Search

Abstract Adverse events suffer from poor reporting within randomised controlled trials, despite them being crucial to the evaluation of a treatment. A recent update to the CONSORT harms checklist aims to improve reporting by providing structure and consistency to the information presented. We propose an extension wherein harms would be reported in conjunction with effectiveness outcome(s) rather than in silo to provide a more complete picture of the evidence acquired within a trial. Benefit-risk methods are designed to simultaneously consider both benefits and risks, and therefore, we believe these methods could be implemented to improve the prominence of adverse events when reporting trials. The aim of this article is to use case studies to demonstrate the practical utility of benefit-risk methods to present adverse events results alongside effectiveness results. Two randomised controlled trials have been selected as case studies, the Option-DM trial and the SANAD II trial. Using a previous review, a shortlist of 17 benefit-risk methods which could potentially be used for reporting RCTs was created. From this shortlist, three benefit-risk methods are applied across the two case studies. We selected these methods for their usefulness to achieve the aim of this paper and which are commonly used in the literature. The methods selected were the Benefit-Risk Action Team (BRAT) Framework, net clinical benefit (NCB), and the Outcome Measures in Rheumatology (OMERACT) 3 × 3 table. Results using the benefit-risk method added further context and detail to the clinical summaries made from the trials. In the case of the SANAD II trial, the clinicians concluded that despite the primary outcome being improved by the treatment, the increase in adverse events negated the improvement and the treatment was therefore not recommended. The benefit-risk methods applied to this case study outlined the data that this decision was based on in a clear and transparent way. Using benefit-risk methods to report the results of trials can increase the prominence of adverse event results by presenting them alongside the primary efficacy/effectiveness outcomes. This ensures that all the factors which would be used to determine whether a treatment would be recommended are transparent to the reader.

IntroductionOptimise:MS is an observational pharmacovigilance study aimed at characterizing the safety profile of disease-modifying therapies (DMTs) for multiple sclerosis (MS) in a real world population. The study will categorize and quantify the occurrence of serious adverse events (SAEs) in a cohort of MS patients recruited from clinical sites around the UK. The study was motivated particularly by a need to establish the safety profile of newer DMTs, but will also gather data on outcomes among treatment-eligible but untreated patients and those receiving established DMTs (interferons and glatiramer acetate). It will also explore the impact of treatment switching.MethodsCausal pathway confounding between treatment selection and outcomes, together with the variety and complexity of treatment and disease patterns observed among MS patients in the real world, present statistical challenges to be addressed in the analysis plan. We developed an approach for analysis of the Optimise:MS data that will include disproportionality-based signal detection methods adapted to the longitudinal structure of the data and a longitudinal time-series analysis of a cohort of participants receiving second-generation DMT for the first time. The time-series analyses will use a number of exposure definitions in order to identify temporal patterns, carryover effects and interactions with prior treatments. Time-dependent confounding will be allowed for via inverse-probability-of-treatment weighting (IPTW). Additional analyses will examine rates and outcomes of pregnancies and explore interactions of these with treatment type and duration.ResultsTo date 14 hospitals have joined the study and over 2,000 participants have been recruited. A statistical analysis plan has been developed and is described here.ConclusionOptimise:MS is expected to be a rich source of data on the outcomes of DMTs in real-world conditions over several years of follow-up in an inclusive sample of UK MS patients. Analysis is complicated by the influence of confounding factors including complex treatment histories and a highly variable disease course, but the statistical analysis plan includes measures to mitigate the biases such factors can introduce. It will enable us to address key questions that are beyond the reach of randomized controlled trials.

Introduction The power of ‘real world’ data to improve our understanding of the clinical aspects of multiple sclerosis (MS) is starting to be realised. Disease modifying therapy (DMT) use across the UK is driven by national prescribing guidelines. As such, the UK provides an ideal country in which to gather MS outcomes data. A rigorously conducted observational study with a focus on pharmacovigilance has the potential to provide important data to inform clinicians and patients while testing the reliability of estimates from pivotal trials when applied to patients in the UK.Methods and analysis The primary aim of this study is to characterise the incidence and compare the risk of serious adverse events in people with MS treated with DMTs. The OPTIMISE:MS database enables electronic data capture and secure data transfer. Selected clinical data, clinical histories and patient-reported outcomes are collected in a harmonised fashion across sites at the time of routine clinical visits. The first patient was recruited to the study on 24 May 2019. As of January 2021, 1615 individuals have baseline data recorded; follow-up data are being captured and will be reported in due course.Ethics and dissemination This study has ethical permission (London City and East; Ref 19/LO/0064). Potential concerns around data storage and sharing are mitigated by the separation of identifiable data from all other clinical data, and limiting access to any identifiable data. The results of this study will be disseminated via publication. Participants provide consent for anonymised data to be shared for further research use, further enhancing the value of the study.