ra4bayesmeta (version 1.0-8)

pri_RA_fits: Prior reference analysis based on bayesmeta fits

Description

Computes a table of Hellinger distance estimates between the benchmark heterogeneity priors inducing the specified model fits in fits.bm and the actual heterogenity priors inducing the specified actual model fits in fits.actual. All fits should be based on the same data set.

Usage

pri_RA_fits(fits.actual, fits.bm)

Value

A matrix of Hellinger distance estimates between actual and benchmark heterogeneity priors with \(n.bm\) columns and \(n.act\) rows, where \(n.bm\)=length(fit.bms) is the number of benchmark fits specified and \(n.act\)=length(fit.actual) the number of actual fits specified.

The columns of the matrix give the following Hellinger distance estimates between two heterogeneity priors (from left to right):

H(pri_bm_1, pri_act):

first benchmark prior bm_1 inducing the fit fits.bm[[1]] and actual prior

H(pri_bm_2, pri_act):

second benchmark prior bm_2 inducing the fit fits.bm[[2]] and actual prior

...

...

H(pri_bm_n.bm, pri_act):

last benchmark prior bm_n.bm inducing the fit fits.bm[[n.bm]] and actual prior

Each row correspond to one actual heterogeneity prior inducing one of the fits in fits.actual, in the same order as in that list. Thus, the row names are:

pri_act_1:

first actual prior in tau.prior

pri_act_2:

second actual prior in tau.prior

...

...

pri_act_n:

nth (last) actual prior in tau.prior

Arguments

fits.actual

a list of model fits of class bayesmeta, computed with the bayesmeta function in the package bayesmeta.

fits.bm

a list of model fits of class bayesmeta, computed with the bayesmeta function in the package bayesmeta. To be used as benchmarks.

Details

Two alternative suggestions for benchmark priors are provided in Ott et al. (2021, Section 3.4) and its Supplementary Material (Sections 2.5 and 2.6). These benchmark priors and the corresponding fits can be computed using the functions fit_models_RA and fit_models_RA_5bm, respectively.

References

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. 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. tools:::Rd_expr_doi("10.1002/sim.9076")

See Also

bayesmeta in the package bayesmeta, fit_models_RA_5bm, post_RA_fits, pri_RA_5bm

Examples

# for aurigular acupuncture (AA) data set
data(aa)
# compute the model fits  % function with long running time
# two actual half-normal heterogeneity priors
# warning: it takes ca. 7 min. to run this function
fits <- fit_models_RA_5bm(df=aa, tau.prior=
                                 list(function(t)dhalfnormal(t, scale=0.5),
                                      function(t)dhalfnormal(t, scale=1)))[[1]]
# benchmark fits under SGC(m_inf), SIGC(M_J), SGC(m_J) and SIGC(M_inf) priors
fits.bm.pri <- fits[1:4]
# actual fits under HN priors
fits.actual <- fits[c(6,7)]
# prior reference analysis
pri_RA_fits(fits.actual=fits.actual, fits.bm=fits.bm.pri)