To inform our subgroup analyses selleck based on risk of bias we will, if we detect variability within the individual risk of bias components, perform subgroup analyses on a component-by-component basis. We will perform meta-regression and subgroup analyses to explore these hypotheses, and interpret the results in the context of the GRADE system (see below).74 Confidence in the estimates of
effect We will use the GRADE approach to evaluate confidence in effect estimates for all reported outcomes.75 GRADE has been adopted by over 70 organisations worldwide, and this approach facilitates transparent, rigorous and comprehensive assessment of evidence quality on a per outcome basis.76–89 Our review of the management of chronic
neuropathic pain will be the first to use the GRADE criteria to evaluate confidence in effect estimates. We will categorise the confidence in estimates (quality of evidence) as high, moderate, low or very low. Using this approach, randomised trials begin as high quality evidence but may be rated down by one or more of four categories of limitations. We will use GRADE guidance to determine whether to rate down confidence in the body of evidence for (1) risk of bias87 and for (2) imprecision,81 inconsistency83 and publication bias.84 For the risk of bias assessment, for any comparisons that suggest a statistically significant treatment effect, we will use recently developed approaches to address missing participant data for dichotomous outcomes and continuous outcomes.90 91 When plausible worst case scenarios reverse the treatment effect we will rate down for risk of bias. We will present the results of our meta-analyses in GRADE evidence
profiles that will provide a succinct, easily digestible presentation of the risk of bias and magnitude of effects.75 Multiple treatment comparison meta-analyses To assess relative effects of competing treatments, we will construct a Cilengitide random effects model within the Bayesian framework using Markov chain Monte Carlo methods.92 We will use trace plots and calculate the Gelman-Rubin statistic to assess model convergence. We will model patient-important outcomes in every treatment group of every study, and specify the relations among the effect sizes across studies.93 This method combines direct and indirect evidence for any given pair of treatments.