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Adjusted Comparisons Suggest Daratumumab Is Associated with Prolonged Survival Compared with Standard of Care Therapies in Patients with Heavily Pre-Treated and Highly Refractory Multiple Myeloma

2016· article· en· W2594482699 on OpenAlexaffabout
Shaji Kumar, Brian G.M. Durie, Zhuo T. Su, Joris Diels, Brian Hutton, Annette Lam, Tetsuro Ito

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsDaratumumabPomalidomideMedicineLenalidomideCarfilzomibInternal medicinePopulationHazard ratioMultiple myelomaOncologyProportional hazards modelPropensity score matchingProgression-free survivalConfidence intervalChemotherapy

Abstract

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Abstract Objective: To fully contextualize the benefit of novel agents such as daratumumab (DARA) monotherapy for the treatment of patients with heavily pre-treated and highly refractory multiple myeloma (MM), it is critical to understand the real-world outcomes of this patient population on current standard of care (SOC) therapies. The objective of this study was to perform adjusted comparisons to determine the comparative effectiveness of DARA monotherapy versus real-world SOC therapies. Methods: Data for patients treated with DARA 16 mg/kg monotherapy were available from clinical trials MMY2002 (n=106) and GEN501 (n=42), while patients treated with SOC therapies were derived from the International Myeloma Foundation (IMF) chart review of patients with MM who had ≥3 prior lines of therapy and were double refractory to a proteasome inhibitor (PI) and an immunomodulatory drug (IMiD) (n=543) (Kumar et al., ASH 2016; submitted). The pooled DARA studies demonstrated a median overall survival (OS) of 20.1 months versus 13.0 months for SOC based on the IMF cohort (Usmani et al., Blood 2016; Kumar et al., ASH 2016; submitted). The relative treatment effect of DARA versus SOC was estimated using two adjusted comparison methodologies, propensity score matching (PSM) and multivariate Cox regression analyses. Both methodologies utilized individual patient data to compare OS. Modeled covariates for the PSM were age, gender, prior lines of therapy, albumin, and refractory status to bortezomib (BOR), carfilzomib (CAR), lenalidomide (LEN), and pomalidomide (POM). PSM was performed using caliper matching with a caliper width 25% of the standard deviation of the logit-transformed propensity score, using sampling without replacement. For the regression analysis, the covariates included in the multivariate proportional hazards regression model were age, gender, prior lines of therapy, albumin, beta-2 microglobulin, prior exposure to POM and CAR, and PI/IMiD refractory status. Clustering of observations at the treatment-line level within patients was controlled for using the robust sandwich estimate for the covariance matrix, making confidence intervals (CIs) more conservative. For both PSM and regression, statistical significance testing was performed using a two-tailed p-value of <0.05, and all comparisons between treatment groups were reported with hazard ratios (HRs) and 95% CIs. Results: Prior to PSM, imbalances between the DARA and SOC groups were significant for prior lines of therapy and proportions of patients refractory to POM, CAR, BOR, and LEN. After PSM, the DARA and SOC groups were well balanced for all covariates included in propensity score calculations. After PSM, comparisons found significant improvement in favor of DARA relative to SOC for OS (HR=0.44 [95% CI 0.31-0.63]) (Figure 1). Regression analyses revealed consistent results. After adjustment for differences in all covariates included in regression between the DARA and SOC groups, results showed significant improvement in favor of DARA compared with SOC for OS (HR=0.43 [95% CI 0.32-0.59]) (Figure 2). Conclusions: Findings from both PSM and regression analyses were consistent and suggest that DARA is associated with significant gains in OS compared with SOC therapies for patients with heavily pre-treated and highly refractory MM. References: 1. Usmani SZ, Weiss BM, Plesner T, Bahlis NJ, Belch A et al. (2016) Clinical efficacy of daratumumab monotherapy in patients with heavily pretreated relapsed or refractory multiple myeloma. Blood 128 (1): 37-44. 2. Kumar SK, et al. (2016) Natural history of relapsed myeloma, refractory to immunomodulatory drugs and proteasome inhibitors: a multicenter IMWG study. The 58th Annual Meeting of the American Society of Hematology: submitted. Disclosures Kumar: Celgene: Consultancy, Research Funding; Noxxon: Consultancy, Honoraria; Janssen: Research Funding; AbbVie: Research Funding; BMS: Consultancy; Amgen: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Skyline: Consultancy, Honoraria. Durie:Amgen: Consultancy; Takeda: Consultancy; Janssen: Consultancy. Su:Janssen: Research Funding. Diels:Johnson & Johnson: Employment, Equity Ownership. Hutton:Essai Canada: Consultancy; Cornerstone Research Group: Consultancy; Janssen: Research Funding. Lam:Janssen: Employment. Tetsuro:Johnson & Johnson: Equity Ownership; Janssen: Employment.

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)0.016
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.253
Teacher spread0.237 · 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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Citations4
Published2016
Admission routes2
Has abstractyes

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