IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i8p836-d534416.html
   My bibliography  Save this article

A New Compromise Design Plan for Accelerated Failure Time Models with Temperature as an Acceleration Factor

Author

Listed:
  • Irene Mariñas-Collado

    (Department of Statistics and Operations Research and Mathematics Didactics, University of Oviedo, 33007 Oviedo, Spain)

  • M. Jesús Rivas-López

    (Department of Statistics, Institute of Fundamental Physics and Mathematics, University of Salamanca, 37008 Salamanca, Spain)

  • Juan M. Rodríguez-Díaz

    (Department of Statistics, Institute of Fundamental Physics and Mathematics, University of Salamanca, 37008 Salamanca, Spain)

  • M. Teresa Santos-Martín

    (Department of Statistics, Institute of Fundamental Physics and Mathematics, University of Salamanca, 37008 Salamanca, Spain)

Abstract

An accelerated life test of a product or material consists of the observation of its failure time when it is subjected to conditions that stress the usual ones. The purpose is to obtain the parameters of the distribution of the time-to-failure for usual conditions through the observed failure times. A widely used method to provoke an early failure in a mechanism is to modify the temperature at which it is used. In this paper, the statistically optimal plan for Accelerated Failure Time (AFT) models, when the accelerated failure process is described making use of Arrhenius or Eyring equations, was calculated. The result was a design that had only two stress levels, as is common in other AFT models and that is not always practical. A new compromise plan was presented as an alternative to the widely used “4:2:1 plan”. The three-point mixture design proposed specified a support point in the interval that was optimal for the estimation of the parameters in AFT models, rather than simply the middle point. It was studied in comparison to different commonly used designs, and it proved to have a higher D-efficiency than the others.

Suggested Citation

  • Irene Mariñas-Collado & M. Jesús Rivas-López & Juan M. Rodríguez-Díaz & M. Teresa Santos-Martín, 2021. "A New Compromise Design Plan for Accelerated Failure Time Models with Temperature as an Acceleration Factor," Mathematics, MDPI, vol. 9(8), pages 1-16, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:8:p:836-:d:534416
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/8/836/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/8/836/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Engler David & Li Yi, 2009. "Survival Analysis with High-Dimensional Covariates: An Application in Microarray Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-22, February.
    2. L. Altstein & G. Li, 2013. "Latent Subgroup Analysis of a Randomized Clinical Trial through a Semiparametric Accelerated Failure Time Mixture Model," Biometrics, The International Biometric Society, vol. 69(1), pages 52-61, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang Zhu & Wang C.Y., 2010. "Buckley-James Boosting for Survival Analysis with High-Dimensional Biomarker Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-33, June.
    2. Xifen Huang & Chaosong Xiong & Jinfeng Xu & Jianhua Shi & Jinhong Huang, 2022. "Mixture Modeling of Time-to-Event Data in the Proportional Odds Model," Mathematics, MDPI, vol. 10(18), pages 1-11, September.
    3. Sudipta Saha & Zhihui Liu & Olli Saarela, 2021. "Instrumental variable estimation of early treatment effect in randomized screening trials," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(4), pages 537-560, October.
    4. Zhang, Yuyang & Schnell, Patrick & Song, Chi & Huang, Bin & Lu, Bo, 2021. "Subgroup causal effect identification and estimation via matching tree," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    5. Zhao, Xiaobing & Zhou, Xian, 2014. "Sufficient dimension reduction on marginal regression for gaps of recurrent events," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 56-71.
    6. Sariyar Murat & Schumacher Martin & Binder Harald, 2014. "A boosting approach for adapting the sparsity of risk prediction signatures based on different molecular levels," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(3), pages 1-15, June.
    7. Xifen Huang & Jinfeng Xu, 2022. "Subgroup Identification and Regression Analysis of Clustered and Heterogeneous Interval-Censored Data," Mathematics, MDPI, vol. 10(6), pages 1-11, March.
    8. Shengli An & Peter Zhang & Hong-Bin Fang, 2023. "Subgroup Identification in Survival Outcome Data Based on Concordance Probability Measurement," Mathematics, MDPI, vol. 11(13), pages 1-10, June.
    9. Antonella Iuliano & Annalisa Occhipinti & Claudia Angelini & Italia De Feis & Pietro Liò, 2021. "COSMONET: An R Package for Survival Analysis Using Screening-Network Methods," Mathematics, MDPI, vol. 9(24), pages 1-25, December.
    10. Lee, Kyu Ha & Chakraborty, Sounak & Sun, Jianguo, 2017. "Variable selection for high-dimensional genomic data with censored outcomes using group lasso prior," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 1-13.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:8:p:836-:d:534416. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.