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Nilotinib Vs Dasatinib As Second-Line Therapy in Patients with Philadelphia-Positive Chronic Myeloid Leukemia in Chronic Phase (Ph+ CML-CP) Who Are Resistant or Intolerant to Imatinib: A Cost-Effectiveness Analysis (CEA) Based on Real-World Data

2015· article· en· W2547879229 on OpenAlexaboutno aff
Nanxin Li, Chelsey Yang, Liangyi Fan, Todor Totev, Annie Guérin, Lei Chen

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsNilotinibDasatinibMedicineDiscontinuationInternal medicineImatinibImatinib mesylateOncologyMyeloid leukemia

Abstract

fetched live from OpenAlex

Abstract Background: Nilotinib and dasatinib are common second-line therapy for patients with Ph+ CML-CP who are resistant or intolerant to imatinib. Since their approval, several CEAs comparing second-line nilotinib vs dasatinib have been published (Rogers et al., Health Technol Assess 2012; 16(22); Loveman et al., Health Technol Assess 2012; 16(23)). However, these models were developed in the absence of any comparative effectiveness evidence between the two drugs. Another major limitation of these models as recognized by a leading health technology assessment (HTA) agency was the use of surrogate markers to impute survival endpoints (e.g., progression-free survival [PFS] and overall survival [OS]). As direct comparative evidence of the two drugs based on real-world data become available, this study was conducted to address these limitations and re-evaluate the cost-effectiveness of second-line nilotinib vs dasatinib for Ph+ CML-CP using real-world comparative PFS and OS data from a third-party payer perspective in the U.S. Methods: A lifetime partitioned survival model was developed to compare healthcare costs, life years (LYs) and quality-adjusted life years (QALYs) associated with second-line therapy with nilotinib vs dasatinib in patients with Ph+ CML-CP who were resistant or intolerant to first-line imatinib. The model included four health states: CP on second-line treatment (CP on treatment), CP post-discontinuation of second-line treatment (CP post-discontinuation), accelerated phase or blast crisis (progressive disease), and death. Patients can only transition into a subsequent health state but not in the other direction; patients in the first three health states can all transition to death. Time on treatment (TOT), PFS, and OS for second-line nilotinib and dasatinib were estimated using data from a real-world comparative effectiveness study (Griffin et al., Curr Med Res Opin 2013; 29(6):623-31). Parametric survival models were used to extrapolate outcomes beyond the study period. Drug treatment costs, medical costs, and adverse event (AE) costs were obtained from literature and publicly available databases. Utilities for health states were derived from literature. Costs, LYs, and QALYs were discounted at 3% per annum. Incremental cost-effectiveness ratios (ICERs), including incremental cost per LY gained and incremental cost per QALY gained, were estimated comparing nilotinib vs dasatinib. Deterministic sensitivity analyses (DSAs) were performed by varying starting age, sex-ratio, adherence level of second-line therapies, drug treatment costs for post-second-line states, medical costs for all health states, AE costs, and utility for CP post-discontinuation. Results: Over life time, initiating second-line treatment with nilotinib was associated with 11.69 LYs, 9.13 QALYs, and total costs of $1,406,265; initiating second-line with dasatinib was associated with 9.51 LYs, 7.30 QALYs, and total costs of $1,418,235. Second-line nilotinib was associated with better health outcomes (difference in LY = 2.18 years, difference in QALY = 1.84 years) and lower costs (difference in total cost = $11,970) relative to dasatinib. DSA results similarly showed better outcomes and lower costs for nilotinib vs dasatinib based on variations of sex-ratio, progressive disease treatment costs, medical costs for all health states, AE costs, and utility for CP post-discontinuation; DSA results also showed better outcomes but higher costs for nilotinib vs dasatinib based on variations of starting age, adherence to second-line therapies, and CP post-discontinuation treatment cost with ICERs of $10,738/QALY, $2,648/QALY, and $2,318/QALY, respectively.Table 1.Base Case ResultsNilotinibDasatinibNilotinib vs DasatinibCosts (2015 USD)Treatment costs$1,235,535$1,253,081-$17,546Medical costs$162,326$156,762$5,564AE costs$8,404$8,392$11Total costs$1,406,265$1,418,235-$11,970EffectivenessTotal LYs11.699.512.18Total QALYs9.137.301.84ICERIncremental cost per LY gainedBetter LY with lower costsIncremental cost per QALY gainedBetter QALY with lower costs Conclusions: The CEA based on real-world comparative evidence suggests that second-line nilotinib is associated with better life expectancy, quality of life, and lower cost when compared with dasatinib, among patients with Ph+ CML-CP who are resistant or intolerant to imatinib. Disclosures Li: Novartis Pharmaceuticals Corporation: Consultancy, Other: Nanxin Li is an employee of