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Application Of The Empirical Likelihood Method In Proportional Hazards Model
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Book Synopsis Application of the Empirical Likelihood Method in Proportional Hazards Model by : Bin He
Download or read book Application of the Empirical Likelihood Method in Proportional Hazards Model written by Bin He and published by . This book was released on 2006 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: Key words: Bootstrap, confidence interval, Cox model, doubly censored data, empirical likelihood function, goodness-of-fit test, maximum likelihood, partly interval-censored data, proportional hazards model, right censored data, survival analysis.
Book Synopsis Empirical Likelihood Method in Survival Analysis by : Mai Zhou
Download or read book Empirical Likelihood Method in Survival Analysis written by Mai Zhou and published by CRC Press. This book was released on 2015-06-17 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: Empirical Likelihood Method in Survival Analysis explains how to use the empirical likelihood method for right censored survival data. The author uses R for calculating empirical likelihood and includes many worked out examples with the associated R code. The datasets and code are available for download on his website and CRAN. The book focuses on all the standard survival analysis topics treated with empirical likelihood, including hazard functions, cumulative distribution functions, analysis of the Cox model, and computation of empirical likelihood for censored data. It also covers semi-parametric accelerated failure time models, the optimality of confidence regions derived from empirical likelihood or plug-in empirical likelihood ratio tests, and several empirical likelihood confidence band results. While survival analysis is a classic area of statistical study, the empirical likelihood methodology has only recently been developed. Until now, just one book was available on empirical likelihood and most statistical software did not include empirical likelihood procedures. Addressing this shortfall, this book provides the functions to calculate the empirical likelihood ratio in survival analysis as well as functions related to the empirical likelihood analysis of the Cox regression model and other hazard regression models.
Book Synopsis Empirical Likelihood by : Art B. Owen
Download or read book Empirical Likelihood written by Art B. Owen and published by CRC Press. This book was released on 2001-05-18 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Empirical likelihood provides inferences whose validity does not depend on specifying a parametric model for the data. Because it uses a likelihood, the method has certain inherent advantages over resampling methods: it uses the data to determine the shape of the confidence regions, and it makes it easy to combined data from multiple sources. It al
Book Synopsis Contemporary Multivariate Analysis and Design of Experiments by : Kaitai Fang
Download or read book Contemporary Multivariate Analysis and Design of Experiments written by Kaitai Fang and published by World Scientific. This book was released on 2005 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: Index. Subject index -- Author index
Book Synopsis Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life by : M.S. Nikulin
Download or read book Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life written by M.S. Nikulin and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parametric and semiparametric models are tools with a wide range of applications to reliability, survival analysis, and quality of life. This self-contained volume examines these tools in survey articles written by experts currently working on the development and evaluation of models and methods. While a number of chapters deal with general theory, several explore more specific connections and recent results in "real-world" reliability theory, survival analysis, and related fields. Specific topics covered include: * cancer prognosis using survival forests * short-term health problems related to air pollution: analysis using semiparametric generalized additive models * semiparametric models in the studies of aging and longevity This book will be of use as a reference text for general statisticians, theoreticians, graduate students, reliability engineers, health researchers, and biostatisticians working in applied probability and statistics.
Book Synopsis Proportional Hazards Regression by : John O'Quigley
Download or read book Proportional Hazards Regression written by John O'Quigley and published by Springer Science & Business Media. This book was released on 2008-01-25 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The place in survival analysis now occupied by proportional hazards models and their generalizations is so large that it is no longer conceivable to offer a course on the subject without devoting at least half of the content to this topic alone. This book focuses on the theory and applications of a very broad class of models – proportional hazards and non-proportional hazards models, the former being viewed as a special case of the latter – which underlie modern survival analysis. Researchers and students alike will find that this text differs from most recent works in that it is mostly concerned with methodological issues rather than the analysis itself.
