Description Usage Arguments Details Value References See Also Examples

Power/type I error calculation for data with two groups (treatment and control group, no covariates) with fixed *a_0* using power priors

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
power.two.grp.fixed.a0(
data.type,
n.t,
n.c,
historical = matrix(0, 1, 4),
samp.prior.mu.t,
samp.prior.mu.c,
samp.prior.var.t,
samp.prior.var.c,
prior.mu.t.shape1 = 1,
prior.mu.t.shape2 = 1,
prior.mu.c.shape1 = 1,
prior.mu.c.shape2 = 1,
delta = 0,
gamma = 0.95,
nMC = 10000,
nBI = 250,
N = 10000
)
``` |

`data.type` |
Character string specifying the type of response. The options are "Normal", "Bernoulli", "Poisson" and "Exponential". |

`n.t` |
Sample size of the treatment group for the simulated datasets. |

`n.c` |
Sample size of the control group for the simulated datasets. |

`historical` |
(Optional) matrix of historical dataset(s). If 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).
For all other data types, 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 discounting parameter value *a_0*(between 0 and 1).
Each row represents a historical dataset. |

`samp.prior.mu.t` |
Vector of possible values of |

`samp.prior.mu.c` |
Vector of possible values of |

`samp.prior.var.t` |
Vector of possible values of |

`samp.prior.var.c` |
Vector of possible values of |

`prior.mu.t.shape1` |
First hyperparameter of the initial prior for |

`prior.mu.t.shape2` |
Second hyperparameter of the initial prior for |

`prior.mu.c.shape1` |
First hyperparameter of the initial prior for |

`prior.mu.c.shape2` |
Second hyperparameter of the initial prior for |

`delta` |
Prespecified constant that defines the boundary of the null hypothesis. The default is zero. |

`gamma` |
Posterior probability threshold for rejecting the null. The null hypothesis is rejected if posterior probability is greater |

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

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

`N` |
Number of simulated datasets to generate. The default is 10,000. |

If `data.type`

is "Bernoulli", "Poisson" or "Exponential", a single response from the treatment group is assumed to follow Bern(*μ_t*), Pois(*μ_t*) or Exp(rate=*μ_t*), respectively,
where *μ_t* is the mean of responses for the treatment group. If `data.type`

is "Normal", a single response from the treatment group is assumed to follow *N(μ_t, τ^{-1})*
where *τ* is the precision parameter.
The distributional assumptions for the control group data are analogous.

`samp.prior.mu.t`

and `samp.prior.mu.c`

can be generated using the sampling priors (see example).

If `data.type`

is "Bernoulli", the initial prior for *μ_t* is
beta(`prior.mu.t.shape1`

, `prior.mu.t.shape2`

).
If `data.type`

is "Poisson", the initial prior for *μ_t* is
Gamma(`prior.mu.t.shape1`

, rate=`prior.mu.t.shape2`

).
If `data.type`

is "Exponential", the initial prior for *μ_t* is
Gamma(`prior.mu.t.shape1`

, rate=`prior.mu.t.shape2`

).
The initial priors used for the control group data are analogous.

If `data.type`

is "Normal", 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.

If a sampling prior with support in the null space is used, the value returned is a Bayesian type I error rate. If a sampling prior with support in the alternative space is used, the value returned is a Bayesian power.

If `data.type`

is "Normal", Gibbs sampling is used for model fitting. For all other data types,
numerical integration is used for modeling fitting.

Power or type I error is returned, depending on the sampling prior used. If `data.type`

is "Normal", average posterior means of *μ_c*, *τ* and *τ_k*'s (if historical data is given) are also returned.

Yixuan Qiu, Sreekumar Balan, Matt Beall, Mark Sauder, Naoaki Okazaki and Thomas Hahn (2019). RcppNumerical: 'Rcpp' Integration for Numerical Computing Libraries. R package version 0.4-0. https://CRAN.R-project.org/package=RcppNumerical

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 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
data.type <- "Bernoulli"
n.t <- 100
n.c <- 100
# Simulate three historical datasets
historical <- matrix(0, ncol=3, nrow=3)
historical[1,] <- c(70, 100, 0.3)
historical[2,] <- c(60, 100, 0.5)
historical[3,] <- c(50, 100, 0.7)
# Generate sampling priors
set.seed(1)
b_st1 <- b_st2 <- 1
b_sc1 <- b_sc2 <- 1
samp.prior.mu.t <- rbeta(50000, b_st1, b_st2)
samp.prior.mu.c <- rbeta(50000, b_st1, b_st2)
# The null hypothesis here is H0: mu_t - mu_c >= 0. To calculate power,
# we can provide samples of mu.t and mu.c such that the mass of mu_t - mu_c < 0.
# To calculate type I error, we can provide samples of mu.t and mu.c such that
# the mass of mu_t - mu_c >= 0.
sub_ind <- which(samp.prior.mu.t < samp.prior.mu.c)
# Here, mass is put on the alternative region, so power is calculated.
samp.prior.mu.t <- samp.prior.mu.t[sub_ind]
samp.prior.mu.c <- samp.prior.mu.c[sub_ind]
N <- 1000 # N should be larger in practice
result <- power.two.grp.fixed.a0(data.type=data.type, n.t=n.t, n.c=n.t, historical=historical,
samp.prior.mu.t=samp.prior.mu.t, samp.prior.mu.c=samp.prior.mu.c,
delta=0, N=N)
``` |

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