R: Implementation of the Berry and Berry Three-Level...

c212.1a {c212}

R Documentation

Implementation of the Berry and Berry Three-Level Hierarchical Model without Point-Mass.

Description

Implementaion of Berry and Berry model without the point-mass (Model 1a Xia et al (2011))

Usage

	c212.1a(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(0.35,1), control = c(0,6), stringsAsFactors = FALSE),
	sim.params = NULL,
	initial_values = NULL,
	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))

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 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.
`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, mu.gamma.0, mu.theta.0, tau2.gamma.0, tau2.theta.0) 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 mu.gamma.0, mu.theta.0, tau2.gamma.0, tau2.theta.0: array of size chain.
`hyper_params`	The hyperparameters for the model. The default hyperparameters are those given in Berry and Berry 2004.

Details

The model is fitted by a Gibbs sampler. The details of the complete conditional densities are given in Berry and Berry (2004). 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, nBodySys, maxAEs, nAE, AE, B, burnin,
	iter, mu.gamma.0, mu.theta.0, tau2.gamma.0, tau2.theta.0,
	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

nBodySys - the number of body-systems

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 - the burnin period for the simulation.

iter - the total number of iterations in the simulation.

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

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

References

S. M. Berry and D. A. Berry (2004). Accounting for multiplicities in assessing drug safety: a three- level hierarchical mixture model. Biometrics, 60(2):418-26.

H. Amy Xia, Haijun Ma, and Bradley P. Carlin (2011). Bayesian hierarchical modelling for detecting safety signals in clinical trials. Journal of Biopharmaceutical Statistics, 21(5):1006– 1029.

Scott M. Berry, Bradley P. Carlin, J. Jack Lee, and Peter M¨ller (2010). Bayesian adaptive methods for clinical trials. CRC Press.

Examples

data(c212.trial.data)
raw = c212.1a(c212.trial.data, burnin = 100, iter = 200)
## Not run: 
data(c212.trial.data)
raw = c212.1a(c212.trial.data)

raw$B
[1] "Bdy-sys_1" "Bdy-sys_2" "Bdy-sys_3" "Bdy-sys_4" "Bdy-sys_5" "Bdy-sys_6"
[7] "Bdy-sys_7" "Bdy-sys_8"

mean(rm$theta[2, 3,1,])
[1] 1.306362


## End(Not run)

[Package c212 version 0.98 Index]