Description Usage Arguments Details Value References See Also Examples

Model fitting using power priors for two groups (treatment and control group, no covariates) with fixed *a_0* when outcome follows Normal distribution

1 2 3 4 5 6 7 8 | ```
two.grp.fixed.a0(
y.c,
n.c,
v.c,
historical = matrix(0, 1, 4),
nMC = 10000,
nBI = 250
)
``` |

`y.c` |
Sum of responses (assumed to follow Normal distribution) for the control group. |

`n.c` |
Sample size of the control group. |

`v.c` |
Sample variance of responses for the control group. |

`historical` |
(Optional) matrix of historical dataset(s) with four columns: The first column contains the sum of responses for the control group. The second column contains the sample size of the control group. The third column contains the sample variance of responses for the control group. The fourth column contains the discounting parameter value *a_0*(between 0 and 1).
Each row represents a historical dataset. |

`nMC` |
Number of iterations (excluding burn-in samples) for the Gibbs sampler. The default is 10,000. |

`nBI` |
Number of burn-in samples for the Gibbs sampler. The default is 250. |

The power prior is applied on the data of the control group only. Therefore, only summaries of the responses of the control group need to be entered.

The responses are assumed to follow *N(μ_c, τ^{-1})* where *μ_c* is the mean of responses for the control group
and *τ* is the precision parameter. Each historical dataset *D_{0k}* is assumed to have a different precision parameter *τ_k*.
The initial prior for *τ* is the Jeffery's prior, *τ^{-1}*, and the initial prior for *τ_k* is *τ_k^{-1}*. The initial prior for the *μ_c* is the uniform improper prior.
Posterior samples are obtained through Gibbs sampling.

Posterior samples of *μ_c*, *τ* and *τ_k*'s (if historical data is given) are returned.

Chen, Ming-Hui, et al. "Bayesian design of noninferiority trials for medical devices using historical data." Biometrics 67.3 (2011): 1163-1170.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
y.c <- 200 # The responses are assumed to follow normal distribution
n.c <- 100
v.c <- 2
# Simulate three historical datasets
historical <- matrix(0, ncol=4, nrow=3)
historical[1,] <- c(200, 100, 2, 0.3)
historical[2,] <- c(300, 100, 2, 0.5)
historical[3,] <- c(400, 100, 2, 0.7)
set.seed(1)
result <- two.grp.fixed.a0(y.c, n.c, v.c, historical, nMC=10000, nBI=250)
``` |

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