n_opt: Optimise A Simple Normal Model
In bdpopt: Optimisation of Bayesian Decision Problems

Description Usage Arguments Value Author(s) See Also

Find an approximation of the optimal sample size and corresponding expected utility for a simple phase III clinical trial model with a single, normally distributed response and a utility function of a fixed form.

n.opt(nu = 0, tau = 1, sigma = 1, alpha = 0.025,
gain.constant = 1, gain.function = function(X, mu) 0,
fixed.cost = 0, sample.cost = 0.005,
k = 1, n.min = 1, n.max = 50, n.step = 1,
n.iter = 10000, n.burn.in = 1000, n.adapt = 1000,
regression.type = "loess",
plot.results = TRUE, independent.SE = FALSE,
parallel = FALSE, path.to.package = NA)

`nu`	The mean of the conjugate normal prior distribution for the unknown population mean.
`tau`	The standard deviation of the conjugate normal prior distribution for the unknown population mean.
`sigma`	The known population standard deviation for each individual response in the trial.
`alpha`	The significance level in the one-sided test used by the regulatory authority to decide upon marketing approval for the new treatment.
`gain.constant`	A constant utility gain received upon treatment approval. The total gain consists of the sum of `gain.constant` and `gain.function`.
`gain.function`	A variable utility gain obtained in addition to the constant utility gain upon treatment approval.
`fixed.cost`	The fixed cost of performing the trial.
`sample.cost`	The marginal cost per observation for the trial.
`k`	The number independent, parallel trials. Must be an integer greater than zero.
`n.min`	Lower limit for the one-dimensional grid for the sample size.
`n.max`	Upper limit for the one-dimensional grid for the sample size.
`n.step`	The step size of the grid for the sample size.
`n.iter`	The number of iterations in the JAGS MCMC simulation.
`n.burn.in`	The number of burn iterations prior to the JAGS MCMC simulation.
`n.adapt`	The number of adaptation iterations prior to the burn in and JAGS MCMC simulation.
`regression.type`	If set to `"loess"`, the default value, then local polynomial regression will be used (via a call to `fit.loess`) to fit the grid simulation results. If set to `"gpr"`, GPR regression will be used instead. For any other value, no regression is performed and the optimisation done will consist of a maximisation over the values corresponding to the grid points.
`plot.results`	Set to `TRUE` if a plot of the results of the simulation over the grid is to be constructed.
`independent.SE`	If `TRUE`, then the standard errors of the sample means used to estimate the expected utility will be computed under the assumption of i.i.d. sampling. If `FALSE`, the standard errors are instead computed using the `coda::spectrum0.ar` function.
`parallel`	Set to `TRUE` if the simulations over the grid should be done in parallel on a multi-core processor. The default value `FALSE` leads to single-core computations.
`path.to.package`	The search path to the installation directory of bdpopt. For the default value, the function will attempt to find the path using `search`.

A list with components

`ns`	A numeric, atomic vector containing the sample size grid points.
`eus`	A numeric, atomic vector containing the sample means of the simulated expected utilities corresponding to the sample size grid points.
`opt.arg`	The optimal sample size found by maximising the estimated expected utility.
`opt.eu`	The estimated optimal utility corresponding to the optimal sample size found.