cace.study {BayesCACE} | R Documentation |

This function performs CACE analysis for a single study using the likelihood and model specified in Section 2.1 of the package manuscript, or a two-step approach for meta-analysis with complete compliance information as described in Section 2.2, "the two-step approach".

```
cace.study(
data,
param = c("CACE", "u1", "v1", "s1", "b1", "pi.c", "pi.n", "pi.a"),
re.values = list(),
model.code = "",
digits = 3,
n.adapt = 1000,
n.iter = 1e+05,
n.burnin = floor(n.iter/2),
n.chains = 3,
n.thin = max(1, floor((n.iter - n.burnin)/1e+05)),
conv.diag = FALSE,
mcmc.samples = FALSE,
two.step = FALSE,
method = "REML"
)
```

`data` |
a input dataset the same structure as the example data |

`param` |
a character string vector indicating the parameters to be tracked and estimated.
By default all parameters in the model (see |

`re.values` |
a list of parameter values for the random effects. It should contain the assignment for these
parameters only: |

`model.code` |
a string representation of the model code; each line should be separated. Default to constructing
model code using the |

`digits` |
a positive integer specifying the digits after the decimal point for
the effect size estimates. The default is |

`n.adapt` |
the number of iterations for adaptation in Markov chain Monte Carlo (MCMC) algorithm;
it is used to maximize the sampling efficiency.
The default is |

`n.iter` |
the number of iterations of each MCMC chain.
The default is |

`n.burnin` |
the number of iterations for burn-in period. The default is
the largest integer not greater than |

`n.chains` |
the number of MCMC chains. The default is |

`n.thin` |
a positive integer indicating thinning rate for MCMC chains, which is used to
avoid potential high auto-correlation and to save computer memory when |

`conv.diag` |
a logical value indicating whether to compute the Gelman and Rubin
convergence statistic ( |

`mcmc.samples` |
a logical value indicating whether to save MCMC posterior samples
in the output object. The default is |

`two.step` |
a logical value indicating whether to conduct a two-step meta-analysis.
If |

`method` |
the method used in meta-analysis if |

The likelihood

```
\log L({\boldsymbol{\beta}}) = N_{000}\log\{\pi_{c}(1-v_1)+\pi_{n}(1-s_1)\}+N_{001}
\log(\pi_{c}v_1+\pi_{n}s_1)+N_{010}\log\{{\pi}_{a}(1-b_1)\}
```

```
+ N_{011}\log\{\pi_{a}b_1\}+ N_{100}
\log\{\pi_{n}(1-s_1)\}+N_{101}\log({\pi}_{n}s_1) + N_{110}\log\{(\pi_{c}(1-u_1)
```

`+ \pi_{a}(1-b_1)\}+{N_{111}\log(\pi_{c}u_1+\pi_{a}b_1)} + constant`

.
If the input `data`

includes more than one study, the study-specific CACEs will be
estimated by retrieving data row by row.
By default, the function `cace.study()`

returns a list
including posterior estimates (posterior mean, standard deviation, median, and a 95%
credible interval (CrI) with 2.5% and 97.5% quantiles as the lower and upper bounds),
and the deviance information criterion (DIC) statistic for each study.

It returns a model object whose attribute type is `cace.Bayes`

```
data("epidural_c", package = "BayesCACE")
set.seed(123)
out.study <- cace.study(data = epidural_c, conv.diag = TRUE,
mcmc.samples = TRUE, two.step = TRUE)
# Show the estimates of theta for each single study (posterior mean and
# standard deviation, posterior median, 95% credible interval, and time-series
# standard error):
out.study$CACE
# If the argument conv.diag is specified as TRUE, the output list contains
# a sub-list conv.out, which outputs the Gelman and Rubin convergence statistic,
# labelled Point est.) calculated for each parameter from each single study, and
# their upper confidence limits (labelled Upper C.I.).
out.study$conv.out[[1]]
```

[Package *BayesCACE* version 1.2.3 Index]