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Record W3183672670 · doi:10.1007/s40120-021-00264-1

How have Economic Evaluations in Relapsing Multiple Sclerosis Evolved Over Time? A Systematic Literature Review

2021· review· en· W3183672670 on OpenAlex
Anggie Wiyani, Lohit Badgujar, Vivek Khurana, Nicholas Adlard

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueNeurology and Therapy · 2021
Typereview
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
FundersNovartis Pharma
KeywordsMedicineSystematic reviewMEDLINEReimbursementHealth technologyFamily medicineCohort studyHealth carePathologyPolitical science

Abstract

fetched live from OpenAlex

INTRODUCTION: The introduction of disease-modifying therapies (DMTs) for relapsing multiple sclerosis (RMS) over the last two decades has prompted the economic assessments of these treatments by reimbursement authorities. The aim of this systematic literature review was to evaluate the modeling approach and data sources used in economic evaluations of DMTs for RMS, identify differences and similarities, and explore how economic evaluation models have evolved over time. METHODS: MEDLINE®, Embase®, and EBM Reviews databases were searched using Ovid® Platform from database inception on 25 December 2019 and subsequently updated on 17 February 2021. In addition, health technology assessment agency websites, key conference proceedings, and gray literature from relevant websites were screened. The quality of included studies was assessed using the Drummond and Philips checklists. RESULTS: A total 155 publications and 30 Health Technology Assessment (HTA) reports were included. Most of these were cost-utility analysis (73 studies and 25 HTA reports) and funded by medicines manufacturers (n = 65). The top three countries where studies were conducted were the USA (n = 29), the UK (n = 16), and Spain (n = 10). Studies predominantly used Markov cohort models (94 studies; 25 HTAs) structured based on the Expanded Disability Status Scale (EDSS) with 21 health states (20 studies; 12 HTA reports). The London Ontario and British Columbia data sets were commonly used sources for natural history data (n = 33; n = 13). Twelve studies and ten HTAs from the UK assumed a waning of DMT effect over the long term, while this was uncommon in studies from other countries. Nineteen studies adjusted for multiple sclerosis (MS)-specific mortality estimates, while 18 studies used data from the national life table without adjustment. Studies prominently referred to mortality data that were about two decades old. The data on treatment effect was generally obtained from randomized controlled trials (43 studies; 7 HTAs) or from published evidence synthesis (23 studies; 24 HTAs). Utility estimates were derived from either published studies and/or supplemented with data from RCTs. Most of the models used the lifetime horizon (n = 37) with a 1-year cycle length (n = 63). CONCLUSION: As expected, similarities as well as differences were observed across the different economic models. Available evidence suggests models should continue using the Markov cohort model with 21 EDSS-based states, however, allowing the transition to a lower EDSS state and assuming a sustained treatment effect. With reference to the data sources, models should consider using a contemporary MS-specific mortality data, recent natural history data, and country-specific utility data if available. In case of data unavailability, a sensitivity analysis using multiple sources of data should be conducted. In addition, future models should incorporate other clinically relevant outcomes, such as the cognition, vision, and psychological aspects of RMS, to be able to present the comprehensive value of DMTs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.409
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.118
GPT teacher head0.377
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it