No abstract

No abstract

We propose a cure rate survival model by assuming that the number of competing causes of the event of interest follows the negative binomial distribution and the time to the event of interest has the Birnbaum-Saunders distribution. Further, the new model includes as special cases some well-known cure rate models published recently. We consider a frequentist analysis for parameter estimation of the negative binomial Birnbaum-Saunders model with cure rate. Then, we derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. We illustrate the usefulness of the proposed model in the analysis of a real data set from the medical area.

No abstract

No abstract

Paper aims: To determine main barriers to the implementation of occupational health and safety management systems OHSMS in the context of small Brazilian enterprises from the perspectives of owners/managers, labor auditors, and OHS consultants. Originality: Survey with three different perspectives on small Brazilian enterprises. Research method: Survey conducted with stakeholders who influence the safety culture in small enterprises to identify the main barriers to the implementation of OHSMS. Main findings: Owners/managers tend to blame employees and the government for difficulty in implementing OHSMS, and external actors tend to blame management and resource allocation. Opinions converge on inappropriate management behavior, ineffective information and communication and production prioritization. Implications for theory and practice: These barriers should be overcome not only to facilitate the implementation of OHSMS but also to improve the conditions for the management of all small business operations.

We propose a new survival model for lifetime data in the presence of surviving fraction and obtain some of its properties. Its genesis is based on extensions of the promotion time cure model, where an extra parameter controls the heterogeneity or dependence of an unobserved number of lifetimes. We construct a regression model to evaluate the effects of covariates in the cured fraction. We discuss inference aspects for the proposed model in a classical approach, where some maximum likelihood tools are explored. Further, an expectation maximization algorithm is developed to calculate the maximum likelihood estimates of the model parameters. We also perform an empirical study of the likelihood ratio test in order to compare the promotion time cure and the proposed models. We illustrate the usefulness of the new model by means of a colorectal cancer data set.

No abstract