Gamma-G regression model in survival analysis

• Grant number: 10/04496-2
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: July 01, 2010
• End date: November 30, 2012
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Applied Probability and Statistics
• Principal Investigator: Edwin Moises Marcos Ortega
• Grantee: Elizabeth Mie Hashimoto
• Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil

Abstract

Survival analysis consists of a collection of statistical procedures to analyze data related to time until the occurrence of an event of interest, whose main characteristic of these data is the presence of censored observations. The probability distributions commonly used in modeling censored data distributions are exponential, Weibull, log-normal, log-logistic and generalized gamma. However, it is frequent occurrence of data to which the hazard function is non-monotonic. It is therefore appropriate to consider parametric families of distributions that are flexible to capture a wide variety of behaviors that include symmetric and asymmetric distributions of the classical survival analysis as special cases and produce more robust estimates in the model considered. For this reason, propose new distributions that modeling survival data which non-monotonic hazard function is a research area very important in many fields, especially in the survival analysis, distributions of models are used to search the treatment of cancer and recently in studies of stem cells and environmental impacts. Thus, this study proposes a new family of probability distribution, called the family of gamma-G distribution applied in the context of regression models, more specifically in survival analysis.

Scientific publications

(9)

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

CORDEIRO, GAUSS M.; HASHIMOTO, ELIZABETH M.; ORTEGA, EDWIN M. M.. The McDonald Weibull model. STATISTICS, v. 48, n. 2, p. 256-278, MAR 4 2014. (10/04496-2)

HASHIMOTO, ELIZABETH M.; CORDEIRO, GAUSS M.; ORTEGA, EDWIN M. M.. The new Neyman type A beta Weibull model with long-term survivors. Computational Statistics, v. 28, n. 3, p. 933-954, JUN 2013. (10/04496-2)

CORDEIRO, GAUSS M.; HASHIMOTO, ELIZABETH M.; ORTEGA, EDWIN M. M.; PASCOA, MARCELINO A. R.. The McDonald extended distribution: properties and applications. AStA-Advances in Statistical Analysis, v. 96, n. 3, p. 409-433, JUL 2012. (10/04496-2)

HASHIMOTO, ELIZABETH M.; SILVA, GIOVANA O.; ORTEGA, EDWIN M. M.; CORDEIRO, GAUSS M.. Log-Burr XII Gamma-Weibull Regression Model with Random Effects and Censored Data. JOURNAL OF STATISTICAL THEORY AND PRACTICE, v. 13, n. 2, JUN 2019. (10/04496-2)

HASHIMOTO, ELIZABETH M.; ORTEGA, EDWIN M. M.; CORDEIRO, GAUSS M.; PASCOA, MARCELINO A. R.. The McDonald Extended Weibull Distribution. JOURNAL OF STATISTICAL THEORY AND PRACTICE, v. 9, n. 3, p. 608-632, 2015. (10/04496-2)

HASHIMOTO, ELIZABETH M.; ORTEGA, EDWIN M. M.; CORDEIRO, GAUSS M.; HAMEDANI, G. G.. The Log-gamma-logistic Regression Model: Estimation, Sensibility and Residual Analysis. Journal of Statistical Theory and Applications, v. 16, n. 4, p. 547-564, DEC 2017. (10/04496-2)

HASHIMOTO, ELIZABETH M.; ORTEGA, EDWIN M. M.; CORDEIRO, GAUSS M.; CANCHO, VICENTE G.; KLAUBERG, CARINE. Zero-spiked regression models generated by gamma random variables with application in the resin oil production. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v. 89, n. 1, p. 52-70, JAN 2 2019. (10/04496-2)

HASHIMOTO, ELIZABETH M.; ORTEGA, EDWIN M. M.; CANCHO, VICENTE G.; CORDEIRO, GAUSS M.. On estimation and diagnostics analysis in log-generalized gamma regression model for interval-censored data. STATISTICS, v. 47, n. 2, p. 379-398, APR 1 2013. (10/04496-2)

HASHIMOTO, ELIZABETH M.; ORTEGA, EDWIN M. M.; CORDEIRO, GAUSS M.; BARRETO, MAURICIO L.. THE LOG-BURR XII REGRESSION MODEL FOR GROUPED SURVIVAL DATA. Journal of Biopharmaceutical Statistics, v. 22, n. 1, p. 141-159, 2012. (10/04496-2)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

HASHIMOTO, Elizabeth Mie. Gama-G regression model in survival analysis. 2013. Doctoral Thesis - Universidade de São Paulo (USP). Escola Superior de Agricultura Luiz de Queiroz (ESALA/BC) Piracicaba.