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Interval-Censored Regression with Non-Proportional Hazards with Applications

Author

Listed:
  • Fábio Prataviera

    (Department of Exact Sciences, “Luiz de Queiroz” School of Agriculture, University of São Paulo—ESALQ/USP, Piracicaba 13418-900, Brazil
    These authors contributed equally to this work.)

  • Elizabeth M. Hashimoto

    (Academic Department of Mathematics, Federal University of Technology, Londrina 86036-370, Brazil
    These authors contributed equally to this work.)

  • Edwin M. M. Ortega

    (Department of Exact Sciences, “Luiz de Queiroz” School of Agriculture, University of São Paulo—ESALQ/USP, Piracicaba 13418-900, Brazil
    These authors contributed equally to this work.)

  • Taciana V. Savian

    (Department of Exact Sciences, “Luiz de Queiroz” School of Agriculture, University of São Paulo—ESALQ/USP, Piracicaba 13418-900, Brazil
    These authors contributed equally to this work.)

  • Gauss M. Cordeiro

    (Department of Statistics, Federal University of Pernambuco, Recife 50670-901, Brazil
    These authors contributed equally to this work.)

Abstract

Proportional hazards models and, in some situations, accelerated failure time models, are not suitable for analyzing data when the failure ratio between two individuals is not constant. We present a Weibull accelerated failure time model with covariables on the location and scale parameters. By considering the effects of covariables not only on the location parameter, but also on the scale, a regression should be able to adequately describe the difference between treatments. In addition, the deviance residuals adapted for data with the interval censored and the exact time of failure proved to be satisfactory to verify the fit of the model. This information favors the Weibull regression as an alternative to the proportional hazards models without masking the effect of the explanatory variables.

Suggested Citation

  • Fábio Prataviera & Elizabeth M. Hashimoto & Edwin M. M. Ortega & Taciana V. Savian & Gauss M. Cordeiro, 2023. "Interval-Censored Regression with Non-Proportional Hazards with Applications," Stats, MDPI, vol. 6(2), pages 1-14, May.
    • Handle: RePEc:gam:jstats:v:6:y:2023:i:2:p:41-656:d:1148973
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    References listed on IDEAS

    as
    1. Hashimoto, Elizabeth M. & Ortega, Edwin M.M. & Cancho, Vicente G. & Cordeiro, Gauss M., 2010. "The log-exponentiated Weibull regression model for interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1017-1035, April.
    2. Leiva, Victor & Barros, Michelli & Paula, Gilberto A. & Galea, Manuel, 2007. "Influence diagnostics in log-Birnbaum-Saunders regression models with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5694-5707, August.
    3. Ma, Ling & Hu, Tao & Sun, Jianguo, 2016. "Cox regression analysis of dependent interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 79-90.
    4. C. P. Farrington, 2000. "Residuals for Proportional Hazards Models with Interval-Censored Survival Data," Biometrics, The International Biometric Society, vol. 56(2), pages 473-482, June.
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