Statistical Methods For Incomplete Covariates And Two Phase Designs
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Statistical Methods For Incomplete Covariates And Two Phase Designs
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Author : Michael McIsaac
language : en
Publisher:
Release Date : 2013
Statistical Methods For Incomplete Covariates And Two Phase Designs written by Michael McIsaac and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Advances In Incomplete Data Methods For Statistical Genetics
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Author : Osvaldo Espin Garcia
language : en
Publisher:
Release Date : 2019
Advances In Incomplete Data Methods For Statistical Genetics written by Osvaldo Espin Garcia and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Handling incomplete or missing data is a common aspect of modern statistical methods. In this thesis, I focus on novel solutions for current problems pertaining to statistical genetics that can be framed in the context of incomplete data. In particular, three methods are proposed that are underpinned by the EM algorithm, arguably the most common yet powerful approach to make statistical inference in the presence of incomplete data. First, motivated by developments in metagenomic sequencing data and the apparent systematic exclusion of the X-chromosome in genetic studies, I propose a finite mixture regression model (FMM) that infers association between a genetic variant in the X-chromosome and an operational taxonomic unit. The proposed FMM accounts for complex biological mechanisms governing the X-chromosome, namely X-chromosome inactivation (XCI), escape of XCI, and skewed or non-random XCI by introducing an latent indicator variable. Second, I investigate two-phase sampling designs in post-genome-wide association studies (GWASs), which can be a cost-effective strategy to infer association between missing-by-design sequencing data and a trait of interest. In phase 1, given a GWAS-identified genetic region, a subsample is selected for which expensive targeted-sequence data are collected using outcome measures and auxiliary covariates to inform the sampling. Phase 2 consists of performing inference using combined phase 1 2 data. In particular, I develop semi-parametric maximum likelihood for analysis and examine heuristic allocations to select the phase 2 subsample. Lastly, derived from the second project, I propose two strategies that aim to optimally select subjects for sequencing when the subsample size is fixed due to budget constraints. These strategies aim to minimize features of the variance-covariance matrix based on predefined optimality criteria found in previous literature and common to experimental design. The proposed strategies implement such minimization via (1) Lagrange multipliers and (2) a genetic algorithm. In all cases, the proposed methods are evaluated through comprehensive simulation studies. In addition, I illustrate their usage in real genetic studies or realistic synthetic data in absence thereof. Overall, the methods perform satisfactorily, moreover, these findings can serve as the basis of further research.
Statistical Design Monitoring And Analysis Of Clinical Trials
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Author : Weichung Joe Shih
language : en
Publisher: CRC Press
Release Date : 2021-10-26
Statistical Design Monitoring And Analysis Of Clinical Trials written by Weichung Joe Shih and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-26 with Medical categories.
Statistical Design, Monitoring, and Analysis of Clinical Trials, Second Edition concentrates on the biostatistics component of clinical trials. This new edition is updated throughout and includes five new chapters. Developed from the authors’ courses taught to public health and medical students, residents, and fellows during the past 20 years, the text shows how biostatistics in clinical trials is an integration of many fundamental scientific principles and statistical methods. The book begins with ethical and safety principles, core trial design concepts, the principles and methods of sample size and power calculation, and analysis of covariance and stratified analysis. It then focuses on sequential designs and methods for two-stage Phase II cancer trials to Phase III group sequential trials, covering monitoring safety, futility, and efficacy. The authors also discuss the development of sample size reestimation and adaptive group sequential procedures, phase 2/3 seamless design and trials with predictive biomarkers, exploit multiple testing procedures, and explain the concept of estimand, intercurrent events, and different missing data processes, and describe how to analyze incomplete data by proper multiple imputations. This text reflects the academic research, commercial development, and public health aspects of clinical trials. It gives students and practitioners a multidisciplinary understanding of the concepts and techniques involved in designing, monitoring, and analyzing various types of trials. The book’s balanced set of homework assignments and in-class exercises are appropriate for students and researchers in (bio)statistics, epidemiology, medicine, pharmacy, and public health.
Logistic Regression With Missing Values In The Covariates
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Author : Werner Vach
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Logistic Regression With Missing Values In The Covariates written by Werner Vach and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
In many areas of science a basic task is to assess the influence of several factors on a quantity of interest. If this quantity is binary logistic, regression models provide a powerful tool for this purpose. This monograph presents an account of the use of logistic regression in the case where missing values in the variables prevent the use of standard techniques. Such situations occur frequently across a wide range of statistical applications. The emphasis of this book is on methods related to the classical maximum likelihood principle. The author reviews the essentials of logistic regression and discusses the variety of mechanisms which might cause missing values while the rest of the book covers the methods which may be used to deal with missing values and their effectiveness. Researchers across a range of disciplines and graduate students in statistics and biostatistics will find this a readable account of this.
Missing Data And Small Area Estimation
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Author : Nicholas T. Longford
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-08-05
Missing Data And Small Area Estimation written by Nicholas T. Longford and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-08-05 with Mathematics categories.
