R/hb_mcmc_mixture_hyperparameters.R
hb_mcmc_mixture_hyperparameters.RdRun a simple model separately on each historical study
control group and compute hyperparameters for hb_mcmc_mixture().
hb_mcmc_mixture_hyperparameters(
data,
response = "response",
study = "study",
study_reference = max(data[[study]]),
group = "group",
group_reference = min(data[[group]]),
patient = "patient",
m_mu = 0,
s_mu = 30,
s_sigma = 30,
m_omega_current = 0,
s_omega_current = 30,
n_chains = 4,
n_adapt = 2000,
n_warmup = 4000,
n_iterations = 20000,
quiet = TRUE
)Tidy data frame with one row per patient,
indicator columns for the response variable,
study, group, and patient,
and covariates. All columns must be atomic vectors
(e.g. not lists). The data for the mixture and simple models
should have just one study,
and the others should have
data from more than one study. The simple model can be used
to get the historical data components of m_omega and s_omega
for the mixture model.
Character of length 1,
name of the column in data with the response/outcome variable.
data[[response]] must be a continuous variable,
and it should be the change from baseline of a
clinical endpoint of interest, as opposed to just
the raw response. Treatment differences
are computed directly from this scale, please supply
change from baseline unless you are absolutely certain
that treatment differences computed directly from
this quantity are clinically meaningful.
Character of length 1,
name of the column in data with the study ID.
Atomic of length 1,
element of the study column that indicates
the current study.
(The other studies are historical studies.)
Character of length 1,
name of the column in data with the group ID.
Atomic of length 1,
element of the group column that indicates
the control group.
(The other groups may be treatment groups.)
Character of length 1,
name of the column in data with the patient ID.
Numeric of length 1, prior mean of the mean mu in
the simple model.
Numeric of length 1, prior standard deviation of the
mean mu in the simple model.
Numeric of length 1, uniform prior upper bound
of the residual standard deviation sigma in the simple model.
Numeric with length 1,
m_omega value of the current study.
Inserted as the final component of the m_omega column in the output.
Numeric with length 1,
s_omega value of the current study.
Inserted as the final component of the s_omega column in the output.
Number of MCMC chains to run.
Number of adaptation iterations to run.
Number of warmup iterations per chain to run.
Number of saved MCMC iterations per chain to run.
Logical of length 1, TRUE to suppress R console output.
A tidy data frame of hyperparameter values for hb_mcmc_mixture().
The first several rows are for historical studies, and the last row
is for the current study. Studies/rows are sorted in the order
hb_mcmc_mixture() sorts them, so you can use columns m_omega
and s_omega for the same dataset and same values of other arguments
directly in hb_mcmc_mixture().
The model is a simple Bayesian model with a normal likelihood,
an unknown mean mu, and an unknown standard deviation sigma.
For each historical study, the posterior mean of mu becomes
the corresponding component of m_omega in the output,
and the posterior standard deviation of mu
becomes the corresponding component of s_omega in the output.
See the examples in this help file for a demonstration.
m_omega and s_omega define the components of the mixture prior
in hb_mcmc_mixture() that act as the contribution of the
historical studies to the model.
Other mcmc:
hb_convergence(),
hb_mcmc_hierarchical(),
hb_mcmc_independent(),
hb_mcmc_mixture(),
hb_mcmc_pool()
data_all_studies <- hb_sim_independent(n_continuous = 2)$data
data_all_studies$study <- paste0("study", data_all_studies$study)
hyperparameters <- hb_mcmc_mixture_hyperparameters(
data = data_all_studies,
response = "response",
study = "study",
study_reference = "study5",
group = "group",
group_reference = 1,
patient = "patient",
n_chains = 1,
n_adapt = 100,
n_warmup = 50,
n_iterations = 50
)
print(hyperparameters)
#> # A tibble: 5 × 4
#> study study_index m_omega s_omega
#> <chr> <int> <dbl> <dbl>
#> 1 study1 1 0.975 0.166
#> 2 study2 2 1.51 0.103
#> 3 study3 3 0.821 0.252
#> 4 study4 4 1.29 0.216
#> 5 study5 5 0 30
data_current_study <- dplyr::filter(data_all_studies, study == max(study))
hb_mcmc_mixture(
data = data_current_study,
response = "response",
study = "study",
study_reference = "study5",
group = "group",
group_reference = 1,
patient = "patient",
m_omega = hyperparameters$m_omega, # use hyperparams from historical data
s_omega = hyperparameters$s_omega, # use hyperparams from historical data
p_omega = rep(1 / nrow(hyperparameters), nrow(hyperparameters)),
n_chains = 1,
n_adapt = 100,
n_warmup = 50,
n_iterations = 50
)
#> # A tibble: 50 × 19
#> alpha beta[…¹ beta[…² delta…³ delta…⁴ omega…⁵ omega…⁶ omega…⁷ omega…⁸ omega…⁹
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 -1.78 0.365 -1.14 0.769 0.361 1.10 1.53 0.672 1.48 -1.78
#> 2 -1.60 0.384 -1.03 0.873 0.224 1.08 1.63 0.823 1.46 -1.60
#> 3 -1.73 0.356 -1.04 0.834 0.329 1.02 1.45 1.13 1.55 -1.73
#> 4 -1.54 0.369 -0.992 0.885 0.327 1.10 1.59 0.819 1.25 -1.54
#> 5 -1.61 0.337 -1.13 1.09 0.502 1.00 1.53 0.883 0.951 -1.61
#> 6 -1.67 0.374 -1.06 0.528 0.467 1.01 1.53 0.871 1.13 -1.67
#> 7 -1.63 0.399 -1.03 0.918 0.354 0.873 1.58 0.414 1.54 -1.63
#> 8 -1.56 0.355 -1.04 0.882 0.366 0.960 1.64 0.596 1.24 -1.56
#> 9 -1.53 0.406 -1.07 0.951 0.341 1.21 1.47 0.483 1.68 -1.53
#> 10 -1.69 0.392 -1.13 0.937 0.283 0.925 1.61 1.05 1.27 -1.69
#> # … with 40 more rows, 9 more variables: `post_p[1]` <dbl>, `post_p[2]` <dbl>,
#> # `post_p[3]` <dbl>, `post_p[4]` <dbl>, `post_p[5]` <dbl>, sigma <dbl>,
#> # .chain <int>, .iteration <int>, .draw <int>, and abbreviated variable names
#> # ¹`beta[1]`, ²`beta[2]`, ³`delta[1]`, ⁴`delta[2]`, ⁵`omega[1]`, ⁶`omega[2]`,
#> # ⁷`omega[3]`, ⁸`omega[4]`, ⁹`omega[5]`