The generalized odd log-logistic-G regression with interval-censored survival data.

The article proposes a new regression based on the generalized odd log-logistic family for interval-censored data. The survival times are not observed for this type of data, and the event of interest occurs at some random interval. This family can be used in interval modeling since it generalizes some popular lifetime distributions in addition to its ability to present various forms of the risk function. The estimation of the parameters is addressed by the classical and Bayesian methods. We examine the behavior of the estimates for some sample sizes and censorship percentages. Selection criteria, likelihood ratio tests, residual analysis, and graphical techniques assess the goodness of fit of the fitted models. The usefulness of the proposed models is red shown by means of two real data sets.

Alternative statistical modeling for radical prostatectomy data.

Several statistical models have been proposed in recent years, among them is the semiparametric regression. In medicine, there are several situations in which it is impracticable to consider a linear regression for statistical modeling, especially when the data contain explanatory variables that present a nonlinear relationship with the response variable. Another common situation is when the response variable does not have a unimodal shape, and it is not possible to adopt distributions belonging to the symmetric or asymmetric classes. In this context, a semiparametric heteroskedastic regression is proposed based on an extension of the normal distribution. Then, we show the usefulness of this model to analyze the cost of prostate cancer surgery. The predictor variables refer to two groups of patients such that one group receives a multimodal local anesthetic solution (Preemptive Target Anesthetic Solution) and the second group is treated with neuraxial blockade (spinal anesthesia/traditional standard). The other relevant predictor variables are also evaluated, thus allowing for the in-depth interpretation of the predictor variables with a nonlinear effect on the dependent variable cost. The penalized maximum likelihood method is adopted to estimate the model parameters. The new regression is a useful statistical tool for analyzing medical data.

A New Truncated Lindley-Generated Family of Distributions: Properties, Regression Analysis, and Applications.

We present the truncated Lindley-G (TLG) model, a novel class of probability distributions with an additional shape parameter, by composing a unit distribution called the truncated Lindley distribution with a parent distribution function G(x). The proposed model's characteristics including critical points, moments, generating function, quantile function, mean deviations, and entropy are discussed. Also, we introduce a regression model based on the truncated Lindley-Weibull distribution considering two systematic components. The model parameters are estimated using the maximum likelihood method. In order to investigate the behavior of the estimators, some simulations are run for various parameter settings, censoring percentages, and sample sizes. Four real datasets are used to demonstrate the new model's potential.

The new Neyman type A generalized odd log-logistic-G-family with cure fraction.

The work proposes a new family of survival models called the Odd log-logistic generalized Neyman type A long-term. We consider different activation schemes in which the number of factors M has the Neyman type A distribution and the time of occurrence of an event follows the odd log-logistic generalized family. The parameters are estimated by the classical and Bayesian methods. We investigate the mean estimates, biases, and root mean square errors in different activation schemes using Monte Carlo simulations. The residual analysis via the frequentist approach is used to verify the model assumptions. We illustrate the applicability of the proposed model for patients with gastric adenocarcinoma. The choice of the adenocarcinoma data is because the disease is responsible for most cases of stomach tumors. The estimated cured proportion of patients under chemoradiotherapy is higher compared to patients undergoing only surgery. The estimated hazard function for the chemoradiotherapy level tends to decrease when the time increases. More information about the data is addressed in the application section.

A competitive family to the Beta and Kumaraswamy generators: Properties, Regressions and Applications.

We define two new flexible families of continuous distributions to fit real data by compoun-ding the Marshall-Olkin class and the power series distribution. These families are very competitive to the popular beta and Kumaraswamy generators. Their densities have linear representations of exponentiated densities. In fact, as the main properties of thirty five exponentiated distributions are well-known, we can easily obtain several properties of about three hundred fifty distributions using the references of this article and five special cases of the power series distribution. We provide a package implemented in R software that shows numerically the precision of one of the linear representations. This package is useful to calculate numerical values for some statistical measurements of the generated distributions. We estimate the parameters by maximum likelihood. We define a regression based on one of the two families. The usefulness of a generated distribution and the associated regression is proved empirically.