Book Synopsis Penalized Empirical Likelihood Based Variable Selection by : Tharshanna Nadarajah
Download or read book Penalized Empirical Likelihood Based Variable Selection written by Tharshanna Nadarajah and published by . This book was released on 2011 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Empirical Likelihood and Extremes by : Yun Gong
Download or read book Empirical Likelihood and Extremes written by Yun Gong and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In 1988, Owen introduced empirical likelihood as a nonparametric method for constructing confidence intervals and regions. Since then, empirical likelihood has been studied extensively in the literature due to its generality and effectiveness. It is well known that empirical likelihood has several attractive advantages comparing to its competitors such as bootstrap: determining the shape of confidence regions automatically using only the data; straightforwardly incorporating side information expressed through constraints; being Bartlett correctable. The main part of this thesis extends the empirical likelihood method to several interesting and important statistical inference situations. This thesis has four components. The first component (Chapter II) proposes a smoothed jackknife empirical likelihood method to construct confidence intervals for the receiver operating characteristic (ROC) curve in order to overcome the computational difficulty when we have nonlinear constrains in the maximization problem. The second component (Chapter III and IV) proposes smoothed empirical likelihood methods to obtain interval estimation for the conditional Value-at-Risk with the volatility model being an ARCH/GARCH model and a nonparametric regression respectively, which have applications in financial risk management. The third component(Chapter V) derives the empirical likelihood for the intermediate quantiles, which plays an important role in the statistics of extremes. Finally, the fourth component (Chapter VI and VII) presents two additional results: in Chapter VI, we present an interesting result by showing that, when the third moment is infinity, we may prefer the Student's t-statistic to the sample mean standardized by the true standard deviation; in Chapter VII, we present a method for testing a subset of parameters for a given parametric model of stationary processes.
Book Synopsis Likelihood Methods in Survival Analysis by : Jun Ma
Download or read book Likelihood Methods in Survival Analysis written by Jun Ma and published by CRC Press. This book was released on 2024-10-01 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many conventional survival analysis methods, such as the Kaplan-Meier method for survival function estimation and the partial likelihood method for Cox model regression coefficients estimation, were developed under the assumption that survival times are subject to right censoring only. However, in practice, survival time observations may include interval-censored data, especially when the exact time of the event of interest cannot be observed. When interval-censored observations are present in a survival dataset, one generally needs to consider likelihood-based methods for inference. If the survival model under consideration is fully parametric, then likelihood-based methods impose neither theoretical nor computational challenges. However, if the model is semi-parametric, there will be difficulties in both theoretical and computational aspects. Likelihood Methods in Survival Analysis: With R Examples explores these challenges and provides practical solutions. It not only covers conventional Cox models where survival times are subject to interval censoring, but also extends to more complicated models, such as stratified Cox models, extended Cox models where time-varying covariates are present, mixture cure Cox models, and Cox models with dependent right censoring. The book also discusses non-Cox models, particularly the additive hazards model and parametric log-linear models for bivariate survival times where there is dependence among competing outcomes. Features Provides a broad and accessible overview of likelihood methods in survival analysis Covers a wide range of data types and models, from the semi-parametric Cox model with interval censoring through to parametric survival models for competing risks Includes many examples using real data to illustrate the methods Includes integrated R code for implementation of the methods Supplemented by a GitHub repository with datasets and R code The book will make an ideal reference for researchers and graduate students of biostatistics, statistics, and data science, whose interest in survival analysis extend beyond applications. It offers useful and solid training to those who wish to enhance their knowledge in the methodology and computational aspects of biostatistics.
Book Synopsis The Frailty Model by : Luc Duchateau
Download or read book The Frailty Model written by Luc Duchateau and published by Springer Science & Business Media. This book was released on 2007-10-23 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: Readers will find in the pages of this book a treatment of the statistical analysis of clustered survival data. Such data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health and demography. A typical example is the time to death in cancer patients, with patients clustered in hospitals. Frailty models provide a powerful tool to analyze clustered survival data. In this book different methods based on the frailty model are described and it is demonstrated how they can be used to analyze clustered survival data. All programs used for these examples are available on the Springer website.
