Methods for the inclusion of real-world evidence in network meta-analysis

Background Network Meta-Analysis (NMA) is a key component of submissions to reimbursement agencies world-wide, especially when there is limited direct head-to-head evidence for multiple technologies from randomised controlled trials (RCTs). Many NMAs include only data from RCTs. However, real-world evidence (RWE) is also becoming widely recognised as a valuable source of clinical data. This study aims to investigate methods for the inclusion of RWE in NMA and its impact on the level of uncertainty around the effectiveness estimates, with particular interest in effectiveness of fingolimod. Methods A range of methods for inclusion of RWE in evidence synthesis were investigated by applying them to an illustrative example in relapsing remitting multiple sclerosis (RRMS). A literature search to identify RCTs and RWE evaluating treatments in RRMS was conducted. To assess the impact of inclusion of RWE on the effectiveness estimates, Bayesian hierarchical and adapted power prior models were applied. The effect of the inclusion of RWE was investigated by varying the degree of down weighting of this part of evidence by the use of a power prior. Results Whilst the inclusion of the RWE led to an increase in the level of uncertainty surrounding effect estimates in this example, this depended on the method of inclusion adopted for the RWE. ‘Power prior’ NMA model resulted in stable effect estimates for fingolimod yet increasing the width of the credible intervals with increasing weight given to RWE data. The hierarchical NMA models were effective in allowing for heterogeneity between study designs, however, this also increased the level of uncertainty. Conclusion The ‘power prior’ method for the inclusion of RWE in NMAs indicates that the degree to which RWE is taken into account can have a significant impact on the overall level of uncertainty. The hierarchical modelling approach further allowed for accommodating differences between study types. Consequently, further work investigating both empirical evidence for biases associated with individual RWE studies and methods of elicitation from experts on the extent of such biases is warranted.

Reward and expectancy effects on neural signals of motor preparation and execution

The prospect of rewards can have strong modulatory effects on response preparation. Importantly, selection and execution of movements in real life happens under an environment characterized by uncertainty and dynamic changes. The current study investigated how the brain's motor system adapts to the dynamic changes in the environment in pursuit of rewards. In addition, we studied how the prefrontal cognitive control system contributes in this adaptive control of motor behavior. To this end, we tested the effect of rewards and expectancy on the hallmark neural signals that reflect activity in motor and prefrontal systems, the lateralized readiness potential (LRP) and the mediofrontal (mPFC) theta oscillations, while participants performed an expected and unexpected action to retrieve rewards. To better capture the dynamic changes in neural processes represented in the LRP waveform, we decomposed the LRP into the preparation (LRPprep) and execution (LRPexec) components. The overall pattern of LRPprep and LRPexec confirmed that they each reflect motor preparation based on the expectancy and motor execution when making a response that is either or not in line with the expectations. In the comparison of LRP magnitude across task conditions, we found a greater LRPprep when large rewards were more likely, reflecting a greater motor preparation to obtain larger rewards. We also found a greater LRPexec when large rewards were presented unexpectedly, suggesting a greater motor effort placed for executing a correct movement when presented with large rewards. In the analysis of mPFC theta, we found a greater theta power prior to performing an unexpected than expected response, indicating its contribution in response conflict resolution. Collectively, these results demonstrate an optimized motor control to maximize rewards under the dynamic changes of real-life environment.

A Similarity-Weighted Informative Prior Distribution for Bayesian Multiple Regression Models

Specifying accurate informative prior distributions is a question of carefully selecting studies that comprise the body of comparable background knowledge. Psychological research, however, consists of studies that are being conducted under different circumstances, with different samples and varying instruments. Thus, results of previous studies are heterogeneous, and not all available results can and should contribute equally to an informative prior distribution. This implies a necessary weighting of background information based on the similarity of the previous studies to the focal study at hand. Current approaches to account for heterogeneity by weighting informative prior distributions, such as the power prior and the meta-analytic predictive prior are either not easily accessible or incomplete. To complicate matters further, in the context of Bayesian multiple regression models there are no methods available for quantifying the similarity of a given body of background knowledge to the focal study at hand. Consequently, the purpose of this study is threefold. We first present a novel method to combine the aforementioned sources of heterogeneity in the similarity measure ω. This method is based on a combination of a propensity-score approach to assess the similarity of samples with random- and mixed-effects meta-analytic models to quantify the heterogeneity in outcomes and study characteristics. Second, we show how to use the similarity measure ωas a weight for informative prior distributions for the substantial parameters (regression coefficients) in Bayesian multiple regression models. Third, we investigate the performance and the behavior of the similarity-weighted informative prior distribution in a comprehensive simulation study, where it is compared to the normalized power prior and the meta-analytic predictive prior. The similarity measure ω and the similarity-weighted informative prior distribution as the primary results of this study provide applied researchers with means to specify accurate informative prior distributions.