| c212.interim.BB.hier3 {c212} | R Documentation |
A Three-Level Hierarchical Body-system based Model for interim analysis with Point-Mass.
Description
Implementation of a Three-Level Hierarchical Body-system based Model for interim analysis with Point-Mass.
Usage
c212.interim.BB.hier3(trial.data, sim_type = "SLICE", burnin = 20000,
iter = 60000, nchains = 5, theta_algorithm = "MH",
global.sim.params = data.frame(type = c("MH", "MH", "MH", "MH",
"SLICE", "SLICE", "SLICE"),
param = c("sigma_MH_alpha", "sigma_MH_beta", "sigma_MH_gamma",
"sigma_MH_theta", "w_alpha", "w_beta", "w_gamma"),
value = c(3, 3, 0.2, 0.25, 1, 1, 1), control = c(0, 0, 0, 0, 6, 6, 6),
stringsAsFactors = FALSE),
sim.params = NULL,
monitor = data.frame(variable = c("theta", "gamma", "mu.gamma", "mu.theta",
"sigma2.theta", "sigma2.gamma",
"mu.theta.0", "mu.gamma.0", "tau2.theta.0", "tau2.gamma.0",
"pi", "alpha.pi", "beta.pi"),
monitor = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
stringsAsFactors = FALSE),
initial_values = NULL, level = 1, hyper_params = list(mu.gamma.0.0 = 0,
tau2.gamma.0.0 = 10, mu.theta.0.0 = 0, tau2.theta.0.0 = 10,
alpha.gamma.0.0 = 3, beta.gamma.0.0 = 1, alpha.theta.0.0 = 3,
beta.theta.0.0 = 1, alpha.gamma = 3,
beta.gamma = 1, alpha.theta = 3, beta.theta = 1,
lambda.alpha = 1.0, lambda.beta = 1.0),
global.pm.weight = 0.5,
pm.weights = NULL,
adapt_phase=1, memory_model = "HIGH")
Arguments
trial.data |
A file or data frame containing the trial data. It must contain must contain the columns B (body-system), AE (adverse event), Group (1 - control, 2 treatment), Count (total number of events), Total (total number of participants). |
burnin |
The burnin period for the monte-carlo simulation. These are discarded from the returned samples. |
iter |
The total number of iterations for which the monte-carlo simulation is run. This includes the burnin period. The total number of samples returned is iter - burnin |
nchains |
The number of independent chains to run. |
theta_algorithm |
MCMC algorithm used to sample the theta variables. "MH" is the only currently supported stable algorithm. |
sim_type |
The type of MCMC method to use for simulating from non-standard distributions apart from theta. Allowed values are "MH" and "SLICE" for Metropolis_Hastings and Slice sampling respectively. |
monitor |
A dataframe indicating which sets of variables to monitor. |
global.sim.params |
A data frame containing the parameters for the simulation type sim_type. For "MH" the parameter is the variance of the normal distribution used to simulate the next candidate value centred on the current value. For "SLICE" the parameters are the estimated width of the slice and a value limiting the search for the next sample. |
sim.params |
A dataframe containing simulation parameters which override the global simulation parameters (global.sim.params) for particular model parameters. sim.params must contain the following columns: type: the simulation type ("MH" or "SLICE"); variable: the model parameter for which the simulation parameters are being overridden; B: the body-system (if applicable); AE: the adverse event (if applicable); param: the simulation parameter; value: the overridden value; control: the overridden control value. The function c212.sim.control.params generates a template for sim.params which can be edited by the user. |
initial_values |
The initial values for starting the chains. If NULL (the default) is passed the function generates the initial values for the chains. initial_values is a list with the following format: list(gamma, theta, mu.gamma, mu.theta, sigma2.gamma, sigma2.theta, pi, mu.gamma.0, mu.theta.0, tau2.gamma.0, tau2.theta.0, alpha.pi, beta.pi) The function c212.gen.initial.values can be used to generate a template for the list which can be updated by the user if required. The formats of the list elements are as follows: gamma, theta: dataframe with columns B, AE, chain, value mu.gamma, mu.theta, sigma2.gamma, sigma2.theta, pi: dataframe with columns B, chain, value mu.gamma.0, mu.theta.0, tau2.gamma.0, tau2.theta.0, alpha.pi, beta.pi: array of size chain. |
level |
Allowed values are 0, 1, 2. Respectively these indicate independent intervals, common body-system means across the intervals and weak relationships between the intervals. |
hyper_params |
The hyperparameters for the model. The default hyperparameters are those given in Berry and Berry 2004. |
global.pm.weight |
A global weighting for the proposal distribution used to sample theta. |
pm.weights |
Override global.pm.weight for specific adverse events. |
adapt_phase |
Unused parameter. |
memory_model |
Allowed values are "HIGH" and "LOW". "HIGH" means use as much memory as possible. "LOW" means use the minimum amount of memory. |
Details
The model is fitted by a Gibbs sampler. The details of the complete conditional densities are given in Berry and Berry (2004).
Value
The output from the simulation including all the sampled values is as follows:
list(id, theta_alg, sim_type, chains, nIntervals, Intervals, nBodySys, maxBs, maxAEs, nAE, AE, B, burnin, iter, monitor, mu.gamma.0, mu.theta.0, tau2.gamma.0, tau2.theta.0, mu.gamma, mu.theta, sigma2.gamma, sigma2.theta, pi, alpha.pi, beta.pi, alpha.pi_acc, beta.pi_acc, gamma, theta, gamma_acc, theta_acc)
where
id - a string identifying the version of the function
theta_alg - an string identifying the algorithm used to sample theta
sim_type - an string identifying the sampling method used for non-standard distributions, either "MH" or "SLICE"
chains - the number of chains for which the simulation was run
nIntervals - the number of intervals in the simulation
Intervals - an array. The intervals.
nBodySys - the number of body-systems
maxBs - the maximum number of body-systems in an interval
maxAEs - the maximum number of AEs in a body-system
nAE - an array. The number of AEs in each body-system.