Modelling non-proportional hazard for survival data with different systematic components

We propose a new extended regression model based on the logarithm of the generalized odd log-logistic Weibull distribution with four systematic components for the analysis of survival data. This regression model can be very useful and could give more realistic fits than other special regression models. We obtain the maximum likelihood estimates of the model parameters for censored data and address influence diagnostics and residual analysis. We prove empirically the importance of the proposed regression by means of a real data set (survival times of the captive snakes) from a study carried out at the Herpetology Laboratory of the Butantan Institute in São Paulo, Brazil.

Predicting survival function and identifying associated factors in patients with renal insufficiency in the metropolitan area of Maringá, Paraná State, Brazil.

Renal insufficiency is a serious medical and public health problem worldwide. Recently, although many surveys have been developed to identify factors related to the lifetime of patients with renal insufficiency, controversial results from several studies suggest that researches should be conducted by region. Thus, in this study we aim to predict and identify factors associated with the lifetime of patients with chronic renal failure (CRF) in the metropolitan area of Maringá, Paraná State, Brazil, based on the generalized additive models for location, scale and shape (GAMLSS) framework. Data used in this study were collected from the Maringá Kidney Institute and comprehends 177 patients (classified with CRF and mostly being treated under the Brazilian Unified National Health System) enrolled in a hemodialysis program from 1978 up to 2010. By using this approach, we concluded that in other regions, gender, kidney transplant indicator, antibodies to hepatitis B and antibodies to hepatitis C are significant factors that affect the expected lifetime.

Predicting survival function and identifying associated factors in patients with renal insufficiency in the metropolitan area of Maringá, Paraná State, Brazil / A predição da função de sobrevivência e identificação de fatores associados em pacientes renais crônicos na área metropolitana de Maringá, Paraná, Brasil / Prediciendo la esperanza de vida e identificando factores asociados en pacientes con insuficiencia renal en el área metropolitana de Maringá, Estado de Paraná, Brasil

Renal insufficiency is a serious medical and public health problem worldwide. Recently, although many surveys have been developed to identify factors related to the lifetime of patients with renal insufficiency, controversial results from several studies suggest that researches should be conducted by region. Thus, in this study we aim to predict and identify factors associated with the lifetime of patients with chronic renal failure (CRF) in the metropolitan area of Maringá, Paraná State, Brazil, based on the generalized additive models for location, scale and shape (GAMLSS) framework. Data used in this study were collected from the Maringá Kidney Institute and comprehends 177 patients (classified with CRF and mostly being treated under the Brazilian Unified National Health System) enrolled in a hemodialysis program from 1978 up to 2010. By using this approach, we concluded that in other regions, gender, kidney transplant indicator, antibodies to hepatitis B and antibodies to hepatitis C are significant factors that affect the expected lifetime.

A insuficiência renal crônica é um grave problema clínico e de saúde pública no mundo inteiro. Recentemente, apesar de muitas pesquisas já realizadas para identificar os fatores relacionados à evolução dos pacientes renais crônicos, os resultados conflitantes entre diversos estudos sugerem a necessidade de pesquisas por região. Portanto, o estudo busca predizer e identificar os fatores associados à evolução dos pacientes com insuficiência renal crônica (IRC) na área metropolitana de Maringá, Estado do Paraná, Brasil, com base nos modelos aditivos generalizados para localização, escala e forma (GAMLSS). Os dados utilizados neste estudo foram coletados no Instituto do Rim de Maringá e incluem 177 pacientes (classificados com IRC, a maioria tratada no Sistema Único de Saúde) inclusos no programa de hemodiálise entre 1978 e 2010. Através dessa abordagem, concluímos que em outras regiões, gênero, indicação para transplante renal e anticorpos aos vírus das hepatites B e C são fatores significativos que afetam a sobrevivência esperada.

La insuficiencia renal representa un problema médico y de salud pública serio en todo el mundo. Recientemente, pese a los muchos estudios que se han desarrollado para identificar factores relacionados con la esperanza de vida de los pacientes con insuficiencia renal, los resultados controvertidos de algunos estudios sugieren que las investigaciones deberían realizarse por regiones. No obstante, en este trabajo pretendemos predecir e identificar los factores asociados con la esperanza de vida de pacientes con fallo renal crónico (FRC) en el área metropolitana de Maringá, Estado de Paraná, Brasil, basado en el marco de modelos aditivos generalizados para ubicación, escala y forma (GAMLSS por sus siglas en inglés). Los datos usados en este estudio fueron recogidos del Instituto del riñón de Maringá y comprende a 177 pacientes (clasificados con FRC y en su mayoría siendo tratados en el Sistema Único de Salud), inscritos en un programa de hemodiálisis desde 1978 hasta 2010. Usando este enfoque, concluimos que en otras regiones, género, indicador de trasplante de riñón, anticuerpos a la hepatitis B y anticuerpos a la hepatitis C son factores significativos que afectan a la esperanza de vida.

