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Journal articles on the topic 'Chaînes de Markov triplet'

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Published: 1 June 2024

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1

Pieczynski, Wojciech. "Copules gaussiennes dans les chaînes triplet partiellement de Markov." Comptes Rendus Mathematique 341, no. 3 (August 2005): 189–94. http://dx.doi.org/10.1016/j.crma.2005.06.012.

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2

Pieczynski, Wojciech. "Fusion de Dempster–Shafer dans les chaînes triplet partiellement de Markov." Comptes Rendus Mathematique 339, no. 11 (December 2004): 797–802. http://dx.doi.org/10.1016/j.crma.2004.10.013.

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3

Delmotte, Thierry. "Estimations pour les chaînes de Markov réversibles." Comptes Rendus de l'Académie des Sciences - Series I - Mathematics 324, no. 9 (May 1997): 1053–58. http://dx.doi.org/10.1016/s0764-4442(97)87885-8.

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4

Brossard, Jean, and Christophe Leuridan. "Chaînes de Markov Constructives Indexées par Z." Annals of Probability 35, no. 2 (March 2007): 715–31. http://dx.doi.org/10.1214/009117906000000430.

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5

El Yazid Boudaren, Mohamed, Emmanuel Monfrini, Wojciech Pieczynski, and Amar Aissani. "Phasic Triplet Markov Chains." IEEE Transactions on Pattern Analysis and Machine Intelligence 36, no. 11 (November 1, 2014): 2310–16. http://dx.doi.org/10.1109/tpami.2014.2327974.

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6

Pieczynski, Wojciech. "Chaı̂nes de Markov Triplet." Comptes Rendus Mathematique 335, no. 3 (January 2002): 275–78. http://dx.doi.org/10.1016/s1631-073x(02)02462-7.

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7

Courbot, Jean-Baptiste, Emmanuel Monfrini, Vincent Mazet, and Christophe Collet. "Oriented Triplet Markov Fields." Pattern Recognition Letters 103 (February 2018): 16–22. http://dx.doi.org/10.1016/j.patrec.2017.12.026.

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8

Ladet, Sylvie, Marc Deconchat, Claude Monteil, Jean-Paul Lacombe, and Gérard Ballent. "Les chaînes de Markov spatialisées comme outil de simulation." Revue internationale de géomatique 15, no. 2 (June 30, 2005): 159–73. http://dx.doi.org/10.3166/rig.15.159-173.

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9

Ait-El-Fquih, B., and F. Desbouvries. "Kalman Filtering in Triplet Markov Chains." IEEE Transactions on Signal Processing 54, no. 8 (August 2006): 2957–63. http://dx.doi.org/10.1109/tsp.2006.877651.

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10

Miclo, Laurent. "Une variante de l'inégalité de Cheeger pour les chaînes de Markov finies." ESAIM: Probability and Statistics 2 (1998): 1–21. http://dx.doi.org/10.1051/ps:1998101.

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11

Diaconis, P. "Une nouvelle construction de champs Gaussiens à partir de chaînes de Markov." Annales de l'Institut Henri Poincare (B) Probability and Statistics 38, no. 6 (December 2002): 863–78. http://dx.doi.org/10.1016/s0246-0203(02)01123-8.

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12

Miclo, Laurent. "Relations entre isopérimétrie et trou spectral pour les chaînes de Markov finies." Probability Theory and Related Fields 114, no. 4 (July 1999): 431–85. http://dx.doi.org/10.1007/s004400050231.

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13

Benboudjema, Dalila, and Wojciech Pieczynski. "Unsupervised image segmentation using triplet Markov fields." Computer Vision and Image Understanding 99, no. 3 (September 2005): 476–98. http://dx.doi.org/10.1016/j.cviu.2005.04.003.

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14

HERVE, L. "Théorème local pour chaînes de Markov de probabilité de transition quasi-compacte. Applications aux chaînes V-géométriquement ergodiques et aux modèles itératifs." Annales de l'Institut Henri Poincare (B) Probability and Statistics 41, no. 2 (March 2005): 179–96. http://dx.doi.org/10.1016/j.anihpb.2004.04.001.

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15

Desbouvries, François, and Wojciech Pieczynski. "Modèles de Markov Triplet et filtrage de Kalman." Comptes Rendus Mathematique 336, no. 8 (April 2003): 667–70. http://dx.doi.org/10.1016/s1631-073x(03)00152-3.

