Diagnostik dan Pengaruh Bagi Data Mandirian dalam Risiko Bersaing

Kajian di dalam tesis ini adalah mengenai perkembangan dan lanjutan bagi teknik penilaian diagnostik dan pengaruh dalam data mandirian yang merangkumi cerapan tertapis. Model mandirian yang dianalisis bermula dengan model hayat terpecut dengan risiko tunggal dikaji secara ringkas. Tumpuan analisis ialah ke atas model risiko bersaing termasuk data terkumpul. Diagnostik pengaruh dilakukan berdasarkan kaedah penghapusan kes manakala ukuran skalar untuk menilai pengaruh berpandukan ukuran jarak Cook (1972). Beberapa bentuk ukuran Cook telah dipertimbangkan untuk menilai pengaruh dalam model hayat terpecut yang mana model ini lazim digunakan dengan meluas untuk menganalisis data kebolehpercayaan. Melalui bantuan pakej komputer yang sesuai didapati kesemua ukuran ini memberikan persetujuan ke atas keputusan yang melibatkan cerapan yang dipercayai berpengaruh. Model risiko bersaing yang dikaji secara amnya merupakan suatu model mandirian yang mana penilaian ke atas sesuatu risiko tertentu diselidiki di dalam situasi yang kompleks dengan kehadiran m risiko yang lain. Walau bagaimanapun penekanan di sini adalah ke atas model risiko bersaing dengan kehadiran dua risiko (m = 2) dengan tumpuan kepada kes tapisan sebagai salah satu daripada risiko ini. Di samping itu analisis kepada data terkumpul telah dilaksanakan berdasarkan risiko bersaing dengan m = 2. Proses transformasi telah digunakan bagi menangani masalah penyuaian model risiko bersaing terutamanya bagi data terkumpul dengan kriteria penumpuan tercapai melalui pendekatan lelaran Marquardt

Diagnostik Pengaruh bagi Model Risiko Bersaingan dengan Tapisan Sebagai Satu Risiko

Met-hods of detecting in fluential obseroations for competing risks model with censoring as one of the risk in a two-risks model are proposed. These methods include deletion of obseroations, one·step deletion, Cook distance and likelihood distance. Emphasis is on assessing the impact of an obseroation on the parameter estimation. Two sets of data are used to illusrate these techniques

Analisis Kovariat bagi Data Kemortalan Bayi

This paper analyses the effect of various covariates on infant mortality. It uses the method for the analysis of grouped life-time in competing risks. With this approach, advantage is taken of the probability of an individual with a particular covariate failing in an interval to consolidate the significance of that covariate model

Generating correlated discrete ordinal data using R and SAS IML.

Correlated ordinal data are common in many areas of research. The data may arise from longitudinal studies in biology, medical, or clinical fields. The prominent characteristic of these data is that the within-subject observations are correlated, whilst between-subject observations are independent. Many methods have been proposed to analyze correlated ordinal data. One way to evaluate the performance of a proposed model or the performance of small or moderate size data sets is through simulation studies. It is thus important to provide a tool for generating correlated ordinal data to be used in simulation studies. In this paper, we describe a macro program on how to generate correlated ordinal data based on R language and SAS IML

Cure rate models: a review of recent progress with a study of change-point cure models when cured is partially known

In medicine and public health researches, survival cure models are widely used to analyse time-to-event data in which some subjects are reasonably believed to be medically cured. In general, there are two types of models for estimation of the cure fraction. The first one is the Mixture Cure Model (MCM), which was developed by Boag in 1949. This type of models assumes that the whole population is composed of susceptible subjects and cured subjects. The second cure model type was proposed by Yakovlev et al. (1993) based on the assumption that the treatment leaves the patient with a number of cancer cells, which may grow slowly over time and produce a detectable recurrence of cancer. It is known as the Non-Mixture Cure Model (NMCM). These two models are related and the NMCM can be transformed into the MCM, when the cure fraction specially specified. Different parametric and semi-parametric estimation methods for model parameters in both types have been proposed and many applications of these models have been reported. The extensions of the cure model focus on study of change point effects on the cure or hazard rate. A change point cure model is proposed when cured is partially known

Nonparametric regression for correlated data.

