Open Access
| Issue |
Int. J. Metrol. Qual. Eng.
Volume 15, 2024
|
|
|---|---|---|
| Article Number | 19 | |
| Number of page(s) | 14 | |
| DOI | https://doi.org/10.1051/ijmqe/2024015 | |
| Published online | 04 October 2024 | |
- R Core Team. R: Alanguage and environment for S statistical S computing. (R S Foundation for Statistical Computing, Vienna, AT, 2022) [Google Scholar]
- T.M. Therneau, Coxme: mixed effects Cox models. R package version 2.2-18.1; 2022. https://CRAN.R-project.org/package=coxme [Google Scholar]
- D.R. Cox, Regression models and life tables (with discussion), J. Royal Stat. Soc. Ser. B. 34, 187–220 (1972) [CrossRef] [Google Scholar]
- J.P. Fine, R.J. Gray, A proportional hazards model for the subdistribution of a competing risk, J. Am. Stat. Assoc. 94, 496–509 (1999) [CrossRef] [Google Scholar]
- F. Bertocci, A. Grandoni, A.T. Djuric-Rissner, Scanning acoustic microscopy (SAM): a robust method for defect detection during the manufacturing process of ultrasound probes for medical imaging, Sensors 19, 4868–4886 (2019) [CrossRef] [PubMed] [Google Scholar]
- F. Bertocci, A. Grandoni, M. Fidanza, R. Berni, A guideline for implementing a robust optimization of a complex multi-stage manufacturing process, Appl. Sci. 11, 1–19 (2021) [Google Scholar]
- A.M. Vitikainen, J.I. Peltonen, E. Vartiainen, Routine ultrasound quality assurance in a multi-unit radiology department: a retrospective evaluation of transducer failures, Ultrasound Med. Biol. 43, 1930–1937 (2017) [CrossRef] [Google Scholar]
- W. Ding, M. Bavencoffe, M. Lethiecq, Modeling and experimental characterization of bonding delaminations in single-element ultrasonic transducer, Materials 14, 2269 (2021) [CrossRef] [PubMed] [Google Scholar]
- N.J. Dudley, D.J. Woolley, Blinded comparison between an in-air reverberation method and an electronic probe tester in the detection of ultrasound probe faults, Ultrasound Med. Biol. 43, 2954–2958 (2017) [CrossRef] [Google Scholar]
- J. Vachutka, L. Dolezal, C. Kollmann, J. Klein, The effect of dead elements on the accuracy of Doppler ultrasound measurements, Ultrason Imaging. 36, 18–34 (2014) [CrossRef] [PubMed] [Google Scholar]
- R. Lorentsson, N. Hosseini, L.G. Mansson, M. Bath, Evaluation of an automatic method for detection of defects in linear and curvilinear ultrasound transducers, Phys. Med. 84, 33–40 (2021) [CrossRef] [Google Scholar]
- R. Lorentsson, N. Hosseini, J.O. Johansson, W. Rosenberg, B. Stenborg, L.G. Mansson, M. Bath, Method for automatic detection of defective ultrasound linear array transducers based on uniformity assessment of clinical images − a case study, J. Appl. Clin. Med. Phys. 19, 265–274 (2018) [CrossRef] [Google Scholar]
- L. Wang, B. Li, B. Hu, G. Shen, Y. Zhen, Y. Zheng, Failure mode effect and criticality analysis of ultrasound device by classification tracking, BMC Health Serv. Res. 22, 1–10 (2022) [CrossRef] [Google Scholar]
- E. Sassaroli, A. Scorza, C. Crake, S.A. Sciuto, M. Park, Breast ultrasound technology and performance evaluation of ultrasound equipment: B‐ Mode. IEEE Trans. Ultrason. Ferroelectr. Fr eq. Control. 64, 192–205 (2017) [CrossRef] [PubMed] [Google Scholar]
- W.Q. Meeker, L.A. Escobar: statistical methods for reliability data (John Wiley & Sons, New York, 1998) [Google Scholar]
- P.D. Allison: Survival analysis using SAS − a practical guide, 2nd edn. (SAS Institute, Cary (US-NC), 2010) [Google Scholar]
- L. Duchateau, P. Janssen, The frailty model (Springer, New York, 2007) [Google Scholar]
- A. Wienke, Frailty models in survival analysis (CRC Press, Amsterdam, The Netherland, 2010) [CrossRef] [Google Scholar]
- J.W. Vaupel, K.G. Manton, E. Stallard, The impact of heterogeneity in individual frailty on the dynamics of mortality, Demography 16, 439–454 (1979) [CrossRef] [PubMed] [Google Scholar]
- T.A. Balan, M. Putter, A tutorial on frailty models, Stat. Methods Med. Res. 29, 3424–3454 (2020) [CrossRef] [PubMed] [Google Scholar]
- J.H. Abbring, G.J. Van Den Berg, The unobserved heterogeneity distribution in duration analysis, Biometrika 94, 87–99 (2007) [CrossRef] [Google Scholar]
- S. Ripatti, J. Palmgren, Estimation of multivariate frailty models using penalized partial likelihood, Biometrics 56, 1016–1022 (2000) [CrossRef] [PubMed] [Google Scholar]
- T.M. Therneau, P.M. Grambsch, V.S. Pankratz, Penalized survival models and frailty, J. Computat. Graph. Stat. 12, 156–175 (2003) [CrossRef] [Google Scholar]
- C. Elbers, G. Ridder, True and spurious duration dependence: the identifiability of the proportional hazard model, Rev. Economic. Studies. 49, 403–409 (1982) [CrossRef] [Google Scholar]
- Y. Lee Y, J.A.Nelder, Hierarchical generalized linear models (with discussion), J. R. Stat. Soc. B 58, 619–678 (1996) [CrossRef] [Google Scholar]
- R. Berni, M. Catelani, C. Fiesoli, V.L. Scarano, A comparison of alloy-surface finish combinations considering different component package types and their impact on soldering reliability, IEEE Trans. Reliab. 65, 272–281 (2016) [CrossRef] [Google Scholar]
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