This function performs a univariate meta-analysis by assuming fixed or random effects. Whereas the fixed effects model assumes that all studies in the analysis share a common effect size, the random-effects model allows different study-specific effect sizes. Concretely, if we move from fixed-effect weights to random-effects weights, large studies lose influence and small studies gain influence (Borenstein 2010).
Provides the summary estimtes of the alternative model for bivariate random-effects meta-analysis by Riley et al. (2008) with their corresponding confidence intervals. The model parameters are given as beta1, beta2, psi1, psi2 and rho. Confidence intervals are derived from the inverse Hessian.
rileyES
(Package: metamisc) :
Fit the alternative model for bivariate random-effects meta-analysis (Riley)
This function fits the alternative model for bivariate random-effects meta-analysis on effect size data when the within-study correlations are unknown. This bivariate model was proposed by Riley et al. (2008) and is similar to the bivariate random-effects model from Reitsma et al. (2005), but includes an overall correlation parameter rather than separating the (usually unknown) within- and between-study correlation. As a consequence, the alternative model is not fully hierarchical, and estimates of additional variation beyond sampling error (psi) are not directly equivalent to the between-study variation (tau) from the general model. Furthermore, it has been argumented that assuming zero within-study correlations (i.e. applying Reitsma's approach) is reasonable when summarizing the sensitivities and false positive rates of a diagnostic test (Reitsma et al. 2005, Daniels and Hughes 1997, Korn et al. 2005, Thompson et al. 2005, Van Houwelingen et al. 2002). The alternative model for bivariate random-effects meta-analysis may, however, be useful when there is large within-study variability, few primary studies are available or the general model estimates the between-study correlation as 1 or -1.
rileyDA
(Package: metamisc) :
Fit the alternative model for bivariate random-effects meta-analysis (Riley)
This function fits the alternative model for bivariate random-effects meta-analysis on diagnostic test accuracy data when the within-study correlations are unknown assumed to be different from zero. A transformation is applied to the sensitivities ans false positive rates of each study, in order to meet the normality assumptions of the model.
riley
(Package: metamisc) :
Fit the alternative model for bivariate random-effects meta-analysis (Riley)
This function fits the alternative model for bivariate random-effects meta-analysis when the within-study correlations are unknown. This bivariate model was proposed by Riley et al. (2008) and is similar to the general bivariate random-effects model (van Houwelingen et al. 2002), but includes an overall correlation parameter rather than separating the (usually unknown) within- and between-study correlation. As a consequence, the alternative model is not fully hierarchical, and estimates of additional variation beyond sampling error (psi) are not directly equivalent to the between-study variation (tau) from the general model. This model is particularly useful when there is large within-study variability, few primary studies are available or the general model estimates the between-study correlation as 1 or -1. Although the model can also be used for diagnostic test accuracy data when substantial within-study correlations are expected, assuming zero within-study correlations (i.e. applying Reitsma's approach) is usually justified (Reitsma et al. 2005, Daniels and Hughes 1997, Korn et al. 2005, Thompson et al. 2005, Van Houwelingen et al. 2002).
Calculates a prediction interval for the summary parameters of Riley's alternative model for bivariate random-effects meta-analysis. This interval predicts in what range future observations will fall given what has already been observed.