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Record W2554151492 · doi:10.1182/blood.v108.11.343.343

Maintenance Therapy with Rituximab for Follicular Lymphoma Is Cost-Effective – A Canadian Perspective.

2006· article· en· W2554151492 on OpenAlexaffabout
Bridget Maturi, Joseph Mıkhael, W. Dunlop, Dominic T. Tilden, Lisa Wong

Bibliographic record

VenueBlood · 2006
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkRoche (Canada)
Fundersnot available
KeywordsRituximabMedicineFollicular lymphomaMaintenance therapyInternal medicineOncologyProgression-free survivalLymphomaSurgeryChemotherapy

Abstract

fetched live from OpenAlex

Abstract Background: Rituximab maintenance therapy has been shown to significantly improve overall survival (OS) (p<0.0111) and progression free survival (PFS) (p<0.0001) compared to observation alone in patients with relapsed/refractory follicular lymphoma (van Oers MHJ et al, et al. Blood. 2006 [Epub ahead of print]).The objective of this analysis was to estimate the cost-effectiveness, from a Canadian perspective, of rituximab maintenance therapy versus observation alone (OA) in relapsed/refractory follicular lymphoma patients following response to induction therapy with or without rituximab, based on data from the European Organisation for Research and Treatment of Cancer (EORTC) 20981 study (Clinical Study Report 1016350). Methods: The impact of rituximab maintenance therapy (375 mg/m2 every 3 months until progression or for 2 years) compared with OA was evaluated using a lifetime, health-state transition model. All patients entered the model following response to chemotherapy +/− rituximab as induction therapy (progression-free health state [PFHS]). The model simulates the movement of patients from PFHS to either progressed health state (PHS) or death based on the data from the study. PFS and OS following rituximab maintenance were extrapolated from 2-year Kaplan-Meier curves from the study data using a Weibull distribution. In the base case model, the PFS and OS benefits of rituximab maintenance therapy were conservatively assumed to last only 5 years. Quality of life utility values for the health states in the model were derived from a study of 165 patients using the EQ-5D questionnaire. Direct annual medical costs including drug acquisition, administration and preparation were estimated from published sources. All costs are reported in 2005 Canadian dollars (CAD). Costs and outcomes were discounted at a rate of 5%. In order to address uncertainty in point estimates, one-way sensitivity analyses were also performed. Results: From the model, the estimated life-time incremental PFS for rituximab maintenance therapy was a 1.4 year increase over OA (3.1 vs 1.7 years). OS of rituximab maintenance patients was 0.9 years longer than in OA patients (5.6 vs 4.7 years). Total cost for rituximab maintenance therapy was estimated to be CAD34,748, with the majority of costs related to drug acquisition (CAD18,652). Rituximab maintenance resulted in a gain of 0.8 Quality Adjusted Life Years (QALYs) (4.0 vs [OA] 3.2 QALYs) at an incremental cost of CAD17,136. The incremental cost effectiveness ratio (ICER) of rituximab maintenance vs OA is, therefore, estimated to be CAD20,428 per QALY gained. The ICER of rituximab maintenance was sensitive to the duration of treatment benefit and frequency of subsequent treatment. Conclusions: In patients responding to induction therapy, rituximab maintenance therapy improves overall survival and progression-free survival compared with observation alone. This pharmacoeconomic model demonstrates that maintenance therapy with rituximab is a cost-effective approach for the management of patients with follicular lymphoma.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.409

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.008
GPT teacher head0.240
Teacher spread0.232 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2006
Admission routes2
Has abstractyes

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