Analysis Group Inc, which has received consultancy fees from Novartis Pharmaceuticals Corporation; AbbVie Inc., Astellas Pharma US Inc., Bayer Healthcare Pharmaceuticals LLC, Bristol-Myers Squibb Company, Celgene Corporation, Forest Laboratories Inc., Sanofi: Consultancy, Other: Nanxin Li is an employee of Analysis Group Inc, which has received consultancy fees from the listed organizations. Yang:Astellas Pharma US, Inc.: Consultancy, Other: Chelsey Yang is an employee of Analysis Group Inc, which has received consultancy fees from Astellas Pharma US, Inc.; Bristol-Myers Squibb Company: Consultancy, Other: Chelsey Yang is an employee of Analysis Group Inc, which has received consultancy fees from Bristol-Myers Squibb Company; Forest Laboratories, Inc.: Consultancy, Other: Chelsey Yang is an employee of Analysis Group Inc, which has received consultancy fees from Forest Laboratories, Inc.; Sanofi: Consultancy, Other: Chelsey Yang is an employee of Analysis Group Inc, which has received consultancy fees from Sanofi; AbbVie Inc.: Consultancy, Other: Chelsey Yang is an employee of Analysis Group Inc, which has received consultancy fees from Abbvie Inc.; Shire Pharmaceuticals Inc.: Consultancy, Other: Chelsey Yang is an employee of Analysis Group Inc, which has received consultancy fees from Shire Pharmaceuticals Inc.; GE Healthcare: Consultancy, Other: Chelsey Yang is an employee of Analysis Group Inc, which has received consultancy fees from GE Healthcare; Millennium Pharmaceuticals, Inc.: Other: Chelsey Yang is an employee of Analysis Group Inc, which has received consultancy fees from Millennium Pharmaceuticals, Inc.; Novartis Pharmaceuticals Corporation: Consultancy, Other: Chelsey Yang is an employee of Analysis Group Inc, which has received consultancy fees from Novartis Pharmaceuticals Corporation. Fan:Novartis Pharmaceuticals Corporation: Consultancy, Other: Liangyi Fan is an employee of Analysis Group Inc, which has received consultancy fees from Novartis Pharmaceuticals Corporation; AbbVie Inc, Astellas Pharma US Inc, Bristol-Myers Squibb Co, Ethicon Inc, Forest Labs Inc, GE Healthcare, Genentech Inc, MedImmune LLC, Millennium Pharmaceuticals Inc, Sanofi, Shire Pharmaceuticals Inc, Takeda Pharmaceuticals Inc, Teva Specialty Brands: Consultancy, Other: Liangyi Fan is an employee of Analysis Group Inc, which has received consultancy fees from the listed organizations. Totev:Novartis Pharmaceuticals Corporation: Consultancy, Other: Todor Totev is an employee of Analysis Group Inc, which has received consultancy fees from Novartis Pharmaceuticals Corporation; AbbVie Inc., Astellas Pharma US Inc., Bayer Healthcare Pharmaceuticals LLC, Biogen Idec Inc., Boehringer Ingelheim, Bristol-Myers Squibb Company, Cleveland HeartLab Inc., Eli Lilly & Company, Forest Laboratories Inc., Gilead Sciences Inc., GlaxoSmithKline: Consultancy, Other: Todor Totev is an employee of Analysis Group Inc, which has received consultancy fees from the listed organizations; Janssen Scientific Affairs LLC, Sanofi, Shire Pharmaceuticals Inc, UCB Inc, Vertex Pharmaceuticals Inc: Consultancy, Other: Todor Totev is an employee of Analysis Group Inc, which has received consultancy fees from the listed organizations. Guerin:Pfizer Canada, Inc.,RX&D, Sanofi, Savient Pharmaceuticals, Inc., Shire Pharmaceuticals Inc., Sunovion Pharmaceuticals Inc.,Takeda Global Research & Development Center, Inc., Takeda Pharmaceuticals U.S.A., Inc.: Consultancy, Other: Annie Guerin is an employee of Analysis Group Inc, which has received consultancy fees from the listed organizations; GlaxoSmithKline, Janssen Scientific Affairs, Janssen-Ortho, Inc., Merck & Co., Inc., Merck Frosst Canada, Novartis Pharmaceuticals Corporation, Novo Nordisk Inc., Ogilvy Renault, Ortho-Clinical Diagnostics, Inc., Otsuka America Pharmaceutical, Inc.,: Consultancy, Other: Annie Guerin is an employee of Analysis Group Inc, which has received consultancy fees from the listed organizations; AbbVie Inc., Alcon Laboratories, Bayer Healthcare Pharmaceuticals, LLC, Celgene Corporation, Cempra Inc., Centocor Ortho Biotech, Cooley LLP, Cyberonics, Inc., DLA Piper, Eli Lilly & Company, Forest Laboratories, Inc., Genentech, Inc.,: Consultancy, Other: Annie Guerin is an employee of Analysis Group Inc, which has received consultancy fees from the listed organizations. Chen:Novartis Pharmaceuticals Corporation: Employment, Equity Ownership, Other: Lei Chen is an employee of and owns stocks/options of Novartis Pharmaceuticals Corporation, the sponsor of this study.

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)0.007
metaresearch head score (Gemma)0.009
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 score0.008
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.006
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.056
GPT teacher head0.348
Teacher spread0.292 · 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".

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Citations2
Published2015
Admission routes1
Has abstractyes

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