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16

Pieczynski, Wojciech. "Multisensor triplet Markov chains and theory of evidence." International Journal of Approximate Reasoning 45, no. 1 (May 2007): 1–16. http://dx.doi.org/10.1016/j.ijar.2006.05.001.

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17

Pieczynski, Wojciech, and Dalila Benboudjema. "Multisensor triplet Markov fields and theory of evidence." Image and Vision Computing 24, no. 1 (January 2006): 61–69. http://dx.doi.org/10.1016/j.imavis.2005.09.012.

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18

Dridi, Noura, Yves Delignon, and Wadih Sawaya. "Critères BIC et AIC pour les chaînes de Markov cachées. Application aux communications numériques." Traitement du signal 31, no. 3-4 (October 28, 2014): 383–400. http://dx.doi.org/10.3166/ts.31.383-400.

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19

Bakry, Dominique, Xavier Milhaud, and Pierre Vandekerkhove. "Statistique de chaînes de Markov cachées à espace d'états fini. Le cas non stationnaire." Comptes Rendus de l'Académie des Sciences - Series I - Mathematics 325, no. 2 (July 1997): 203–6. http://dx.doi.org/10.1016/s0764-4442(97)84600-9.

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20

Letierce, A., and C. Kempf. "Modélisation des changements de traitement antihypertenseurs en France à l’aide de chaînes de Markov." Revue d'Épidémiologie et de Santé Publique 62 (February 2014): S27—S28. http://dx.doi.org/10.1016/j.respe.2013.11.069.

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21

Letierce, A., and C. Kempf. "Modélisation des changements de traitement antihypertenseurs en France à l’aide de chaînes de Markov." Revue d'Épidémiologie et de Santé Publique 62 (February 2014): S55. http://dx.doi.org/10.1016/j.respe.2013.12.066.

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22

Bickel, P. "La vraisemblance des chaînes de Markov cachées se comporte comme celle des variables i.i.d." Annales de l'Institut Henri Poincare (B) Probability and Statistics 38, no. 6 (December 2002): 825–46. http://dx.doi.org/10.1016/s0246-0203(02)01134-2.

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23

Guibourg, Denis. "Théorème de renouvellement pour chaînes de Markov fortement ergodiques : application aux modèles itératifs lipschitziens." Comptes Rendus Mathematique 346, no. 7-8 (April 2008): 435–38. http://dx.doi.org/10.1016/j.crma.2008.02.010.

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24

Pieczynski, Wojciech. "Arbres de Markov Triplet et fusion de Dempster–Shafer." Comptes Rendus Mathematique 336, no. 10 (May 2003): 869–72. http://dx.doi.org/10.1016/s1631-073x(03)00188-2.

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25

Hervé, Loïc. "Vitesse de convergence dans le théorème limite central pour des chaînes de Markov fortement ergodiques." Annales de l'Institut Henri Poincaré, Probabilités et Statistiques 44, no. 2 (April 2008): 280–92. http://dx.doi.org/10.1214/07-aihp101.

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26

Benboudjema, D., and W. Pieczynski. "Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields." IEEE Transactions on Pattern Analysis and Machine Intelligence 29, no. 8 (August 2007): 1367–78. http://dx.doi.org/10.1109/tpami.2007.1059.

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27

Blanchet, J., and F. Forbes. "Triplet Markov Fields for the Classification of Complex Structure Data." IEEE Transactions on Pattern Analysis and Machine Intelligence 30, no. 6 (June 2008): 1055–67. http://dx.doi.org/10.1109/tpami.2008.27.

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28

Xiaojie Lian, Yan Wu, Wei Zhao, Fan Wang, Qiang Zhang, and Ming Li. "Unsupervised SAR Image Segmentation Based on Conditional Triplet Markov Fields." IEEE Geoscience and Remote Sensing Letters 11, no. 7 (July 2014): 1185–89. http://dx.doi.org/10.1109/lgrs.2013.2286222.

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29

Gorynin, Ivan, Hugo Gangloff, Emmanuel Monfrini, and Wojciech Pieczynski. "Assessing the segmentation performance of pairwise and triplet Markov models." Signal Processing 145 (April 2018): 183–92. http://dx.doi.org/10.1016/j.sigpro.2017.12.006.

