inconsistency_variance_prior {rnmamod} | R Documentation |
Function for the hyper-parameters of the prior distribution of the inconsistency variance (network meta-analysis with random inconsistency effects)
Description
Calculates 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.
Usage
inconsistency_variance_prior(mean_tau2, sd_tau2, mean_scale, measure)
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
|
measure |
Character string indicating the effect measure. For a binary
outcome, use only |
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)).
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.
Author(s)
Loukia M. Spineli
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