Analysing Survival Data from Clinical Trials and Observational Studies

Author by : Ettore Marubini
Languange Used : en
Release Date : 2004-07-02
Publisher by : John Wiley & Sons

ISBN :

Description : A practical guide to methods of survival analysis for medical researchers with limited statistical experience. Methods and techniques described range from descriptive and exploratory analysis to multivariate regression methods. Uses illustrative data from actual clinical trials and observational studies to describe methods of analysing and reporting results. Also reviews the features and performance of statistical software available for applying the methods of analysis discussed....

Author by : Ettore Marubini
Languange Used : en
Release Date : 2004-07-02
Publisher by : John Wiley & Sons

ISBN :

Description : A practical guide to methods of survival analysis for medical researchers with limited statistical experience. Methods and techniques described range from descriptive and exploratory analysis to multivariate regression methods. Uses illustrative data from actual clinical trials and observational studies to describe methods of analysing and reporting results. Also reviews the features and performance of statistical software available for applying the methods of analysis discussed....

Author by : Mikhail Nikulin
Languange Used : en
Release Date : 2016-04-11
Publisher by : Springer

ISBN :

Description : 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....

Author by : James Carpenter
Languange Used : en
Release Date : 2012-12-19
Publisher by : John Wiley & Sons

ISBN :

Description : A practical guide to analysing partially observed data. Collecting, analysing and drawing inferences from data is central to research in the medical and social sciences. Unfortunately, it is rarely possible to collect all the intended data. The literature on inference from the resulting incomplete data is now huge, and continues to grow both as methods are developed for large and complex data structures, and as increasing computer power and suitable software enable researchers to apply these methods. This book focuses on a particular statistical method for analysing and drawing inferences from incomplete data, called Multiple Imputation (MI). MI is attractive because it is both practical and widely applicable. The authors aim is to clarify the issues raised by missing data, describing the rationale for MI, the relationship between the various imputation models and associated algorithms and its application to increasingly complex data structures. Multiple Imputation and its Application: Discusses the issues raised by the analysis of partially observed data, and the assumptions on which analyses rest. Presents a practical guide to the issues to consider when analysing incomplete data from both observational studies and randomized trials. Provides a detailed discussion of the practical use of MI with real-world examples drawn from medical and social statistics. Explores handling non-linear relationships and interactions with multiple imputation, survival analysis, multilevel multiple imputation, sensitivity analysis via multiple imputation, using non-response weights with multiple imputation and doubly robust multiple imputation. Multiple Imputation and its Application is aimed at quantitative researchers and students in the medical and social sciences with the aim of clarifying the issues raised by the analysis of incomplete data data, outlining the rationale for MI and describing how to consider and address the issues that arise in its application....

Author by : Allan Hackshaw
Languange Used : en
Release Date : 2015-03-02
Publisher by : John Wiley & Sons

ISBN :

Description : A Concise Guide to Observational Studies in Healthcare provides busy healthcare professionals with an easy-to-read introduction and overview to conducting, analysing and assessing observational studies. It is a suitable introduction for anyone without prior knowledge of study design, analysis or conduct as the important concepts are presented throughout the text. It provides an overview to the features of design, analyses and conduct of observational studies, without using mathematical formulae, or complex statistics or terminology and is a useful guide for researchers conducting their own studies, those who participate in studies co-ordinated by others, or who read or review a published report of an observational study. Examples are based on clinical features of people, biomarkers, lifestyle habits and environmental exposures, and evaluating quality of care....

Author by : Xiaoru Wu
Languange Used : en
Release Date : 2011
Publisher by :

ISBN :

Description : This dissertation addresses two problems from novel perspectives. In chapter 2, I propose an empirical likelihood based method to nonparametrically adjust for baseline covariates in randomized clinical trials and in chapter 3, I develop a survival analysis framework for multivariate K-sample problems. (I): Covariate adjustment is an important tool in the analysis of randomized clinical trials and observational studies. It can be used to increase efficiency and thus power, and to reduce possible bias. While most statistical tests in randomized clinical trials are nonparametric in nature, approaches for covariate adjustment typically rely on specific regression models, such as the linear model for a continuous outcome, the logistic regression model for a dichotomous outcome, and the Cox model for survival time. Several recent efforts have focused on model-free covariate adjustment. This thesis makes use of the empirical likelihood method and proposes a nonparametric approach to covariate adjustment....

Author by : Felipe Fregni
Languange Used : en
Release Date : 2018-03-13
Publisher by : Oxford University Press

ISBN :

Description : One of the most crucial skills a clinician, scientist, or student can learn is to create, conduct, and interpret the conclusions of a clinical study. Critical Thinking in Clinical Research teaches these fundamentals in four distinct sections, called "units": the first unit focuses on issues surrounding the design of a study such as population, question selection, randomization, and blinding; Unit 2 presents statistical methods such as analyzing data collected, how to present and discuss the data concisely; the third unit covers practical aspects such as methodology, organizational considerations, principles of trial conduct and reporting; and the final unit delves into study designs, providing the advantages and drawbacks of each design style. Each chapter begins with a short introduction, followed by a hypothetical case that challenges the reader to make decisions, to consider pros and cons of specific approaches, and to evaluate options based on specific conditions. Knowing how to critically read and understand scientific papers and to collect, analyze, and interpret research data, which they in turn can then present in their own scientific manuscript makes this book the perfect resource for anyone looking to contribute to the wealth of scientific and medical inquiry....