Background: With many disease-modifying therapies currently approved for the management of multiple sclerosis, there is a growing need to evaluate the comparative effectiveness and safety of those therapies from real-world data sources. Propensity score methods have recently gained popularity in multiple sclerosis research to generate real-world evidence. Recent evidence suggests, however, that the conduct and reporting of propensity score analyses are often suboptimal in multiple sclerosis studies.

Objectives: To provide practical guidance to clinicians and researchers on the use of propensity score methods within the context of multiple sclerosis research.

Methods: We summarize recommendations on the use of propensity score matching and weighting based on the current methodological literature, and provide examples of good practice.

Results: Step-by-step recommendations are presented, starting with covariate selection and propensity score estimation, followed by guidance on the assessment of covariate balance and implementation of propensity score matching and weighting. Finally, we focus on treatment effect estimation and sensitivity analyses.

Conclusion: This comprehensive set of recommendations highlights key elements that require careful attention when using propensity score methods.

Background: Modelling non-linear associations between an outcome and continuous patient characteristics, whilst investigating heterogeneous treatment effects, is one of the opportunities offered by individual participant data meta-analysis (IPD-MA). Splines offer great flexibility, but guidance is lacking.

Objective: To introduce modelling of nonlinear associations using restricted cubic splines (RCS), natural B-splines, P-splines, and smoothing splines in IPD-MA to estimate absolute treatment effects.

Methods: We describe the pooling of spline-based models using pointwise and multivariate meta-analysis (two-stage methods) and one-stage generalised additive mixed effects models (GAMMs). We illustrate their performance on three IPD-MA scenarios of five studies each: one where only the associations differ across studies, one where only the ranges of the effect modifier differ and one where both differ. We also evaluated the approaches in an empirical example, modelling the risk of fever and/or ear pain in children with acute otitis media conditional on age.

Results: In the first scenario, all pooling methods showed similar results. In the second and third scenario, pointwise meta-analysis was flexible but showed non-smooth results and wide confidence intervals; multivariate meta-analysis failed to converge with RCS, but was efficient with natural B-splines. GAMMs produced smooth pooled regression curves in all settings. In the empirical example, results were similar to the second and third scenario, except for multivariate meta-analysis with RCS, which now converged.

Conclusion: We provide guidance on the use of splines in IPD-MA, to capture heterogeneous treatment effects in presence of non-linear associations, thereby facilitating estimation of absolute treatment effects to enhance personalized healthcare.

Background: The objective of this systematic review is to investigate the use of Bayesian data analysis in epidemiology in the past decade, and particularly to evaluate the quality of research papers reporting the results of these analyses.

Methods: Complete volumes of five major epidemiological journals in the period 2005-2015 were searched via Pubmed. In addition we performed an extensive within-manuscript search using a specialized Java application. Details of reporting on Bayesian statistics were examined in original research papers with primary Bayesian data analyses.

Results: The number of studies in which Bayesian techniques were used for primary data analysis remain constant over the years. Though many authors presented thorough descriptions of the analyses they performed and the results they obtained, several reports presented incomplete method sections, and even some incomplete results sections. Especially, information on the process of prior elicitation, specification and evaluation was often lacking.

Conclusions: Though available guidance papers concerned with reporting of Bayesian analyses emphasize the importance of transparent prior specification, the results obtained in this systematic review show that these guidance papers are often not used. Additional efforts should be made to increase the awareness of the existence and importance of these checklists in order to overcome the controversy with respect to the use of Bayesian techniques. The reporting quality in epidemiological literature could be improved by updating existing guidelines on the reporting of frequentist analyses to address issues that are important for Bayesian data analyses.