In many clinical studies, continuous variables such as age, blood pressure and cholesterol are measured and analyzed. Often clinicians prefer to categorize these continuous variables into different groups, such as low and high risk groups. The goal of this work is to find the cutpoint of a continuous variable where the transition occurs from low to high risk group. Different methods have been published in literature to find such a cutpoint. We extended the methods of Contal and O’Quigley (1999) which was based on the log-rank test and the methods of Klein and Wu (2004) which was based on the Score test to find the cutpoint of a continuous covariate. Since the log-rank test is a nonparametric method and the Score test is a parametric method, we are interested to see if an extension of the parametric procedure performs better when the distribution of a population is known. We have developed a method that uses the parametric score residuals to find the cutpoint. The performance of the proposed method will be compared with the existing methods developed by Contal and O’Quigley and Klein and Wu by estimating the bias and mean square error of the estimated cutpoints for different scenarios in simulated data.

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Este trabalho visa apresentar métodos de categorização de variáveis explicativas contínuas em Análise de Sobrevivência. Do ponto de vista clínico, agrupar pacientes em grupos de risco distintos é importante para agilizar tomadas de decisões; entretanto, perda de informação e outros problemas estatísticos podem ocorrer. Portanto, métodos para seleção de pontos de corte e correção dos possíveis problemas gerados pela categorização são criticamente avaliados. Para a aplicação e comparação dos métodos são utilizados dados do Instituto do Coração do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (InCor - FMUSP), em que a variável fração de ejeção é dicotomizada e tricotomizada. / This dissertation aims to present methods of categorization for continuous variables in Survival Analysis. From a clinical point of view, grouping patients into distinct risk groups is important for accelerating decision-making; however, loss of information and other statistical problems may occur. Therefore, methods for selecting cutpoints and correcting problems generated by categorization are critically evaluated. For the application and comparison of the methods, the dataset from Heart Institute - University of Sao Paulo Medical School (InCor FMUSP) is used, in which the variable ejection fraction is dichotomized and trichotomized.

Este trabalho visa apresentar métodos de categorização de variáveis explicativas contínuas em Análise de Sobrevivência. Do ponto de vista clínico, agrupar pacientes em grupos de risco distintos é importante para agilizar tomadas de decisões; entretanto, perda de informação e outros problemas estatísticos podem ocorrer. Portanto, métodos para seleção de pontos de corte e correção dos possíveis problemas gerados pela categorização são criticamente avaliados. Para a aplicação e comparação dos métodos são utilizados dados do Instituto do Coração do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (InCor - FMUSP), em que a variável fração de ejeção é dicotomizada e tricotomizada. / This dissertation aims to present methods of categorization for continuous variables in Survival Analysis. From a clinical point of view, grouping patients into distinct risk groups is important for accelerating decision-making; however, loss of information and other statistical problems may occur. Therefore, methods for selecting cutpoints and correcting problems generated by categorization are critically evaluated. For the application and comparison of the methods, the dataset from Heart Institute - University of Sao Paulo Medical School (InCor FMUSP) is used, in which the variable ejection fraction is dichotomized and trichotomized.