Longitudinal evaluation of the impact of dental caries treatment on oral health-related quality of life among schoolchildren.

This study aimed to evaluate the impact of dental caries treatment on oral health-related quality of life (OHRQoL) among schoolchildren and the responsiveness of the Child Perceptions Questionnaire (CPQ8-10 ) instrument. Brazilian schoolchildren, 8-10 yr of age, were randomly selected and assigned to two groups--dental caries treatment (DCT) and caries-free (CF)--according to their caries experience [decayed, missing, or filled primary teeth (dmft) and decayed, missing or filled secondary teeth (DMFT) values of ≥ 0]. The CPQ8-10 instrument was administered at baseline and at 4 wk of follow-up (i.e. 4 wk after completion of dental treatment). In the DCT group, increases in CPQ8-10 scores were observed between the baseline and follow-up results. However, longitudinal evaluation of the CF group demonstrated no statistically significant difference in CPQ8-10 scores. Responsiveness of the CPQ8-10 instrument (magnitude of change in CPQ8-10 scores) in the DCT group was greater (effect size >0.7) than in the CF group. The findings of this study show that dental caries treatment has an important impact on OHRQoL of children. The CPQ8-10 was considered an acceptable instrument for longitudinal measurement of changes in OHRQoL.

A power series beta Weibull regression model for predicting breast carcinoma.

The postmastectomy survival rates are often based on previous outcomes of large numbers of women who had a disease, but they do not accurately predict what will happen in any particular patient's case. Pathologic explanatory variables such as disease multifocality, tumor size, tumor grade, lymphovascular invasion, and enhanced lymph node staining are prognostically significant to predict these survival rates. We propose a new cure rate survival regression model for predicting breast carcinoma survival in women who underwent mastectomy. We assume that the unknown number of competing causes that can influence the survival time is given by a power series distribution and that the time of the tumor cells left active after the mastectomy for metastasizing follows the beta Weibull distribution. The new compounding regression model includes as special cases several well-known cure rate models discussed in the literature. The model parameters are estimated by maximum likelihood. Further, for different parameter settings, sample sizes, and censoring percentages, some simulations are performed. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess local influences. The potentiality of the new regression model to predict accurately breast carcinoma mortality is illustrated by means of real data.

The log-Burr XII regression model for grouped survival data.

The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data.

The beta modified Weibull distribution.

A five-parameter distribution so-called the beta modified Weibull distribution is defined and studied. The new distribution contains, as special submodels, several important distributions discussed in the literature, such as the generalized modified Weibull, beta Weibull, exponentiated Weibull, beta exponential, modified Weibull and Weibull distributions, among others. The new distribution can be used effectively in the analysis of survival data since it accommodates monotone, unimodal and bathtub-shaped hazard functions. We derive the moments and examine the order statistics and their moments. We propose the method of maximum likelihood for estimating the model parameters and obtain the observed information matrix. A real data set is used to illustrate the importance and flexibility of the new distribution.

Generalized log-gamma regression models with cure fraction.

In this paper, the generalized log-gamma regression model is modified to allow the possibility that long-term survivors may be present in the data. This modification leads to a generalized log-gamma regression model with a cure rate, encompassing, as special cases, the log-exponential, log-Weibull and log-normal regression models with a cure rate typically used to model such data. The models attempt to simultaneously estimate the effects of explanatory variables on the timing acceleration/deceleration of a given event and the surviving fraction, that is, the proportion of the population for which the event never occurs. The normal curvatures of local influence are derived under some usual perturbation schemes and two martingale-type residuals are proposed to assess departures from the generalized log-gamma error assumption as well as to detect outlying observations. Finally, a data set from the medical area is analyzed.