Determine the prior distribution for the heterogeneity parameter
Source:R/heterogeneity.param.prior_function.R
heterogeneity_param_prior.RdGenerates the prior distribution (weakly informative or empirically-based)
for the heterogeneity parameter.
run_model inherits heterogeneity_param_prior via the
argument heter_prior.
Arguments
- measure
Character string indicating the effect measure. For a binary outcome, the following can be considered:
"OR","RR"or"RD"for the odds ratio, relative risk, and risk difference, respectively. For a continuous outcome, the following can be considered:"MD","SMD", or"ROM"for mean difference, standardised mean difference and ratio of means, respectively.- model
Character string indicating the analysis model with values
"RE", or"FE"for the random-effects and fixed-effect model, respectively. The default argument is"RE".- heter_prior
A list of three elements with the following order: 1) a character string indicating the distribution with (currently available) values
"halfnormal","uniform","lognormal", or"logt"; 2) two numeric values that refer to the parameters of the selected distribution. For"lognormal", and"logt"these numbers refer to the mean and precision, respectively. For"halfnormal", these numbers refer to zero and the scale parameter (equal to 4 or 1 being the corresponding precision of the scale parameter 4 or 1). For"uniform", these numbers refer to the minimum and maximum value of the distribution.
Value
A value to be passed to run_model.
Details
The names of the (current) prior distributions follow the JAGS syntax.
The mean and precision of "lognormal" and "logt" should align
with the values proposed by Turner et al. (2015) and Rhodes et al. (2015)
for the corresponding empirically-based prior distributions when
measure is "OR" or "SMD", respectively.
The users may refer to Dias et al. (2013) to determine the minimum and
maximum value of the uniform distribution, and to Friede et al. (2017)
to determine the mean and precision of the half-normal distribution.
When model is "FE", heterogeneity_param_prior
is ignored in run_model.
References
Dias S, Sutton AJ, Ades AE, Welton NJ. Evidence synthesis for decision making 2: a generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Med Decis Making 2013;33(5):607–17. doi: 10.1177/0272989X12458724
Friede T, Roever C, Wandel S, Neuenschwander B. Meta-analysis of two studies in the presence of heterogeneity with applications in rare diseases. Biom J 2017;59(4):658–71. doi: 10.1002/bimj.201500236
Rhodes KM, Turner RM, Higgins JP. Predictive distributions were developed for the extent of heterogeneity in meta-analyses of continuous outcome data. J Clin Epidemiol 2015;68(1):52–60. doi: 10.1016/j.jclinepi.2014.08.012
Turner RM, Jackson D, Wei Y, Thompson SG, Higgins JP. Predictive distributions for between-study heterogeneity and simple methods for their application in Bayesian meta-analysis. Stat Med 2015;34(6):984–98. doi: 10.1002/sim.6381