AE - an array of dimension nBodySys, maxAEs. The Adverse Events.
B - an array. The body-systems.
burnin - burnin used for the simulation.
iter - the total number of iterations in the simulation.
monitor - the variables being monitored. A dataframe.
mu.gamma.0 - array of samples of dimension chains, iter - burnin
mu.theta.0 - array of samples of dimension chains, iter - burnin
tau2.gamma.0 - array of samples of dimension chains, iter - burnin
tau2.theta.0 - array of samples of dimension chains, iter - burnin
mu.gamma - array of samples of dimension chains, nBodySys iter - burnin
mu.theta - array of samples of dimension chains, nBodySys iter - burnin
sigma2.gamma - array of samples of dimension chains, nBodySys iter - burnin
sigma2.theta - array of samples of dimension chains, nBodySys iter - burnin
pi - array of samples of dimension chains, nBodySys iter - burnin alpha.pi - array of samples of dimension chains, iter - burnin beta.pi - array of samples of dimension chains, iter - burnin
alpha.pi_acc - the acceptance rate for the alpha.pi samples if a Metropolis-Hastings method is used. An array of dimension chains, maxAEs
beta.pi_acc - the acceptance rate for the beta.pi samples if a Metropolis-Hastings method is used. An array of dimension chains, maxAEs
gamma - array of samples of dimension chains, nBodySys, maxAEs, iter - burnin
theta - array of samples of dimension chains, nBodySys, maxAEs, iter - burnin
gamma_acc - the acceptance rate for the gamma samples if a Metropolis-Hastings method is used. An array of dimension chains, nBodySys, maxAEs
theta_acc - the acceptance rate for the theta samples. An array of dimension chains, nBodySys, maxAEs
Note
The function performs the simulation and returns the raw output. No checks for convergence are performed.
Author(s)
R. Carragher
Examples
data(c212.trial.interval.data1)
raw = c212.interim.BB.hier3(c212.trial.interval.data1, level = 1, burnin = 100, iter = 200)
## Not run:
data(c212.trial.interval.data1)
raw = c212.interim.BB.hier3(c212.trial.interval.data1, level = 1)
raw$B
[,1] [,2] [,3] [,4] [,5]
[1,] "Bdy-sys_1" "Bdy-sys_10" "Bdy-sys_11" "Bdy-sys_12" "Bdy-sys_13"
[2,] "Bdy-sys_1" "Bdy-sys_10" "Bdy-sys_11" "Bdy-sys_12" "Bdy-sys_13"
[3,] "Bdy-sys_1" "Bdy-sys_10" "Bdy-sys_11" "Bdy-sys_12" "Bdy-sys_13"
[4,] "Bdy-sys_1" "Bdy-sys_10" "Bdy-sys_11" "Bdy-sys_12" "Bdy-sys_13"
[5,] "Bdy-sys_1" "Bdy-sys_10" "Bdy-sys_11" "Bdy-sys_12" "Bdy-sys_13"
[6,] "Bdy-sys_1" "Bdy-sys_10" "Bdy-sys_11" "Bdy-sys_12" "Bdy-sys_13"
[,6] [,7] [,8] [,9] [,10] [,11]
[1,] "Bdy-sys_14" "Bdy-sys_15" "Bdy-sys_2" "Bdy-sys_3" "Bdy-sys_4" "Bdy-sys_5"
[2,] "Bdy-sys_14" "Bdy-sys_15" "Bdy-sys_2" "Bdy-sys_3" "Bdy-sys_4" "Bdy-sys_5"
[3,] "Bdy-sys_14" "Bdy-sys_15" "Bdy-sys_2" "Bdy-sys_3" "Bdy-sys_4" "Bdy-sys_5"
[4,] "Bdy-sys_14" "Bdy-sys_15" "Bdy-sys_2" "Bdy-sys_3" "Bdy-sys_4" "Bdy-sys_5"
[5,] "Bdy-sys_14" "Bdy-sys_15" "Bdy-sys_2" "Bdy-sys_3" "Bdy-sys_4" "Bdy-sys_5"
[6,] "Bdy-sys_14" "Bdy-sys_15" "Bdy-sys_2" "Bdy-sys_3" "Bdy-sys_4" "Bdy-sys_5"
[,12] [,13] [,14] [,15]
[1,] "Bdy-sys_6" "Bdy-sys_7" "Bdy-sys_8" "Bdy-sys_9"
[2,] "Bdy-sys_6" "Bdy-sys_7" "Bdy-sys_8" "Bdy-sys_9"
[3,] "Bdy-sys_6" "Bdy-sys_7" "Bdy-sys_8" "Bdy-sys_9"
[4,] "Bdy-sys_6" "Bdy-sys_7" "Bdy-sys_8" "Bdy-sys_9"
[5,] "Bdy-sys_6" "Bdy-sys_7" "Bdy-sys_8" "Bdy-sys_9"
[6,] "Bdy-sys_6" "Bdy-sys_7" "Bdy-sys_8" "Bdy-sys_9"
## End(Not run)