Bayesian network meta-analysis methods for combining individual participant data and aggregate data from single arm trials and randomised controlled trials

Abstract

Background Increasingly in network meta-analysis (NMA), there is a need to incorporate non-randomised evidence to estimate relative treatment effects, and in particular in cases with limited randomised evidence, sometimes resulting in disconnected networks of treatments. When combining different sources of data, complex NMA methods are required to address issues associated with participant selection bias, incorporating single-arm trials (SATs), and synthesising a mixture of individual participant data (IPD) and aggregate data (AD). We develop NMA methods which synthesise data from SATs and randomised controlled trials (RCTs), using a mixture of IPD and AD, for a dichotomous outcome. Methods We propose methods under both contrast-based (CB) and arm-based (AB) parametrisations, and extend the methods to allow for both within- and across-trial adjustments for covariate effects. To illustrate the methods, we use an applied example investigating the effectiveness of biologic disease-modifying anti-rheumatic drugs for rheumatoid arthritis (RA). We applied the methods to a dataset obtained from a literature review consisting of 14 RCTs and an artificial dataset consisting of IPD from two SATs and AD from 12 RCTs, where the artificial dataset was created by removing the control arms from the only two trials assessing tocilizumab in the original dataset. Results Without adjustment for covariates, the CB method with independent baseline response parameters (CBunadjInd) underestimated the effectiveness of tocilizumab when applied to the artificial dataset compared to the original dataset, albeit with significant overlap in posterior distributions for treatment effect parameters. The CB method with exchangeable baseline response parameters produced effectiveness estimates in agreement with CBunadjInd, when the predicted baseline response estimates were similar to the observed baseline response. After adjustment for RA duration, there was a reduction in across-trial heterogeneity in baseline response but little change in treatment effect estimates. Conclusions Our findings suggest incorporating SATs in NMA may be useful in some situations where a treatment is disconnected from a network of comparator treatments, due to a lack of comparative evidence, to estimate relative treatment effects. The reliability of effect estimates based on data from SATs may depend on adjustment for covariate effects, although further research is required to understand this in more detail.

Is Network Meta-analysis a Revolutionary Statistical Tool for Improving the Reliability of Clinical Trial Results? A Brief Overview and Emerging Issues Arising

“…Although the availability of such data is not always feasible, an increased IPD rate has been shown to lead to more accurate estimates for most models (140,141) and these methods need further evaluation. A typical example is the multilevel network meta-regression (ML-NMR) method as the most recent application, which in this case, is the generalization of NMA for synthesizing data from a mixture of IPD and AD studies that provide estimates for a population decision target (95,96,103,142). This use of meta-analysis, which is also the future of population adjustment, including individual studies, can be extended to areas such as prognostic models and prognostic factors that are particularly important in medical disciplines such as oncology.…”

“…In this way, NMA models are built using MCMC simulations based on our choice and using in parallel: JAGS called by the rjags R package, OpenBUGS (92) using the BRugs R package (93) or the WinBUGS (80) software called by the R2WinBUGS R package (94). multinma R package (95) that includes functions for setting up NMA and multilevel meta-regression (ML-NMR) models (96,97). More specifically a suite of tools is used for performing ML-NMR and NMA with individual patient data (IPD) (98,99), aggregate data (AD), or mixtures of both (100,101).…”

Letter to the Editor. Validity of the results of network meta-analysis

“…First, Bayesian models in NMA rely on assumptions regarding the distribution of the data. 8 Assuming the distribution of any node could alter the outcome in unpredictable ways because data on DBS for AN are highly limited. Second, we elected to use a frequentist model due to the ease of interpretation compared with a Bayesian model.…”

Acupuncture for migraine prophylaxis: A protocol for systematic review and Bayesian network meta-analysis

“…NMA allows for the estimation of heterogeneity in the effect of any given treatment and inconsistency in the evidence from different pairs of treatments. [ 41 ] We plan to conduct an NMA and compare the efficacy of different acupuncture therapies for migraine prophylaxis to obtain the potential optimal option among different treatments.…”