Random-effects models, which incorporate between-trial heterogeneity and thus generally give wider confidence intervals when heterogeneity is present, were used.
We used random-effects models, which incorporate between-trial heterogeneity and give wider confidence intervals when heterogeneity is present, to pool data.
Mortality data in each subgroup were pooled using random-effects models, which incorporate between-study heterogeneity (Review Manager; Cochrane Collaboration, Oxford, UK), expressed as risk ratios (RRs) with 95% confidence intervals (CIs).
All analyses were performed by using Review Manager (RevMan version 5.2; Cochrane Collaboration, Oxford, UK) and random effects models, which incorporate between-trial heterogeneity and give wider and more conservative confidence intervals (CIs) when heterogeneity is present [ 8].
We note that MSPEs using P1, which incorporates between-imputation variability from, are always higher than MSPEs using P2 = P3, which in the linear model equate to using.
The potential for defining a threshold temperature to identify unhealthy cattle based on IRT was examined using a Bayesian hierarchical model, which incorporates between-animal variation and facilitates predictions outside the data which allow for parameter uncertainty.
The summary statistics from each study can be combined using a variety of meta-analytical methods, which are classified as fixed-effect models in which studies are weighted according to the amount of information they contain, or random-effects models, which incorporate an estimate of between-study variation (heterogeneity) in the weighting.
We aggregated outcomes data at the study level and performed statistical calculations with Review Manager (RevMan) 5.0 (2009; The Cochrane Collaboration, Oxford, UK) using random-effects models [ 23], which incorporate both within-study and between-study variation and generally provide more conservative effect estimates when heterogeneity is present.
For the purposes of the present article, we focus on three themes which incorporate data suggesting clear distinctions between dyads in which patients had remained in employment and dyads in which the patient was no longer in work due to their back problem – (1) Consequences of illness; (2) Nature of work and (3) Patient identity.
In this study, we develop eight compartmental models based on types SIS (Susceptible-Infected-Susceptible), SIR (Susceptible-Infected-Recovered) and SIRS (Susceptible-Infected-Recovered-Susceptible) [ 19, 20], which incorporate different assumptions regarding the relation between seropositivity and immunity.
The proposed two-level CPG organization has been investigated with a computational model which incorporates interactions between the CPG, spinal circuits and afferent inputs.
