Defective models for cure rate modeling

• Autor(a) principal: Rocha, Ricardo Ferreira da
• Data de Publicação: 2016
• Tipo de documento: Tese
• Idioma: eng
• Título da fonte: Repositório Institucional da UFSCAR
• Texto Completo: https://repositorio.ufscar.br/handle/ufscar/7751
• topic: Cure fraction; Defective models; Inverse Gaussian distribution; Gompertz distribution; Kumaraswamy family; Fração de cura; Análise de sobrevivência; Distribuição Gompertz; Distribuição inversa Gaussiana; Modelos de longa duração; Modelos defeituosos; CIENCIAS EXATAS E DA TERRA

Modeling of a cure fraction, also known as long-term survivors, is a part of survival analysis. It studies cases where supposedly there are observations not susceptible to the event of interest. Such cases require special theoretical treatment, in a way that the modeling assumes the existence of such observations. We need to use some strategy to make the survival function converge to a value p 2 (0; 1), representing the cure rate. A way to model cure rates is to use defective distributions. These distributions are characterized by having probability density functions which integrate to values less than one when the domain of some of their parameters is di erent from that usually de ned. There is not so much literature about these distributions. There are at least two distributions in the literature that can be used for defective modeling: the Gompertz and inverse Gaussian distribution. The defective models have the advantage of not need the assumption of the presence of immune individuals in the data set. In order to use the defective distributions theory in a competitive way, we need a larger variety of these distributions. Therefore, the main objective of this work is to increase the number of defective distributions that can be used in the cure rate modeling. We investigate how to extend baseline models using some family of distributions. In addition, we derive a property of the Marshall-Olkin family of distributions that allows one to generate new defective models.