This paper considers nonparametric regression to analyze correlated data. The correlated data could be longitudinal or clustered data. Some developments of nonparametric regression have been achieved for longitudinal or clustered categorical data. For data with exponential family distribution, nonparametric regression for correlated data has been proposed using GEE-Local Polynomial Kernel (LPK). It was showed that in order to obtain an efficient estimator, one must ignore within subject correlation. This means within subject observations should be assumed independent, hence the working correlation matrix must be an identity matrix. Thus to obtained efficient estimates we should ignore correlation that exist in longitudinal data, even if correlation is the interest of study. In this paper we propose GEE-Smoothing spline to analyze correlated data and study the properties of the estimator such as the bias, consistency and efficiency. We use natural cubic spline and combine with GEE in estimation. We want to study numerically, whether the properties of GEE-Smoothing spline are better than of GEE-Local Polynomial Kernel. Several conditions have been considered. i.e. several sample sizes and several correlation structures. Using simulation we show that GEE-Smoothing Spline is better than GEE-local polynomial. The bias of pointwise estimator is decreasing with increasing sample size. The pointwise estimator is also consistent even with incorrect correlation structure, and the most efficient estimate is obtained if the true correlation structure is used. We also give example using real data, and compared the result of the proposed method with parametric method and GEE-Smoothing Spline under independent assumption

Maximum Likelihood Performance of Mean Time to Failure for Right-Censored Data

In this paper failure times following Weibull, exponential and log-normal distribution are considered. The parameters of these distributions are estimated by the maximum likelihood method and these values are used to estimate other quantity of interest such as Mean Time to Failure (MTTF), an important function in a reliability analysis. This study is to look at the performance of maximum likelihood estimate (MLE) under various conditions by considering varying sample size and percentage of censored data. The performance is quantified from the study

Approximate bayesian estimates of weibull parameters with Lindley’s method

One of the most important lifetime distributions that is used for modelling and analysing data in clinical, life sciences and engineering is the Weibull distribution. The main objective of this paper was to determine the best estimator for the two-parameter Weibull distribution. The methods under consideration are the frequentist maximum likelihood estimator, least square regression estimator and the Bayesian estimator by using two loss functions, which are squared error and linear exponential. Lindley approximation is used to obtain the Bayes estimates. Comparisons are made through simulation study to determine the performance of these methods. Based on the results obtained from this simulation study the Bayesian approach used in estimating the Weibull parameters under linear exponential loss function is found to be superior as compared to the conventional maximum likelihood and least squared methods

Comparison on modelling the relative risk estimation: Bayesian study

The estimation of the disease incidents was previously analyzed using a classical approach. However, this approach features large outlying relative risks and considered as misleading due to several major problems. Some approaches such as the hierarchical Bayesian method have been adopted in the literature in order to overcome these problems. The purpose of this study is to compare between hierarchical Bayesian models that improve the relative risk estimation. The focus lies on examining the performance of different sets of densities via monitoring the history graphs, estimating the potential scale reduction factors and conducting sensitivity analysis for different choice of prior information. The best model fit is accomplished by conducting a goodness of fit test. The study is applied on Scotland lip cancer data set. The results show that for models with large number of parameters, more iteration is needed to achieve the convergence. The study also shows that diagnostic test and sensitivity analysis are important to decide about the stability and the the influence of the choice of the prior densities. The DIC results were in line with the previous results and provide a good method of comparison

Parametric tests for partly interval-censored failure time data under Weibull distribution via multiple imputation

The statistical problem considered in this article is the parametric treatment comparison when partly interval-censored failure time data exist. Partly interval-censored failure time data are composed of exact observations and interval-censored observations. This phenomenon often occurs in clinical trials and health studies that require periodic following up with patients. The authors constructed a score test and likelihood ratio test for this type of failure time data under Weibull distributions using multiple imputation technique. A simulation study and a modified secondary data set from breast cancer study are used to assess the proposed test and illustrate the differences between the two tests. The results indicate that the presented procedure works well for both tests, but the likelihood ratio test is better than the score test in certain situations