Survival Analysis of Hemodialysis Patients

Survival analysis as a collection of statistical procedures for analyzing the data that its outcome variable was the time to occurrence of an event. Kaplan-Meier method is a type of survival analysis technique, this method is often called the Product Limit Method. Chronic Kidney Disease (CKD) became one of the public health problem throughout the world, including Indonesia. The number of hemodialysis patients has increased every year and have an impact on increasing the number of death in General Hospital Ibnu Sina Gresik. This study was determine the survival of hemodialysis patients using Kaplan-Meier analysis techniques. Non-reactive research with a retrospective cohort using the calculations right censoring. 155 population were taken randomly and sample size of 111. Data were collected using a checklist. The estimated survival time of female, adult age, further education, patients work, patients without insurance, patients with normal nutritional status, patients with a history of disease, patient with hypertention and patient with diabetic had a better survival time. The insurance status, nutritional status, hypertension, and diabetes mellitus were significant difference to the survival time (p-value <0.05). It was necessary special treatment for CKD patients through giving information, education to families and patients to maintain healthy lifestyle

Survival Analysis in Patients with Dengue Hemorrhagic Fever (DHF) Using Cox Proportional Hazard Regression

Indonesia is a tropical country that has two seasons: the rainy season and dry season. In the rainy season frequent flooding or puddles of water that could become mosquito breeding and the spread of various diseases, one of which is the dengue fever. Dengue Hemorrhagic Fever (DHF) is the cause of public health problems with a very rapid deployment and can lead to death within a short time. This causes dengue become one of the attractions to be investigated further. This study discusses the survival analysis and the factors that affect the healing rate of dengue patients using Cox proportional hazard regression based on data from the medical records of hospitalized dengue patients at the Jember Klinik Hospital. The results showed that the factors of age, gender, hemoglobin, trombonist, and hematocrit affect the healing rate of DHF patients

Nonparametric survival analysis of epidemic data

This paper develops nonparametric methods for the survival analysis of epidemic data based on contact intervals. The contact interval from person i to person j is the time between the onset of infectiousness in i and infectious contact from i to j, where we define infectious contact as a contact sufficient to infect a susceptible individual. We show that the Nelson-Aalen estimator produces an unbiased estimate of the contact interval cumulative hazard function when who-infects-whom is observed. When who-infects-whom is not observed, we average the Nelson-Aalen estimates from all transmission networks consistent with the observed data using an EM algorithm. This converges to a nonparametric MLE of the contact interval cumulative hazard function that we call the marginal Nelson-Aalen estimate. We study the behavior of these methods in simulations and use them to analyze household surveillance data from the 2009 influenza A(H1N1) pandemic. In an appendix, we show that these methods extend chain-binomial models to continuous time.Comment: 30 pages, 6 figure

On null hypotheses in survival analysis

The conventional nonparametric tests in survival analysis, such as the log-rank test, assess the null hypothesis that the hazards are equal at all times. However, hazards are hard to interpret causally, and other null hypotheses are more relevant in many scenarios with survival outcomes. To allow for a wider range of null hypotheses, we present a generic approach to define test statistics. This approach utilizes the fact that a wide range of common parameters in survival analysis can be expressed as solutions of differential equations. Thereby we can test hypotheses based on survival parameters that solve differential equations driven by cumulative hazards, and it is easy to implement the tests on a computer. We present simulations, suggesting that our tests perform well for several hypotheses in a range of scenarios. Finally, we use our tests to evaluate the effect of adjuvant chemotherapies in patients with colon cancer, using data from a randomised controlled trial

Survival Analysis in LGD Modeling

The paper proposes an application of the survival time analysis methodology to estimations of the Loss Given Default (LGD) parameter. The main advantage of the survival analysis approach compared to classical regression methods is that it allows exploiting partial recovery data. The model is also modified in order to improve performance of the appropriate goodness of fit measures. The empirical testing shows that the Cox proportional model applied to LGD modeling performs better than the linear and logistic regressions. In addition a significant improvement is achieved with the modified “pseudo” Cox LGD model.credit risk, recovery rate, loss given default, correlation, regulatory capital

Computational Methods in Survival Analysis

Survival analysis is widely used in the fields of medical science, pharmaceutics, reliability and financial engineering, and many others to analyze positive random phenomena defined by event occurrences of particular interest. In the reliability field, we are concerned with the time to failure of some physical component such as an electronic device or a machine part. This article briefly describes statistical survival techniques developed recently from the standpoint of statistical computational methods focussing on obtaining the good estimates of distribution parameters by simple calculations based on the first moment and conditional likelihood for eliminating nuisance parameters and approximation of the likelihoods. The method of partial likelihood (Cox, 1972, 1975) was originally proposed from the view point of conditional likelihood for avoiding estimating the nuisance parameters of the baseline hazards for obtaining simple and good estimates of the structure parameters. However, in case of heavy ties of failure times calculating the partial likelihood does not succeed. Then the approximations of the partial likelihood have been studied, which will be described in the later section and a good approximation method will be explained. We believe that the better approximation method and the better statistical model should play an important role in lessening the computational burdens greatly. --