Book Synopsis Proportional Hazards Regression Model with Unknown Link Function and Applications to Longitudinal Time-to-event Data by : Wei Wang
Download or read book Proportional Hazards Regression Model with Unknown Link Function and Applications to Longitudinal Time-to-event Data written by Wei Wang and published by . This book was released on 2001 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Cox Model and Its Applications by : Mikhail Nikulin
Download or read book The Cox Model and Its Applications written by Mikhail Nikulin and published by Springer. This book was released on 2016-04-11 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book will be of interest to readers active in the fields of survival analysis, genetics, ecology, biology, demography, reliability and quality control. Since Sir David Cox’s pioneering work in 1972, the proportional hazards model has become the most important model in survival analysis. The success of the Cox model stimulated further studies in semiparametric and nonparametric theories, counting process models, study designs in epidemiology, and the development of many other regression models that could offer more flexible or more suitable approaches in data analysis. Flexible semiparametric regression models are increasingly being used to relate lifetime distributions to time-dependent explanatory variables. Throughout the book, various recent statistical models are developed in close connection with specific data from experimental studies in clinical trials or from observational studies.
Book Synopsis APPLICATIONS OF EMPIRICAL LIKELIHOOD TO ZERO-INFLATED DATA AND EPIDEMIC CHANGE POINT by : Junvie Montealto Pailden
Download or read book APPLICATIONS OF EMPIRICAL LIKELIHOOD TO ZERO-INFLATED DATA AND EPIDEMIC CHANGE POINT written by Junvie Montealto Pailden and published by . This book was released on 2013 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many studies in health care deal with zero-inflated data sets characterized by a significant proportion of zero and highly skewed positive values. Although it is a common practice to use the median instead of the mean as the measure of central location in skewed data, many applications require the use of the mean. For instance, the mean can be used to recover the total medical cost which reflects the entire expenditure on health care in a given patient population. For testing the value of a mean, the empirical likelihood method offers the benefit of making no distributional assumptions beyond some mild moment conditions while retaining the same advantages that parametric likelihood based tests enjoyed. In this dissertation, we proposed an empirical likelihood ratio test for the difference between means of two zero-inflated samples. The proposed test was derived by jointly specifying the empirical likelihood for the mean parameter and the probability of taking zero value in the data. There are two unique features in this procedure. One is that the information contained in the zero observations is fully utilized and that the proposed test is insensitive to the skewness of the non-zero observations. We derive an asymptotic distribution that will be used to calibrate the statistic in testing the null hypothesis of no mean difference. We also extend the procedure to testing the mean equality of several independent zero-inflated populations. As a benchmark for comparison against conventional tests, we investigate the empirical type 1 error and power rates in finite sample settings. Both the proposed two sample test for the mean difference and the equality of means between three or more populations exhibits comparable if not superior finite sample performance. Another application of empirical likelihood approach that we consider is on detecting epidemic change point in a sequence of observations. Under some mild conditions, the asymptotic null distribution of the test statistic is showed to be an extreme distribution. Simulations indicate that the proposed test performs at par if not better than other available tests while enjoying less constraint on the data distribution.
