| c212.interim.1a.hier2 {c212} | R Documentation |
A Two-Level Hierarchical Body-system based Model for interim analysis without Point-Mass.
Description
Implementation of a Two-Level Hierarchical Body-system based Model for interim analysis without Point-Mass.
Usage
c212.interim.1a.hier2(trial.data, sim_type = "SLICE", burnin = 10000,
iter = 40000, nchains = 3,
global.sim.params = data.frame(type = c("MH", "SLICE"),
param = c("sigma_MH", "w"), value = c(0.2,1), control = c(0,6),
stringsAsFactors = FALSE),
sim.params = NULL,
monitor = data.frame(variable = c("theta", "gamma", "mu.gamma",
"mu.theta", "sigma2.theta", "sigma2.gamma"),
monitor = c(1, 1, 1, 1, 1, 1),
stringsAsFactors = FALSE),
initial_values = NULL,
level = 1,
hyper_params = list(mu.gamma.0 = 0, tau2.gamma.0 = 10, mu.theta.0 = 0,
tau2.theta.0 = 10, alpha.gamma = 3, beta.gamma = 1,
alpha.theta = 3, beta.theta = 1),
memory_model = "HIGH")
Arguments
trial.data |
A file or data frame containing the trial data. It must contain must contain the columns I_index (interval index), B (body-system), AE (adverse event), Group (1 - control, 2 treatment), Count (total number of events), Total (total number of participants in the trial arm). |
sim_type |
The type of MCMC method to use for simulating from non-standard distributions. Allowed values are "MH" and "SLICE" for Metropolis_Hastings and Slice sampling respectively. |
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. |
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. |
monitor |
A dataframe indicating which sets of variables to monitor. |
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) where each element of the list is either a dataframe or array. 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: dataframe with columns B, chain, value |
level |
The level of longitudinal dependency between the intervals. 0 - independent intervals, 1 - common interval body-system means. |
hyper_params |
The hyperparameters for the model. The default hyperparameters are those given in Berry and Berry 2004. |
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 posterior distributions for gamma and theta are sampled with either a Metropolis-Hastings step or a slice sampler.
Value
The output from the simulation including all the sampled values is as follows:
list(id, sim_type, chains, nIntervals, Intervals, nBodySys, maxBs, maxAEs, nAE, AE, B, burnin, iter, monitor, mu.gamma, mu.theta, sigma2.gamma, sigma2.theta, gamma, theta, gamma_acc, theta_acc)
where
id - a string identifying the version of the function
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 - 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
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 if a Metropolis-Hastings method is used. 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.1a.hier2(c212.trial.interval.data1, level = 1, burnin = 100, iter = 200)
## Not run:
data(c212.trial.interval.data1)
raw = c212.interim.1a.hier2(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)