkner.com/brms/reference/autocor-terms.html, and the choice is ultimately encoded in the main body of the output formula through terms like unstru() and arma(), some of which are configurable through arguments autoregressive_order, moving_average_order, and residual_covariance_arma_estimation of brm_formula(). Choices in brms.mmrm:

autoregressive_order

Nonnegative integer, autoregressive order for the "autoregressive_moving_average" and "autoregressive" correlation structures.

moving_average_order

Nonnegative integer, moving average order for the "autoregressive_moving_average" and "moving_average" correlation structures.

residual_covariance_arma_estimation

TRUE or FALSE, whether to estimate ARMA effects using residual covariance matrices. Directly supplied to the cov argument in brms for "autoregressive_moving_average", "autoregressive", and "moving_average" correlation structures. C.f. https://paulbuerkner.com/brms/reference/arma.html.

...

Named arguments to specific brm_formula() methods.

intercept

Logical of length 1. TRUE (default) to include an intercept, FALSE to omit.

baseline

Logical of length 1. TRUE to include an additive effect for baseline response, FALSE to omit. Default is TRUE if brm_data() previously declared a baseline variable in the dataset. Ignored for informative prior archetypes. For informative prior archetypes, this option should be set in functions like brm_archetype_successive_cells() rather than in brm_formula() in order to make sure columns are appropriately centered and the underlying model matrix has full rank.

baseline_subgroup

Logical of length 1.

baseline_subgroup_time

Logical of length 1. TRUE to include baseline-by-subgroup-by-time interaction, FALSE to omit. Default is TRUE if brm_data() previously declared baseline and subgroup variables in the dataset. Ignored for informative prior archetypes. For informative prior archetypes, this option should be set in functions like brm_archetype_successive_cells() rather than in brm_formula() in order to make sure columns are appropriately centered and the underlying model matrix has full rank.

baseline_time

Logical of length 1. TRUE to include baseline-by-time interaction, FALSE to omit. Default is TRUE if brm_data() previously declared a baseline variable in the dataset. Ignored for informative prior archetypes. For informative prior archetypes, this option should be set in functions like brm_archetype_successive_cells() rather than in brm_formula() in order to make sure columns are appropriately centered and the underlying model matrix has full rank.

covariates

Logical of length 1. TRUE (default) to include any additive covariates declared with the covariates argument of brm_data(), FALSE to omit. For informative prior archetypes, this option is set in functions like brm_archetype_successive_cells() rather than in brm_formula() in order to make sure columns are appropriately centered and the underlying model matrix has full rank.

group

Logical of length 1. TRUE (default) to include additive effects for treatment groups, FALSE to omit.

group_subgroup

Logical of length 1. TRUE to include group-by-subgroup interaction, FALSE to omit. Default is TRUE if brm_data() previously declared a subgroup variable in the dataset.

group_subgroup_time

Logical of length 1. TRUE to include group-by-subgroup-by-time interaction, FALSE to omit. Default is TRUE if brm_data() previously declared a subgroup variable in the dataset.

group_time

Logical of length 1. TRUE (default) to include group-by-time interaction, FALSE to omit.

subgroup

Logical of length 1. TRUE to include additive fixed effects for subgroup levels, FALSE to omit. Default is TRUE if brm_data() previously declared a subgroup variable in the dataset.

subgroup_time

Logical of length 1. TRUE to include subgroup-by-time interaction, FALSE to omit. Default is TRUE if brm_data() previously declared a subgroup variable in the dataset.

time

Logical of length 1. TRUE (default) to include a additive effect for discrete time, FALSE to omit.

center

TRUE to center the columns of the model matrix before fitting the model if the model formula includes an intercept term controlled by brms. FALSE to skip centering. Centering usually leads to more computationally efficient sampling in the presence of an intercept, but it changes the interpretation of the intercept parameter if included in the model (as explained in the help file of brms::brmsformula()). Informative prior archetypes always use center = FALSE and use an intercept not controlled by brms.mmrm to ensure the intercept parameter is interpretable and compatible with user-defined priors.

effect_baseline

Deprecated on 2024-01-16 (version 0.0.2.9002). Use baseline instead.

effect_group

Deprecated on 2024-01-16 (version 0.0.2.9002). Use group instead.

effect_time

Deprecated on 2024-01-16 (version 0.0.2.9002). Use time instead.

interaction_baseline

Deprecated on 2024-01-16 (version 0.0.2.9002). Use baseline_time instead.

interaction_group

Deprecated on 2024-01-16 (version 0.0.2.9002). Use group_time instead.

warn_ignored

Set to TRUE to throw a warning if ignored arguments are specified, FALSE otherwise.

Value

An object of class "brmsformula" returned from brms::brmsformula(). It contains the fixed effect mapping, correlation structure, and residual variance structure.

brm_data() formulas

For a brm_data() dataset, brm_formula() builds an R formula for an MMRM based on the details in the data and your choice of mapping. Customize your mapping by toggling on or off the various TRUE/FALSE arguments of brm_formula(), such as intercept, baseline, and group_time. All plausible additive effects, two-way interactions, and three-way interactions can be specified. The following interactions are not supported:

  • Any interactions with the concomitant covariates you specified in the covariates argument of brm_data().

  • Any interactions which include baseline response and treatment group together. Rationale: in a randomized controlled experiment, baseline and treatment group assignment should be uncorrelated.

