Interval-Censored Regression with Non-Proportional Hazards with Applications

Fábio Prataviera, Elizabeth M. Hashimoto, Edwin M. M. Ortega (), Taciana V. Savian and Gauss M. Cordeiro
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Fábio Prataviera: Department of Exact Sciences, “Luiz de Queiroz” School of Agriculture, University of São Paulo—ESALQ/USP, Piracicaba 13418-900, Brazil
Elizabeth M. Hashimoto: Academic Department of Mathematics, Federal University of Technology, Londrina 86036-370, Brazil
Edwin M. M. Ortega: Department of Exact Sciences, “Luiz de Queiroz” School of Agriculture, University of São Paulo—ESALQ/USP, Piracicaba 13418-900, Brazil
Taciana V. Savian: Department of Exact Sciences, “Luiz de Queiroz” School of Agriculture, University of São Paulo—ESALQ/USP, Piracicaba 13418-900, Brazil
Gauss M. Cordeiro: Department of Statistics, Federal University of Pernambuco, Recife 50670-901, Brazil

Stats, 2023, vol. 6, issue 2, 1-14

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.

Keywords: interval-censored data; non-proportional hazards; regression model; residual deviance; survival analysis (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2023
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