Run the longitudinal pooled model with MCMC.
hbl_mcmc_pool(
data,
response = "response",
study = "study",
study_reference = max(data[[study]]),
group = "group",
group_reference = min(data[[group]]),
patient = "patient",
rep = "rep",
rep_reference = min(data[[rep]]),
covariates = grep("^covariate", colnames(data), value = TRUE),
constraint = FALSE,
s_alpha = 30,
s_delta = 30,
s_beta = 30,
s_sigma = 30,
s_lambda = 1,
covariance_current = "unstructured",
covariance_historical = "unstructured",
control = list(max_treedepth = 17, adapt_delta = 0.99),
...
)Tidy data frame with one row per patient per rep, indicator columns for the response variable, study, group, patient, rep, and covariates. All columns must be atomic vectors (e.g. not lists).
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.
Character of length 1,
name of the column in data with the rep ID.
Atomic of length 1,
element of the rep column that indicates
baseline, i.e. the first rep chronologically.
(The other reps may be post-baseline study visits or time points.)
Character vector of column names
in data with the columns with baseline covariates.
These can be continuous, categorical, or binary.
Regardless, historicalborrowlong derives the appropriate
model matrix.
Each baseline covariate column must truly be a baseline covariate: elements must be equal for all time points within each patient (after the steps in the "Data processing" section). In other words, covariates must not be time-varying.
A large number of covariates, or a large number of levels in a categorical covariate, can severely slow down the computation. Please consider carefully if you really need to include such complicated baseline covariates.
Logical of length 1, whether to pool all study arms at baseline (first rep). Appropriate when the response is the raw response (as opposed to change from baseline) and the first rep (i.e. time point) is prior to treatment.
Numeric of length 1, prior standard deviation
of the study-specific control group mean parameters alpha.
Numeric of length 1, prior standard deviation
of the study-by-group effect parameters delta.
Numeric of length 1, prior standard deviation
of the fixed effects beta.
Numeric of length 1, prior upper bound of the residual standard deviations.
shape parameter of the LKJ priors on the unstructured correlation matrices.
Character of length 1,
covariance structure of the current study.
Possible values are "unstructured" for fully parameterized
covariance matrices, "ar1" for AR(1) covariance matrices,
and "diagonal" for residuals independent across time within
each patient. In MCMC (e.g. hbl_mcmc_hierarchical()),
the covariance structure affects computational speed.
Unstructured covariance is slower than AR(1), and AR(1) is slower
than diagonal. This is particularly true for covariance_historical
if there are many historical studies in the data.
Same as covariance_current,
but for the covariance structure of each separate historical study.
Each historical study has its own separate covariance matrix.
A named list of parameters to control the sampler's
behavior. It defaults to NULL so all the default values are used.
First, the following are adaptation parameters for sampling algorithms.
These are parameters used in Stan with similar names here.
adapt_engaged (logical)
adapt_gamma (double, positive, defaults to 0.05)
adapt_delta (double, between 0 and 1, defaults to 0.8)
adapt_kappa (double, positive, defaults to 0.75)
adapt_t0 (double, positive, defaults to 10)
adapt_init_buffer (integer, positive, defaults to 75)
adapt_term_buffer (integer, positive, defaults to 50)
adapt_window (integer, positive, defaults to 25)
In addition, algorithm HMC (called 'static HMC' in Stan) and NUTS share the following parameters:
stepsize (double, positive, defaults to 1)
Note: this controls the initial stepsize only, unless adapt_engaged=FALSE.
stepsize_jitter (double, [0,1], defaults to 0)
metric (string, one of "unit_e", "diag_e", "dense_e",
defaults to "diag_e")
For algorithm NUTS, we can also set:
max_treedepth (integer, positive, defaults to 10)
For algorithm HMC, we can also set:
int_time (double, positive)
For test_grad mode, the following parameters can be set:
epsilon (double, defaults to 1e-6)
error (double, defaults to 1e-6)
Additional named arguments of rstan::sampling().
See the documentation of rstan::sampling() for details.
A tidy data frame of parameter samples from the
posterior distribution. Columns .chain, .iteration,
and .draw have the meanings documented in the
posterior package.
Before running the MCMC, dataset is pre-processed. This includes expanding the rows of the data so every rep of every patient gets an explicit row. So if your original data has irregular rep IDs, e.g. unscheduled visits in a clinical trial that few patients attend, please remove them before the analysis. Only the most common rep IDs should be added.
After expanding the rows, the function fills in missing values for every column except the response. That includes covariates. Missing covariate values are filled in, first with last observation carried forward, then with last observation carried backward. If there are still missing values after this process, the program throws an informative error.
Other mcmc:
hbl_convergence(),
hbl_mcmc_hierarchical(),
hbl_mcmc_independent(),
hbl_mcmc_sge()
if (!identical(Sys.getenv("HBL_TEST", unset = ""), "")) {
set.seed(0)
data <- hbl_sim_pool(
n_study = 3,
n_group = 2,
n_patient = 5,
n_rep = 3
)$data
tmp <- utils::capture.output(
suppressWarnings(
mcmc <- hbl_mcmc_pool(
data,
chains = 1,
warmup = 10,
iter = 20,
seed = 0
)
)
)
mcmc
}