View source: R/fit_models_RA_5bm.R

fit_models_RA_5bm | R Documentation |

Computes the posterior distribution of the
parameters in a random-effects meta-analysis (expressed
as a normal-normal hierarchical model) for
each of five benchmark heterogeneity priors and
the actual heterogeneity prior(s) specified.
Applies the function `bayesmeta`

from the package bayesmeta.

```
fit_models_RA_5bm(df, tau.prior=list(), compute.J.bm=TRUE,
m_J=NA, M_J=NA, upper.J=3, digits.J=2,
m_inf=NA, M_inf=NA, rlmc0=0.0001, rlmc1=0.9999,
mu.mean=0, mu.sd=4)
```

`df` |
data frame with one column "y" containing the (transformed) effect estimates for the individual studies and one column "sigma" containing the standard errors of these estimates. |

`tau.prior` |
list of prior specifications, which are |

`compute.J.bm` |
logical. Specifies if the parameter values of the two benchmarks priors
SGC( |

`m_J` |
parameter value |

`M_J` |
parameter value |

`upper.J` |
upper bound for the parameters |

`digits.J` |
specifies the desired precision of the parameter values |

`m_inf` |
parameter value |

`M_inf` |
parameter value |

`rlmc0` |
RLMC target value for the SGC( |

`rlmc1` |
RLMC target value for the SIGC( |

`mu.mean` |
mean of the normal prior for the effect mu. |

`mu.sd` |
standard deviation of the normal prior for the effect mu. |

The five heterogeneity benchmark priors used are
introduced in the Supplementary Material of Ott et al. (2021, Sections 2.2.1, 2.5 and 2.6, see also Section 3.4 of Ott et al. (2021) for Jeffreys reference prior),
where they are denoted by
SGC(`m_{inf}`

), SIGC(`M_J`

), SGC(`m_J`

), SIGC(`M_{inf}`

)
and Jeffreys.
Note that "Jeffreys" refers to Jeffreys reference prior,
which is improper, but leads to a proper posterior if
there are at least two studies in the meta-analysis data set.

If the fits induced by the SIGC(`M_J`

) and SGC(`m_J`

) benchmarks
are not needed for the analysis (such as for the posterior reference analysis
proposed in the Supplementary Material of Ott et al. (2021), see for example Table 8 for illustration),
specifying
`compute.J.bm=FALSE`

may reduce the
computation time by several minutes.

The default values for `mu.mean`

and `mu.sd`

are suitable for effects mu on the log odds (ratio) scale.

A list with two elements:
The first element named "fits" is a list containing the model fits and the
second element called "par" is a named vector giving the parameter values
of the benchmark priors.
The vector "par" has the following five components:
`m_inf`

, `M_J`

, `m_J`

, `M_inf`

and `C`

.

The element "fits" is a list of lists of class `bayesmeta`

.
This list has length `5`

+ length(`tau.prior`

)
and contains one element for each heterogeneity prior considered
(5 benchmark priors and the actual priors specified),
in the following order:

`fit.SGC.m_inf ` |
for SGC( |

`fit.SIGC.M_J ` |
for SIGC( |

`fit.SGC.m_J ` |
for SGC( |

`fit.SIGC.M_inf ` |
for SIGC( |

`fit.j ` |
for Jeffreys (improper) reference prior |

`fit.actual_1 ` |
for the first prior in the list |

`fit.actual_2 ` |
for the second prior in the list |

`...` |
... |

`fit.actual_n ` |
for the nth prior in the list |

See `bayesmeta`

in the package bayesmeta
for information on the structure of the lists of class `bayesmeta`

.

This function may take several minutes to run if the parameter `m_J`

and/or `M_J`

is not specified,
especially if the desired precision is `digits.J=2`

or even `digits.J=3`

.

If `fit_models_RA`

ends with an error or warning,
we recommend to check the values of the parameters `M_J`

, `m_J`

and `M_inf`

for the benchmark priors.
The choice of these parameters can be studied by using the functions
`M_j_sigc`

, `m_j_sgc`

and `M_inf_sigc`

.
If one of the parameters `m_inf`

, `M_J`

, `m_J`

or `M_inf`

has a value larger than 5*10^6, a warning message will be displayed,
since such large parameter values may lead to numerical problems
in the `bayesmeta`

function.

Ott, M., Plummer, M., Roos, M. (2021). Supplementary Material:
How vague is vague? How informative is informative? Reference analysis for
Bayesian meta-analysis. *Statistics in Medicine*.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.9076")}

Ott, M., Plummer, M., Roos, M. (2021). How vague is vague?
How informative is informative? Reference analysis for
Bayesian meta-analysis. *Statistics in Medicine* **40**, 4505–4521.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.9076")}

`bayesmeta`

in the package bayesmeta,
`m_j_sgc`

, `M_j_sigc`

,
`m_inf_sgc`

, `M_inf_sigc`

```
# for aurigular acupuncture (AA) data set with one
# actual half-normal and the "DuMouchel" heterogeneity prior
data(aa)
# warning: it takes ca. 7 min. to run this function
fit_models_RA_5bm(df=aa, tau.prior=list(function(t)dhalfnormal(t, scale=1),
"DuMouchel"))
# computation is much faster if m_J and M_J are specified
fit_models_RA_5bm(df=aa, tau.prior=list(function(t)dhalfnormal(t, scale=1),
"DuMouchel"),
m_J = 1.35, M_J = 1.3)
```

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