OneSampleNormal2.Design {BayesDIP} R Documentation

## One sample Normal model with two-parameter unknown - both mean and variance unknown

### Description

Calculate the minimum planned sample size under an admissible design. The users decide the power and type-I-error, and pick the efficacy and futility boundaries. If there are no admissible design based on controlled type-I-error, then default to output the designs with the lowest type-I-error and at least the user-defined (e.g. 80%) power.

### Usage

OneSampleNormal2.Design(
prior,
nmin = 10,
nmax = 100,
mu0,
mu1,
var0,
var,
d = 0,
ps,
pf,
power = 0.8,
t1error = 0.05,
alternative = c("less", "greater"),
seed = 202209,
sim = 1000
)


### Arguments

 prior A list of length 3 containing the distributional information of the prior. The first element is a number specifying the type of prior. Options are DIP ; Normal(mu0,var/k) and var ~ Inverse-Gamma(v/2, v*var0/2) where mu0 = prior mean, k = sample size of prior observations (Normal prior), v = sample size of prior observations (Gamma prior), var0 = prior sample variance The second and third elements of the list are the parameters k and v, respectively. nmin The start searching sample size nmax The stop searching sample size mu0 The null mean value, which could be taken as the standard or current mean. mu1 The mean value of the new treatment. var0 The prior sample variance var The variance d The target improvement (minimal clinically meaningful difference). ps The efficacy boundary (upper boundary). pf The futility boundary (lower boundary). power The power to achieve. t1error The controlled type-I-error. alternative less (lower values imply greater efficacy) or greater (larger values imply greater efficacy). seed The seed for simulations. sim The number of simulations.

### Value

A list of the arguments with method and computed elements.

### Examples


# with traditional Bayesian prior Beta(1,1)
OneSampleNormal2.Design(list(2,2,1), nmin = 10, nmax = 100, mu0 = 100, mu1 = 95,
var0=225, var=225, d = 0, ps = 0.95, pf = 0.05,
power = 0.8, t1error = 0.05, alternative = "less",
seed = 202210, sim = 10)
# with DIP
OneSampleNormal2.Design(list(1,0,0), nmin = 10, nmax = 100, mu0 = 100, mu1 = 95,
var0=225, var=225, d = 0, ps = 0.95, pf = 0.05,
power = 0.8, t1error = 0.05, alternative = "less",
seed = 202210, sim = 10)



[Package BayesDIP version 0.1.1 Index]