ÖZETAmaç: Bebeklerin sağlıklı bir biçimde büyüyüp gelişebilmesinde anne sütünün payı oldukça büyüktür. Anne sütü yeni doğan bebeklerin gelişimini destekleyebilecek besin değeri bakımından tüm yapay besinlerden daha üstün bir özelliğe sahiptir. Anne sütünün besin değerinin yanında emzirme süresi de büyük önem taşımaktadır. Dünya Sağlık Örgütü ve UNICEF bebeklerin doğumdan itibaren ilk 6 ay yalnızca anne sütü ile beslemelerini ve iki yaşına kadar devam edilmesini önermektedir. Bu çalışmada, bebeklerin anne sütü ile beslenme sürelerine etki eden faktörleri belirlemek amaçlanmıştır. Yöntemler: Olgu serisini tanımlayıcı, kesitsel tipte olan bu çalışma KKTC Gazimagusa'da Haziran-Ağustos 2013 tarihleri arasında yapılmıştır. Çalışmaya katılmaya gönüllü olan 187 anne çalışmaya dâhil edilmiştir. Annenin bebeğini emzirme süresini etkileyen faktörleri belirlemek için yaşam çözümlemesi yöntemleri kullanılmıştır. Bulgular: Anne sütü ile beslenmede toplam süre ele alındığında yalnızca anne sütü ile beslenme süresinin, aile tipinin, annenin sigara/alkol kullanımının ve ek katı gıdalara geçme süresinin anne sütü ile beslenme süresini etkileyen önemli faktörler olduğu %95 güven düzeyinde görül-müştür. Yalnızca anne sütü ile beslenme dikkate alındığında ise annenin doğum anındaki sağlık durumu, gebelik haftası, doğum şekli, ailenin yaşadığı yer, aylı gelir, annein eğitim düzeyi, bebeğin doğum ağırlığı, bebeğin ilk emzirilme zamanı, emzik kullanma, ek sıvı gıda ve hazır bebek maması değişkenlerinin sadece anne sütü ile beslenme süresini etkileyen önemli faktörler olduğu %95 güven düzeyinde görülmüştür. Sonuç: Anne sütü ile beslenme süresinin modellenmesi için çoğunlukla lojistik regresyon modeli kullanılırken, bu çalışmada beslenme süresi de çalışmaya dâhil edilerek yaşam çözümlemesi yöntemleri kullanılmıştır. Böylece, daha uygun ve bilgi verici bir model elde edilmiştir.Anahtar kelimeler: Anne sütü ile beslenme, Kaplan-Meier tahmini, yaşam çözümlemesi, KKTC ABSTRACT Objective: Breastfeeding contributes greatly to growing a healthy baby. The nutritional value of breast milk is superior than all artificial food for the development of newborn babies. Besides the nutritional value of breast milk, the duration of breastfeeding is of great importance. The World Health Organization (WHO) and UNICEF suggest exclusive breastfeeding from birth through the first 6 months and continue breastfeeding up to two years. In this study, it is aimed to determine the factors that effect the duration of breasfeeding. Methods: This is a cross-sectional, descriptive study was conducted in Gazimagusa, Turkish Republic of Northern Cyprus between June-August 2013. A hundred and eighty-seven mothers who volunteered to participate were included in this study. Survival analysis was used to clarify the factors that affect the duration of breastfeeding. Results: When overall duration of full breastfeeding was analyzed, duration of exclusive breastfeeding, family type, maternal smoking/alcohol use and starting on additional solid food were found as important risk...

Background In this study, we aimed to investigate the prognostic value of metabolic 18 F-fluorodeoxyglucose positron emission tomography/computed tomography parameters in malignant pleural mesothelioma patients. Methods A total of 65 patients with malignant pleural mesothelioma (34 males, 31 females; median age: 60 years; range, 39 to 84 years) who underwent whole-body 18 F-fluorodeoxyglucose positron emission tomography/computed tomography for staging before treatment between March 2008 and January 2018 were included. Relationships between clinicopathological factors and 18 F-fluorodeoxyglucose positron emission tomography/computed tomography parameters and overall survival were evaluated using a log-rank test and Cox regression analysis. Results The median follow-up was 13 (range, 4 to 55) months. The Kaplan-Meier analysis revealed a mean survival time of 17±2.6 months. The cumulative two- and five-year survival rates were 34.8% and 7.8%, respectively. Univariate analysis showed that ≥60 age, left hemithorax involvement, a maximum standardized uptake value of ≥9.8, c-T4 status, c-M1 status, and non-surgery were negatively associated with overall survival (p<0.05). Multivariate analysis showed that ≥60 age, left hemithorax involvement, a maximum standardized uptake value of ≥9.8, c-M1 status, and a total lesion glycolysis of ≥180.2 g were negatively associated with overall survival (p<0.05). Conclusion Metabolic parameters of 18 F-fluorodeoxyglucose positron emission tomography/computed tomography have the potential to provide prognostic information for malignant pleural mesothelioma patients who are receiving surgery and/or chemotherapy.

Survival analysis has a wide application area from medicine to marketing and Cox model takes an important part in survival analysis. When the distribution of survival data is known or it is appropriate to assume a survival distribution, use of a parametric form of Cox model is employed. In this article, we take into account Cox-Gompertz model from the Bayesian perspective. Considering the difficulties in parameter estimation in classical setting, we propose a simple Bayesian approach for Cox-Gompertz model. We derive full conditional posterior distributions of all parameters in Cox-Gompertz model to run Gibbs sampling. Over an extensive simulation study, estimation accuracies of the classical Cox model and classical and Bayesian settings of Cox-Gompertz model are compared with each other by generating exponential, Weibull, and Gompertz distributed survival data sets. Consequently, if survival data follows Gompertz distribution, most accurate parameter estimates are obtained by the Bayesian setting of Cox-Gompertz model. We also provide a real data analysis to illustrate our approach. In the data analysis, we observe the importance of use of the most accurate model over the survival probabilities of censored observations.

Flexible parametric survival models using cubic splines become popular in survival data analysis. The property of allowing converging hazard functions leads them to be the alternatives to Cox proportional hazards model and parametric survival models. In this study, flexible parametric survival models are applied to the data set of 106 gastric cancer patients. According to this data set, metastasis and muscle contraction are found as important risk factors on survival.

Multivariate models such as the Cox regression model, if developed carefully, are powerful tools for making prognostic prediction which are frequently used in studies of clinical outcomes. Many applications require a large number of variables to be modelled by using a relatively small patient sample. Determination of the important variables in a model is critical to understand the behaviour of phenomena as the independent variables contribute the most to the outcome. From a practical perspective, a small subset of independent variables are usually selected from a large data set without the loss of any predictive efficiency. Automatic variable selection algorithms in scientific studies are commonly used for obtaining interpretable and practically applicable models. However, the careless use of these methods may lead to statistical problems. The performance of the generated models may be poor due to the violation of assumption, omission of the important variables, problems of overfitting, and the problem of multicollinearity and outliers. In order to enhance the accuracy of a model, it is essential to explore the data and its main characteristics before making any statistical inference. This study suggests an approach for acquiring a trustworthy model selection procedure for survival data by performing classical variables selection methods, accompanied by a graphical visualization method, namely robust coplot. Thus, it enables us to investigate the discrimination of observations, clusters of the variables and clusters of the observations that are highly characterized by a particular variable in a one graph. We present an application of combined method, as an integral part of statistical modelling, on survival data on multiple myeloma to show how coplot results are used in automatic variable selection algorithm in Cox regression model-building.