Book Synopsis Empirical Likelihood Methods in Nonignorable Covariate-missing Data Problems by : Yanmei Xie
Download or read book Empirical Likelihood Methods in Nonignorable Covariate-missing Data Problems written by Yanmei Xie and published by . This book was released on 2019 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing covariate data occurs often in regression analysis, which frequently arises in the health and social sciences as well as in survey sampling. This dissertation contains three topics in nonignorable covariate-missing data problems, in which we study methods for the analysis of a nonignorable covariate-missing data problem in an assumed conditional mean function when some covariates are completely observed but other covariates are missing for some subjects. First, by exploitation of a probability model of missingness and a working conditional score model from a semiparametric perspective, we propose a unified approach to constructing a system of unbiased estimating equations, where there are more equations than unknown parameters of interest. These unbiased estimating equations naturally incorporate the incomplete data into the data analysis, making it possible to seek efficient estimation of the parameter of interest even when the working regression function is not specified to be the optimal regression function. Based on the proposed estimating equations, we introduce three maximum empirical likelihood estimators of the underlying regression parameters and compare their efficiencies with other existing competitors. By utilizing the proposed empirical likelihood method on a data set from the US National Health and Nutrition Examination Survey (NHANES), we study the effect of daily alcohol consumption on hypertension. Second, we explore unconstrained and constrained empirical likelihood ratio statistics to construct empirical likelihood confidence regions for the underlying regression parameters without and with constraints. We establish the asymptotic distributions of the proposed empirical likelihood ratio statistics. The proposed empirical likelihood methods have a better finite-sample performance than other existing competitors in terms of coverage probability and interval length. An analysis on the data set from the US NHANES demonstrates that increased alcohol consumption per day is significantly associated with increased systolic blood pressure. In addition, higher body mass index and older age have a significantly higher risk of hypertension. Third, we propose a pseudo empirical likelihood ratio statistic, yet it is demonstrated following an asymptotically chi-squared distribution. Our proposed method allows for confidence interval construction without variance estimation and thus is more computationally feasible. Simulation results suggest that the proposed empirical likelihood confidence interval has a better finite-sample performance than the corresponding Wald-based competitor in terms of coverage probability and interval length. Moreover, the proposed empirical likelihood ratio test is always superior to the Wald method in terms of their power performances in our simulation studies.
Book Synopsis Adjusted Empirical Likelihood Method for Comparison of Treatment Effects in Linear Model Setting by : Kang, Xi
Download or read book Adjusted Empirical Likelihood Method for Comparison of Treatment Effects in Linear Model Setting written by Kang, Xi and published by . This book was released on 2016 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Estimation of Markov Decision Processes in the Presence of Model Uncertainty by : Eldar A. Nigmatullin
Download or read book Estimation of Markov Decision Processes in the Presence of Model Uncertainty written by Eldar A. Nigmatullin and published by . This book was released on 2003 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials by : Mark Chang
Download or read book Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials written by Mark Chang and published by CRC Press. This book was released on 2019-03-20 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This is truly an outstanding book. [It] brings together all of the latest research in clinical trials methodology and how it can be applied to drug development.... Chang et al provide applications to industry-supported trials. This will allow statisticians in the industry community to take these methods seriously." Jay Herson, Johns Hopkins University The pharmaceutical industry's approach to drug discovery and development has rapidly transformed in the last decade from the more traditional Research and Development (R & D) approach to a more innovative approach in which strategies are employed to compress and optimize the clinical development plan and associated timelines. However, these strategies are generally being considered on an individual trial basis and not as part of a fully integrated overall development program. Such optimization at the trial level is somewhat near-sighted and does not ensure cost, time, or development efficiency of the overall program. This book seeks to address this imbalance by establishing a statistical framework for overall/global clinical development optimization and providing tactics and techniques to support such optimization, including clinical trial simulations. Provides a statistical framework for achieve global optimization in each phase of the drug development process. Describes specific techniques to support optimization including adaptive designs, precision medicine, survival-endpoints, dose finding and multiple testing. Gives practical approaches to handling missing data in clinical trials using SAS. Looks at key controversial issues from both a clinical and statistical perspective. Presents a generous number of case studies from multiple therapeutic areas that help motivate and illustrate the statistical methods introduced in the book. Puts great emphasis on software implementation of the statistical methods with multiple examples of software code (both SAS and R). It is important for statisticians to possess a deep knowledge of the drug development process beyond statistical considerations. For these reasons, this book incorporates both statistical and "clinical/medical" perspectives.
