Health-related factors on tobacco consumption in Turkey / Fatores relacionados com a saúde e o consumo de tabaco na Turquia

ABSTRACT: In this study, we analyzed the role of individuals' health-related factors along with socio-demographic and economic characteristics on both the likelihood of tobacco consumption and quantity demanded levels using two competitive econometric methods: double hurdle model versus hyperbolic sine double-hurdle model. Statistical tests confirmed the dependency errors between the prevalence rate of smoking and the consumption level, whilst the inverse-hyperbolic sine double-hurdle model data fits best in describing the normalization of the data and the two data generating processes: the probability and consumption levels of cigarettes. Also, the variance-covariance of the selected model as a function of additional exogenous variables are confirmed, while the error terms between the likelihood to smoke and the consumption levels are positive and statistically significant, indicating that holding control variables fixed, the uncontrolled variables out of the system that increase the prevalence rate of smoking also boost the consumption level, or vice versa. Many individual disease variables are significant in both equations, breaking new grounds in literature for identifying how both the prevalence rate of smoking and amount have shaped.

RESUMO: Neste estudo, analisamos o papel dos fatores relacionados à saúde dos indivíduos, juntamente com as características sócio-demográficas e econômicas, tanto na probabilidade de consumo de tabaco quanto nos níveis de quantidade demandada, usando dois métodos econométricos competitivos: modelo de obstáculo duplo versus modelo de obstáculo duplo seno hiperbólico. Os testes estatísticos confirmaram os erros de dependência entre a taxa de prevalência de tabagismo e o nível de consumo, enquanto o modelo de seno duplo inverso-hiperbólico se ajusta melhor aos dados para descrever a normalização dos dados e os dois processos geradores de dados: os níveis de probabilidade e consumo de cigarros. Também são confirmadas a covariância de variância do modelo selecionado em função de variáveis exógenas adicionais, enquanto os termos de erro entre a probabilidade de fumar e os níveis de consumo são positivos e estatisticamente significativos, indicando que, mantendo variáveis de controle fixas, as variáveis não controladas são do sistema que aumenta a taxa de prevalência do tabagismo e também cresce o nível de consumo, ou vice-versa. Muitas variáveis individuais da doença são encontradas significativamente em ambas as equações, abrindo novos caminhos na literatura para identificar como a taxa de prevalência de tabagismo e a quantidade se moldaram.

Review of statistical methods for survival analysis using genomic data

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains “censored” data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing “omics” data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.

An imputation-based method to reduce bias in model parameter estimates due to non-random censoring in oncology trials

In oncology trials, patients are withdrawn from study at the time when progressive disease (PD) is diagnosed, which is defined as 20% increase of tumor size from the minimum. Such informative censoring can lead to biased parameter estimates when nonlinear mixed effects models are fitted using NONMEM. In this work, we investigated how empirical Bayes estimates (EBE) could be exploited to impute missing tumor size observations and partially correct biases in the parameter estimates. 50 simulated datasets, each consisting of 100 patients, were generated based on the published model. From the simulated dataset, censoring due to PD diagnosis has been implemented. Using the post-hoc EBEs acquired from fitting the censored datasets using NONMEM, imputed values were generated from the tumor size model. Model fitting was carried out using censored and imputed datasets. Parameter estimates using both datasets were compared with true values. Tumor growth rate and cell kill rate were approximately 28% and 16% underestimated when fitted using the censored dataset, respectively. With the imputed datasets, relative biases of tumor growth rate and cell kill rate decreased to about 6% and 0%, respectively. Our work demonstrates that using EBEs acquired from fitting the model to the censored dataset and imputing the unknown tumor size observations with individual predictions beyond the PD time point is a viable option to solve the bias associated with structural parameter estimates. This approach, however, would not be helpful in getting better estimates of variance parameters.

Estimating a Hazard Function for Each of Four Items of Adverse Event Induced by the Anti-cancer Drug TS-1 / 薬剤疫学

Background : The safety of newly approved drugs must be assessed using postmarketing surveillance data. One of the difficulties in assessing the hazard rates of adverse events induced by the anti-cancer drug TS-1 was that the time to event was not exactly identified due to the interval censoring. Most patients were outpatients who underwent clinical laboratory tests almost periodically at 1- or 2-week intervals and therefore, the occurrence of an adverse event was confirmed at the time of testing days after the event occurrence.<BR>Objective : The purpose of this study was to propose a new model of hazard functions for each of 4 items of adverse event induced by TS-1 using post-marketing surveillance data considering the interval censoring.<BR>Methods : The data obtained from 3, 294 patients with gastric cancer who received an initial 4-week course of therapy with TS-1 administered orally twice a day, followed by a 4-week second course with a 2-week no-treatment period after the initial course, were used to estimate hazard functions. Four items of adverse event--hemoglobin level (HB), white blood cell (WBC), neutrophil (NEUT) and platelet counts (PLT) --were graded, respectively, using the criteria established by the Japan Society of Clinical Oncology. Slip-mixed log-logistic and slip-mixed Weibull models were proposed as candidate models for estimating hazard functions. The goodness of fit of the two candidate models was evaluated by applying them to the above-mentioned data. The hazard functions for each of 4 items were assessed using the model with the better fit.<BR>Results : The initial occurrence of adverse event was shown to follow the slip-mixed log-logistic model for each of 4 items. Although most events occurred early on in the initial course of therapy, a small peak in HB was also observed in the second course, while no such peak appeared for the other items.