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Challenges and opportunities of real-world data: Statistical analysis plan for the Optimise: MS multicentre prospective cohort pharmacovigilance study

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Title: Challenges and opportunities of real-world data: Statistical analysis plan for the Optimise: MS multicentre prospective cohort pharmacovigilance study
Authors: Waddingham, E
Miller, A
Dobson, R
Matthews, P
Item Type: Journal Article
Abstract: Introduction: Optimise:MS is an observational pharmacovigilance study aimed at characterising the safety profile of disease-modifying therapies (DMTs) for multiple sclerosis (MS) in a real world population. The study will categorise 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. Methods: Causal 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. Results: To date 13 hospitals have joined the study and over 2000 participants have been recruited. A statistical analysis plan has been developed and is described here. Conclusion: Optimise: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
Date of Acceptance: 3-Mar-2022
URI: http://hdl.handle.net/10044/1/95967
DOI: 10.3389/fneur.2022.799531
ISSN: 1664-2295
Publisher: Frontiers Media
Journal / Book Title: Frontiers in Neurology
Volume: 13
Copyright Statement: © 2022 Waddingham, Miller, Dobson and Matthews. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Sponsor/Funder: Biogen International GmbH
Merck Serono Ltd
Celgene Europe Ltd
Funder's Grant Number: PO No: 28827
PO: 5485264
Keywords: cohort study
multiple sclerosis
real-world data
signal detection
statistical analysis plan
1103 Clinical Sciences
1109 Neurosciences
1701 Psychology
Publication Status: Published
Appears in Collections:Faculty of Medicine
Department of Brain Sciences

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