This function serves as a wrapper to fit a Bayesian model using the `brms` package. It uses a formula and data prepared by `prepare_formula_model` and simplifies the process of assigning complex priors to the different non-linear components of the model.
Arguments
- prepared_model
A list object returned from `prepare_formula_model()`. It must contain the elements `formula`, `data`, and `response_type`.
- predictive_effect_priors
A named list with elements `shrunk` and/or `unshrunk` containing the priors for predictive effects.
- prognostic_effect_priors
A named list with elements `shrunk`, `unshrunk`, and/or `intercept` containing the priors for prognostic effects.
- stanvars
An object created by `brms::stanvar()` to add custom Stan code.
- ...
Additional arguments passed directly to `brms::brm()`.
Prior Specification
Priors for prognostic and predictive effects should be provided as named lists. This makes the code explicit and prevents errors. For example: `prognostic_effect_priors = list(shrunk = "horseshoe(1)", unshrunk = "normal(0, 5)", intercept = "normal(0, 10)")`. The function accepts prior definitions as character strings (e.g., `"normal(0, 1)"`) or as full `brmsprior` objects, which is useful for defining complex hierarchical or parameter-specific priors.
Examples
if (require("brms") && require("survival")) {
# 1. Create Sample Data
set.seed(123)
n <- 100
sim_data <- data.frame(
time = round(runif(n, 1, 100)),
status = sample(0:1, n, replace = TRUE),
trt = sample(0:1, n, replace = TRUE),
age = rnorm(n, 50, 10),
region = sample(c("A", "B"), n, replace = TRUE),
subgroup = sample(c("S1", "S2", "S3"), n, replace = TRUE)
)
sim_data$trt <- factor(sim_data$trt, levels = c(0, 1))
sim_data$region <- as.factor(sim_data$region)
sim_data$subgroup <- as.factor(sim_data$subgroup)
# 2. Prepare the formula and data
prepared_model <- prepare_formula_model(
data = sim_data,
response_formula_str = "Surv(time, status) ~ trt",
shrunk_predictive_formula_str = "~ trt:subgroup",
unshrunk_prognostic_formula_str = "~ age",
shrunk_prognostic_formula_str = "~ region",
response_type = "survival",
stratification_formula_str = "~ region"
)
# 3. Fit the model
if (FALSE) { # \dontrun{
fit <- fit_brms_model(
prepared_model = prepared_model,
# Note: intercept prior is not needed for survival models
prognostic_effect_priors = list(shrunk = "horseshoe(1)", unshrunk = "normal(0, 2)"),
predictive_effect_priors = list(shrunk = "horseshoe(1)"),
chains = 1, iter = 50, warmup = 10, refresh = 0 # For a quick example
)
print(fit)
} # }
}
#> Loading required package: brms
#> Loading required package: Rcpp
#> Loading 'brms' package (version 2.23.0). Useful instructions
#> can be found by typing help('brms'). A more detailed introduction
#> to the package is available through vignette('brms_overview').
#>
#> Attaching package: ‘brms’
#> The following object is masked from ‘package:stats’:
#>
#> ar
#> Loading required package: survival
#>
#> Attaching package: ‘survival’
#> The following object is masked from ‘package:brms’:
#>
#> kidney
#> Response type is 'survival'. Modeling the baseline hazard explicitly using bhaz().
#> Applying stratification: estimating separate baseline hazards by 'region'.
#> Treatment 'trt' added to unshrunk prognostic terms by default.
#> Auto-adding missing prognostic effect for interaction: subgroup