Function for the hyper-parameters of the prior distribution of the inconsistency variance (network meta-analysis with random inconsistency effects)
Source:R/inconsistency.variance.prior_function.R
inconsistency_variance_prior.RdCalculates the mean and standard deviation of the log-normal distribution and location-scale t-distribution of the inconsistency variance in the log-odds ratio and standardised mean difference scales, respectively, based on corresponding empirical distributions for the between-study variance proposed by Turner et al. (2015) and Rhodes et al. (2015). It also return the median value of the inconsistency standard deviation.
Arguments
- mean_tau2
Mean value from the empirical prior distribution for the between-study variance.
- sd_tau2
Standard deviation value from the empirical prior distribution for the between-study variance.
- mean_scale
Positive (non-zero value) as a scaling factor of
mean_tau2. See Law et al. (2016).- measure
Character string indicating the effect measure. For a binary outcome, use only
"OR"for the odds ratio. For a continuous outcome, use only"SMD"for standardised mean difference.
Value
A list of three elements: the mean and standard deviation for the prior distribution for the inconsistency variance, and the median inconsistency standard deviation according to the selected empirical prior distribution for the between-study variance.
Details
Law et al. (2016) suggested using the proposed empirical prior distributions for between-study variance to construct a prior distribution for the inconsistency variance. The authors provided the formulas for the hyper-parameters of the inconsistency variance for a binary outcome measured in the log odds ratio scale. We extended the idea for a continuous outcome measured in the standardised mean difference scale. Currently, the empirical prior distributions for the between-study variance have been proposed for these effect measures only (Turner et al. (2015), Rhodes et al. (2015)).
References
Law M, Jackson D, Turner R, Rhodes K, Viechtbauer W. Two new methods to fit models for network meta-analysis with random inconsistency effects. BMC Med Res Methodol 2016;16:87. doi: 10.1186/s12874-016-0184-5
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