Formulas for informative prior archetypes

Functions like brm_archetype_successive_cells() tailor datasets to informative prior archetypes. For these specialized tailored datasets, brm_formula() works differently. It still applies the variance and correlation structure of your choosing, and it still lets you choose whether to adjust for nuisance covariates, but it no longer lets you toggle on/off individual terms in the model, such as intercept, baseline, or group. Instead, to ensure the correct interpretation of the parameters, brm_formula() uses the x_* and nuisance_* columns generated by brm_archetype_successive_cells( prefix_interest = "x_", prefix_nuisance = "nuisance_").

Parameterization

For a formula on a brm_data() dataset, the formula is not the only factor that determines the fixed effect mapping. The ordering of the categorical variables in the data, as well as the contrast option in R, affect the construction of the model matrix. To see the model matrix that will ultimately be used in brm_model(), run brms::make_standata() and examine the X element of the returned list. See the examples below for a demonstration.

See also

Other models: brm_formula_sigma(), brm_model()

Examples

set.seed(0)
data <- brm_data(
  data = brm_simulate_simple()$data,
  outcome = "response",
  group = "group",
  time = "time",
  patient = "patient",
  reference_group = "group_1",
  reference_time = "time_1"
)
brm_formula(data)
#> response ~ group + group:time + time + unstr(time = time, gr = patient) 
#> sigma ~ 0 + time
brm_formula(data = data, intercept = FALSE, baseline = FALSE)
#> response ~ 0 + group + group:time + time + unstr(time = time, gr = patient) 
#> sigma ~ 0 + time
formula <- brm_formula(
  data = data,
  intercept = FALSE,
  baseline = FALSE,
  group = FALSE
)
formula
#> response ~ 0 + group:time + time + unstr(time = time, gr = patient) 
#> sigma ~ 0 + time
# Standard deviations of residuals are distributional parameters that can
# regress on variables in the data.
homogeneous <- brm_formula_sigma(data, time = FALSE)
by_group <- brm_formula_sigma(data, group = TRUE, intercept = TRUE)
homogeneous
#> sigma ~ 0
#> attr(,"brm_allow_effect_size")
#> [1] TRUE
#> <environment: 0x5617387de100>
by_group
#> sigma ~ group + time
#> attr(,"brm_allow_effect_size")
#> [1] TRUE
#> <environment: 0x561738573cf0>
brm_formula(data, sigma = homogeneous)
#> response ~ group + group:time + time + unstr(time = time, gr = patient) 
#> sigma ~ 0
brm_formula(data, sigma = by_group)
#> response ~ group + group:time + time + unstr(time = time, gr = patient) 
#> sigma ~ group + time
# Optional: set the contrast option, which determines the model matrix.
options(contrasts = c(unordered = "contr.SAS", ordered = "contr.poly"))
# See the fixed effect mapping you get from the data:
head(brms::make_standata(formula = formula, data = data)$X)
#>   timetime_1 timetime_2 timetime_3 timetime_4 timetime_1:groupgroup_1
#> 1          1          0          0          0                       1
#> 2          0          1          0          0                       0
#> 3          0          0          1          0                       0
#> 4          0          0          0          1                       0
#> 5          1          0          0          0                       1
#> 6          0          1          0          0                       0
#>   timetime_2:groupgroup_1 timetime_3:groupgroup_1 timetime_4:groupgroup_1
#> 1                       0                       0                       0
#> 2                       1                       0                       0
#> 3                       0                       1                       0
#> 4                       0                       0                       1
#> 5                       0                       0                       0
#> 6                       1                       0                       0
# Specify a different contrast method to use an alternative
# mapping when fitting the model with brm_model():
options(
  contrasts = c(unordered = "contr.treatment", ordered = "contr.poly")
)
# different model matrix than before:
head(brms::make_standata(formula = formula, data = data)$X)
#>   timetime_1 timetime_2 timetime_3 timetime_4 timetime_1:groupgroup_2
#> 1          1          0          0          0                       0
#> 2          0          1          0          0                       0
#> 3          0          0          1          0                       0
#> 4          0          0          0          1                       0
#> 5          1          0          0          0                       0
#> 6          0          1          0          0                       0
#>   timetime_2:groupgroup_2 timetime_3:groupgroup_2 timetime_4:groupgroup_2
#> 1                       0                       0                       0
#> 2                       0                       0                       0
#> 3                       0                       0                       0
#> 4                       0                       0                       0
#> 5                       0                       0                       0
#> 6                       0                       0                       0
# Formula on an informative prior archetype:
data <- brm_simulate_outline(
  n_group = 2,
  n_patient = 100,
  n_time = 4,
  rate_dropout = 0,
  rate_lapse = 0
) |>
  dplyr::mutate(response = rnorm(n = dplyr::n())) |>
  brm_data_change() |>
  brm_simulate_continuous(names = c("biomarker1", "biomarker2")) |>
  brm_simulate_categorical(
    names = "biomarker3",
    levels = c("present", "absent")
  )
archetype <- brm_archetype_successive_cells(data)
formula <- brm_formula(data = archetype)
formula
#> change ~ 0 + x_group_1_time_2 + x_group_1_time_3 + x_group_1_time_4 + x_group_2_time_2 + x_group_2_time_3 + x_group_2_time_4 + nuisance_biomarker1 + nuisance_biomarker2 + nuisance_biomarker3_absent + nuisance_baseline + nuisance_baseline.timetime_2 + nuisance_baseline.timetime_3 + unstr(time = time, gr = patient) 
#> sigma ~ 0 + time