Claret-Bruno longitudinal model with subject-level covariates

Separate covariate predictors model the log-normal distribution parameters for baseline tumour size, growth, resistance, and growth inhibition.

Formula intercept columns are removed because every predictor has a separate intercept. Predictor parametrizations have the same meaning as in LongitudinalSteinFojoCov().

Usage

LongitudinalClaretBrunoCov(
  mu_b_formula = ~study,
  omega_b_formula = ~study,
  mu_g_formula = ~arm,
  omega_g_formula = ~arm,
  mu_c_formula = ~arm,
  omega_c_formula = ~arm,
  mu_p_formula = ~arm,
  omega_p_formula = ~arm,
  mu_b_parametrization = "linear",
  omega_b_parametrization = "log-linear",
  mu_g_parametrization = "linear",
  omega_g_parametrization = "log-linear",
  mu_c_parametrization = "linear",
  omega_c_parametrization = "log-linear",
  mu_p_parametrization = "linear",
  omega_p_parametrization = "log-linear",
  mu_b_intercept_prior = prior_normal(log(60), 1),
  mu_b_coefficients_prior = prior_normal(0, 1),
  omega_b_intercept_prior = prior_normal(log(0.2), 1),
  omega_b_coefficients_prior = prior_normal(0, 1),
  mu_g_intercept_prior = prior_normal(log(1), 1),
  mu_g_coefficients_prior = prior_normal(0, 1),
  omega_g_intercept_prior = prior_normal(log(0.2), 1),
  omega_g_coefficients_prior = prior_normal(0, 1),
  mu_c_intercept_prior = prior_normal(log(0.4), 1),
  mu_c_coefficients_prior = prior_normal(0, 1),
  omega_c_intercept_prior = prior_normal(log(0.2), 1),
  omega_c_coefficients_prior = prior_normal(0, 1),
  mu_p_intercept_prior = prior_normal(log(2), 1),
  mu_p_coefficients_prior = prior_normal(0, 1),
  omega_p_intercept_prior = prior_normal(log(0.2), 1),
  omega_p_coefficients_prior = prior_normal(0, 1),
  sigma = prior_lognormal(log(0.1), 1),
  scaled_variance = FALSE,
  centred_baseline = TRUE,
  centred_growth = FALSE,
  centred_resistance = FALSE,
  centred_inhibition = FALSE
)

Arguments

mu_b_formula: (formula) One-sided formula for subject-level covariate effects on the baseline tumour size mean.
omega_b_formula: (formula) One-sided formula for subject-level covariate effects on the baseline tumour size standard deviation.
mu_g_formula: (formula) One-sided formula for subject-level covariate effects on the growth mean.
omega_g_formula: (formula) One-sided formula for subject-level covariate effects on the growth standard deviation.
mu_c_formula: (formula) One-sided formula for subject-level covariate effects on the resistance mean.
omega_c_formula: (formula) One-sided formula for subject-level covariate effects on the resistance standard deviation.
mu_p_formula: (formula) One-sided formula for subject-level covariate effects on the growth inhibition mean.
omega_p_formula: (formula) One-sided formula for subject-level covariate effects on the growth inhibition standard deviation.
mu_b_parametrization: (character) Parametrization of the baseline tumour size mean predictor.
omega_b_parametrization: (character) Parametrization of the baseline tumour size standard deviation predictor. Only "exponential" and "log-linear" are supported.
mu_g_parametrization: (character) Parametrization of the growth mean predictor.
omega_g_parametrization: (character) Parametrization of the growth standard deviation predictor. Only "exponential" and "log-linear" are supported.
mu_c_parametrization: (character) Parametrization of the resistance mean predictor.
omega_c_parametrization: (character) Parametrization of the resistance standard deviation predictor. Only "exponential" and "log-linear" are supported.
mu_p_parametrization: (character) Parametrization of the growth inhibition mean predictor.
omega_p_parametrization: (character) Parametrization of the growth inhibition standard deviation predictor. Only "exponential" and "log-linear" are supported.
mu_b_intercept_prior: (Prior) Prior for the intercept of the baseline tumour size mean predictor.
mu_b_coefficients_prior: (Prior) Prior for the covariate coefficients of the baseline tumour size mean predictor.
omega_b_intercept_prior: (Prior) Prior for the intercept of the baseline tumour size standard deviation predictor.
omega_b_coefficients_prior: (Prior) Prior for the covariate coefficients of the baseline tumour size standard deviation predictor.
mu_g_intercept_prior: (Prior) Prior for the intercept of the growth mean predictor.
mu_g_coefficients_prior: (Prior) Prior for the covariate coefficients of the growth mean predictor.
omega_g_intercept_prior: (Prior) Prior for the intercept of the growth standard deviation predictor.
omega_g_coefficients_prior: (Prior) Prior for the covariate coefficients of the growth standard deviation predictor.
mu_c_intercept_prior: (Prior) Prior for the intercept of the resistance mean predictor.
mu_c_coefficients_prior: (Prior) Prior for the covariate coefficients of the resistance mean predictor.
omega_c_intercept_prior: (Prior) Prior for the intercept of the resistance standard deviation predictor.
omega_c_coefficients_prior: (Prior) Prior for the covariate coefficients of the resistance standard deviation predictor.
mu_p_intercept_prior: (Prior) Prior for the intercept of the growth inhibition mean predictor.
mu_p_coefficients_prior: (Prior) Prior for the covariate coefficients of the growth inhibition mean predictor.
omega_p_intercept_prior: (Prior) Prior for the intercept of the growth inhibition standard deviation predictor.
omega_p_coefficients_prior: (Prior) Prior for the covariate coefficients of the growth inhibition standard deviation predictor.
sigma: (Prior) Observation-error standard deviation prior.
scaled_variance: (logical) Whether to use multiplicative observation error.
centred_baseline: (logical) Whether to use centred parameterizations.
centred_growth: (logical) Whether to use centred parameterizations.
centred_resistance: (logical) Whether to use centred parameterizations.
centred_inhibition: (logical) Whether to use centred parameterizations.

Value

A LongitudinalClaretBrunoCov object.

Usage

Arguments

Value

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