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30

Lemaire, B. "Régularité des matrices à diagonale dominante. Applications à l'absorption dans les chaînes et processus de Markov." RAIRO - Operations Research 19, no. 3 (1985): 233–41. http://dx.doi.org/10.1051/ro/1985190302331.

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31

Miclo, L. "Une majoration sous-exponentielle pour la convergence de l'entropie des chaînes de Markov à trou spectral." Annales de l'Institut Henri Poincare (B) Probability and Statistics 35, no. 3 (June 1999): 261–311. http://dx.doi.org/10.1016/s0246-0203(99)80013-2.

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32

Boussama, Farid. "Ergodicité des chaînes de Markov à valeurs dans une variété algébrique : application aux modèles GARCH multivariés." Comptes Rendus Mathematique 343, no. 4 (August 2006): 275–78. http://dx.doi.org/10.1016/j.crma.2006.06.027.

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33

Fan Wang, Yan Wu, Qiang Zhang, Peng Zhang, Ming Li, and Yunlong Lu. "Unsupervised Change Detection on SAR Images Using Triplet Markov Field Model." IEEE Geoscience and Remote Sensing Letters 10, no. 4 (July 2013): 697–701. http://dx.doi.org/10.1109/lgrs.2012.2219494.

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34

Ben Abbes, Ali, Imed Riadh Farah, Vincent Barra, and Mohamed Farah. "A non-stationary NDVI time series modelling using triplet Markov chain." International Journal of Information and Decision Sciences 11, no. 2 (2019): 163. http://dx.doi.org/10.1504/ijids.2019.10022656.

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35

Abbes, Ali Ben, Mohamed Farah, Imed Riadh Farah, and Vincent Barra. "A non-stationary NDVI time series modelling using triplet Markov chain." International Journal of Information and Decision Sciences 11, no. 2 (2019): 163. http://dx.doi.org/10.1504/ijids.2019.101143.

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36

Lanchantin, Pierre, Jérôme Lapuyade-Lahorgue, and Wojciech Pieczynski. "Unsupervised segmentation of triplet Markov chains hidden with long-memory noise." Signal Processing 88, no. 5 (May 2008): 1134–51. http://dx.doi.org/10.1016/j.sigpro.2007.10.015.

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37

Lehmann, Frederic, and Wojciech Pieczynski. "Suboptimal Kalman Filtering in Triplet Markov Models Using Model Order Reduction." IEEE Signal Processing Letters 27 (2020): 1100–1104. http://dx.doi.org/10.1109/lsp.2020.3002420.

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38

Patron, Alberto, and Christian Cremona. "Modèle de chaînes de Markov pour l'étude de la fissuration par fatigue des assemblages soudés de ponts." Revue Européenne de Génie Civil 11, no. 9-10 (December 2007): 1111–33. http://dx.doi.org/10.1080/17747120.2007.9692979.

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39

Patron, Alberto, and Christian Cremona. "Modèle de chaînes de Markov pour l'étude de la fissuration par fatigue des assemblages soudés de ponts." Revue européenne de génie civil 11, no. 9-10 (December 31, 2007): 1111–33. http://dx.doi.org/10.3166/regc.11.1111-1133.

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40

Chen, Shou, and Xiangqian Jiang. "Modeling Repayment Behavior of Consumer Loan in Portfolio across Business Cycle: A Triplet Markov Model Approach." Complexity 2020 (January 19, 2020): 1–11. http://dx.doi.org/10.1155/2020/5458941.

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Abstract:

With a view to develop a more realistic model for credit risk analysis in consumer loan, our paper addresses the problem of how to incorporate business cycles into a repayment behavior model of consumer loan in portfolio. A particular Triplet Markov Model (TMM) is presented and introduced to describe the dynamic repayment behavior of consumers. The particular TMM can simultaneously capture the phases of business cycles, transition of systematic credit risk of a loan portfolio, and Markov repayment behavior of consumers. The corresponding Markov chain Monte Carlo algorithms of the particular TMM are also developed for estimating the model parameters. We show how the transition of consumers’ repayment states and systematic credit risk of a loan portfolio are affected by the phases of business cycles through simulations.

41

Volobuev, AN N., ES S. Petrov, and NP P. Romanchuk. "BIOPHYSICAL BASES OF GENOME ORGANIZATION." Science and Innovations in Medicine 2, no. 4 (December 15, 2017): 13–17. http://dx.doi.org/10.35693/2500-1388-2017-0-4-13-17.

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Abstract:

Aim - the analysis of nucleotide sequences of DNA molecules and the bases of the information storage with the help of DNA. Material and methods - the study is based on Markov chain theory and Bayesian Information Criterion. Results. Principles of genetic code construction were investigated. Specific nucleotide sequences were analyzed using Markov chain theory; the method of sequencing nucleotide sequences was described. Conclusion. A nucleotide sequence has certain restrictions associated with complementarity of the bases along DNA chain. These restrictions at the level of triplet sequence can be eliminated by degeneracy of the genetic code.

42

Lu Gan, Yan Wu, Fan Wang, Peng Zhang, and Qiang Zhang. "Unsupervised SAR Image Segmentation Based on Triplet Markov Fields With Graph Cuts." IEEE Geoscience and Remote Sensing Letters 11, no. 4 (April 2014): 853–57. http://dx.doi.org/10.1109/lgrs.2013.2280025.

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43

Zhang, Peng, Ming Li, Yan Wu, Lu Gan, Ming Liu, Fan Wang, and Gaofeng Liu. "Unsupervised multi-class segmentation of SAR images using fuzzy triplet Markov fields model." Pattern Recognition 45, no. 11 (November 2012): 4018–33. http://dx.doi.org/10.1016/j.patcog.2012.04.019.

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44

Wang, Fan, Yan Wu, Qiang Zhang, Wei Zhao, Ming Li, and Guisheng Liao. "Unsupervised SAR Image Segmentation Using Higher Order Neighborhood-Based Triplet Markov Fields Model." IEEE Transactions on Geoscience and Remote Sensing 52, no. 8 (August 2014): 5193–205. http://dx.doi.org/10.1109/tgrs.2013.2287273.

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45

Wang, Fan, Yan Wu, Peng Zhang, Qingjun Zhang, and Ming Li. "Unsupervised SAR Image Segmentation Using Ambiguity Label Information Fusion in Triplet Markov Fields Model." IEEE Geoscience and Remote Sensing Letters 14, no. 9 (September 2017): 1479–83. http://dx.doi.org/10.1109/lgrs.2017.2715223.

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46

Wang, Fan, Jianwei Fan, Yan Wu, Qiang Zhang, Xue Zhang, and Ming Li. "Synthetic aperture radar image segmentation using fuzzy label field-based triplet Markov fields model." IET Image Processing 8, no. 12 (December 1, 2014): 856–65. http://dx.doi.org/10.1049/iet-ipr.2013.0686.

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47

Wu, Yan, Xin Wang, Ping Xiao, Lu Gan, ChunYan Liu, and Ming Li. "Fast algorithm based on triplet Markov fields for unsupervised multi-class segmentation of SAR images." Science China Information Sciences 54, no. 7 (March 26, 2011): 1524–33. http://dx.doi.org/10.1007/s11432-011-4215-x.

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48

Wang, Fan, Yan Wu, Peng Zhang, Wenkai Liang, and Ming Li. "Synthetic aperture radar image segmentation using non-linear diffusion-based hierarchical triplet Markov fields model." IET Image Processing 11, no. 12 (December 1, 2017): 1302–9. http://dx.doi.org/10.1049/iet-ipr.2016.0901.

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49

Naqvi, Syed Arsalan Ahmed, Kunwer Sufyan Faisal, Muhammad Ali Khan, Kaneez Zahra Rubab Khakwani, Holly K. Van Houten, James P. Moriarty, Bijan J. Borah, et al. "A cost-effectiveness analysis assessing systemic treatments in metastatic castration-sensitive prostate cancer (mCSPC) by volume of disease." Journal of Clinical Oncology 42, no. 4_suppl (February 1, 2024): 93. http://dx.doi.org/10.1200/jco.2024.42.4_suppl.93.

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Abstract:

93 Background: The treatment paradigm for mCSPC patients has rapidly evolved over the last few years with the emergence of triplet therapy and prognostication by volume of disease. However, the health economic implications of triplet therapy by disease volume remain unclear. Methods: A Markov model where patients transitioned between the progression-free, progression and death states was developed. State utilities were derived from the LATITUDE trial. Point probabilities for overall survival, progression-free survival, grade ≥ 3 and 5 toxicities were obtained from the eligible mCSPC phase III clinical trials. Medicare Advantage and commercial total paid amounts (2022 US$) of treatments were obtained from OptumLabs Data Warehouse. Costs and disutilities of adverse events were derived from published literature. Half-cycle corrected costs and utilities were accrued monthly over a 20-year lifetime horizon and were discounted at 3%. Monte Carlo simulation was used to calculate incremental cost-effectiveness ratios (ICERs). A willingness-to-pay (WTP) threshold of US$100,000 was used. Results: In terms of Medicare Advantage payer’s perspective (Table), docetaxel (D)+ADT was observed to provide additional 0.068 QALYs compared to ADT at an ICER of US$16,544 per QALY in high volume disease. Compared to D+ADT, abiraterone (AAP) triplet provided additional 1.03 QALYs at US$56,641 per QALY. Compared to AAP triplet, apalutamide (APA)+ADT provided additional 0.048 QALYs at US$5,846,248 per QALY in high volume disease. In low volume disease, D+ADT provided 0.45 more QALYs at US$20,690 per QALY compared to ADT alone. Compared to D+ADT, AAP triplet provided an additional 1.51 QALYS at US$44,267 per QALY. In terms of total paid commercial costs, AAP triplet provided an additional 1.19 QALYs compared to ADT at US$91,952 per QALY in high volume disease. In low volume, D+ADT provided additional 0.38 QALYs at US$22,058 per QALY compared to ADT. Compared to D+ADT, AAP triplet provided a 0.97 QALY gain at US$105,958 per QALY. Compared to AAP triplet, darolutamide (DARO) triplet provided a 0.47 QALY at US$689,196 per QALY. Compared to DARO triplet, APA+ADT provided an additional 0.49 QALYs at an ICER of US$971,389 per QALY. All other treatments (not reported here) were dominated. Conclusions: At the selected WTP of US$100,000 per QALY, AAP triplet therapy was most likely the cost-effective strategy in overall mCSPC population particularly in patients with high volume disease. However, these results should be interpreted with careful accounting for timing of metastases. [Table: see text]

50

Obeng-Kusi, Mavis, Daniel Arku, Neda Alrawashdh, Briana Choi, Nimer S. Alkhatib, Ali McBride, and Ivo Abraham. "Ixazomib (IXA), carfilzomib (CAR), elotuzumab (ELO) or daratumumab (DAR) with lenalidomide and dexamethasone (LEN+DEX) versus LEN+DEX only in relapsed/refractory multiple myeloma (R/R MM): A comparative cost-effectiveness analysis." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 8043. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.8043.

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8043 Background: IXA, CAR, ELO and DARin combination with LEN+DEXhave been found superior in efficacy compared to LEN+DEX in the management of R/R MM. Applying indirect treatment comparisons from a network meta-analysis (NMA), this economic evaluation aimed to estimate the comparative cost-effectiveness and cost-utility of these four triplet regimens in terms of progression-free survival (PFS). Methods: In the absence of direct treatment comparison from a single clinical trial, NMA was used to indirectly estimate the comparative PFS benefit of each regimen. A 2-state Markov model simulating the health outcomes and costs was used to evaluate PFS life years (LY) and quality-adjusted life years (QALY) with the triplet regimens over LEN+DEX and expressed as the incremental cost-effectiveness (ICER) and cost-utility ratios (ICUR). Probability sensitivity analyses were conducted to assess the influence of parameter uncertainty on the model. Results: The NMA revealed that DAR+LEN+DEX was superior to the other triplet therapies, which did not differ statistically amongst them. As detailed in the Table, in our cost-effectiveness analysis, all 4 triplet regimens were associated with increased PFSLY and PFSQALY gained (g) over LEN+DEX at an additional cost. DAR+LEN+DEX emerged the most cost-effective with ICER and ICUR of $667,652/PFSLYg and $813,322/PFSQALYg, respectively. The highest probability of cost-effectiveness occurred at a willingness-to-pay threshold of $1,040,000/QALYg. Conclusions: Our economic analysis shows that all the triplet regimens were more expensive than LEN +DEX only but were also more effective with respect to PFSLY and PFSQALY gained. Relative to the other regimens, the daratumumab regimen was the most cost-effective.[Table: see text]