This book evolved from lectures, courses and workshops on missing data and small-area estimation that I presented during my tenure as the ?rst C- pion Fellow (2000–2002). For the Fellowship I proposed these two topics as areas in which the academic statistics could contribute to the development of government statistics, in exchange for access to the operational details and background that would inform the direction and sharpen the focus of a- demic research. After a few years of involvement, I have come to realise that the separation of ‘academic’ and ‘industrial’ statistics is not well suited to either party, and their integration is the key to progress in both branches. Most of the work on this monograph was done while I was a visiting l- turer at Massey University, Palmerston North, New Zealand. The hospitality and stimulating academic environment of their Institute of Information S- ence and Technology is gratefully acknowledged. I could not name all those who commented on my lecture notes and on the presentations themselves; apart from them, I want to thank the organisers and silent attendees of all the events, and, with a modicum of reluctance, the ‘grey ?gures’ who kept inquiring whether I was any nearer the completion of whatever stage I had been foolish enough to attach a date.
Analysis Of Survey Data
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Author : R. L. Chambers
language : en
Publisher: John Wiley & Sons
Release Date : 2003-08-01
Analysis Of Survey Data written by R. L. Chambers and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-08-01 with Mathematics categories.
This book is concerned with statistical methods for the analysis of data collected from a survey. A survey could consist of data collected from a questionnaire or from measurements, such as those taken as part of a quality control process. Concerned with the statistical methods for the analysis of sample survey data, this book will update and extend the successful book edited by Skinner, Holt and Smith on 'Analysis of Complex Surveys'. The focus will be on methodological issues, which arise when applying statistical methods to sample survey data and will discuss in detail the impact of complex sampling schemes. Further issues, such as how to deal with missing data and measurement of error will also be critically discussed. There have significant improvements in statistical software which implement complex sampling schemes (eg SUDAAN, STATA, WESVAR, PC CARP ) in the last decade and there is greater need for practical advice for those analysing survey data. To ensure a broad audience, the statistical theory will be made accessible through the use of practical examples. This book will be accessible to a broad audience of statisticians but will primarily be of interest to practitioners analysing survey data. Increased awareness by social scientists of the variety of powerful statistical methods will make this book a useful reference.
Handbook Of Statistical Methods For Case Control Studies
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Author : Ørnulf Borgan
language : en
Publisher: CRC Press
Release Date : 2018-06-27
Handbook Of Statistical Methods For Case Control Studies written by Ørnulf Borgan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-27 with Mathematics categories.
Handbook of Statistical Methods for Case-Control Studies is written by leading researchers in the field. It provides an in-depth treatment of up-to-date and currently developing statistical methods for the design and analysis of case-control studies, as well as a review of classical principles and methods. The handbook is designed to serve as a reference text for biostatisticians and quantitatively-oriented epidemiologists who are working on the design and analysis of case-control studies or on related statistical methods research. Though not specifically intended as a textbook, it may also be used as a backup reference text for graduate level courses. Book Sections Classical designs and causal inference, measurement error, power, and small-sample inference Designs that use full-cohort information Time-to-event data Genetic epidemiology About the Editors Ørnulf Borgan is Professor of Statistics, University of Oslo. His book with Andersen, Gill and Keiding on counting processes in survival analysis is a world classic. Norman E. Breslow was, at the time of his death, Professor Emeritus in Biostatistics, University of Washington. For decades, his book with Nick Day has been the authoritative text on case-control methodology. Nilanjan Chatterjee is Bloomberg Distinguished Professor, Johns Hopkins University. He leads a broad research program in statistical methods for modern large scale biomedical studies. Mitchell H. Gail is a Senior Investigator at the National Cancer Institute. His research includes modeling absolute risk of disease, intervention trials, and statistical methods for epidemiology. Alastair Scott was, at the time of his death, Professor Emeritus of Statistics, University of Auckland. He was a major contributor to using survey sampling methods for analyzing case-control data. Chris J. Wild is Professor of Statistics, University of Auckland. His research includes nonlinear regression and methods for fitting models to response-selective data.
Iss 2012 Proceedings Volume On Longitudinal Data Analysis Subject To Measurement Errors Missing Values And Or Outliers
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Author : Brajendra C. Sutradhar
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-08-13
Iss 2012 Proceedings Volume On Longitudinal Data Analysis Subject To Measurement Errors Missing Values And Or Outliers written by Brajendra C. Sutradhar and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-13 with Mathematics categories.
This proceedings volume contains nine selected papers that were presented in the International Symposium in Statistics, 2012 held at Memorial University from July 16 to 18. These nine papers cover three different areas for longitudinal data analysis, four dealing with longitudinal data subject to measurement errors, four on incomplete longitudinal data analysis, and the last one for inferences for longitudinal data subject to outliers. Unlike in the independence setup, the inferences in measurement errors, missing values, and/or outlier models, are not adequately discussed in the longitudinal setup. The papers in the present volume provide details on successes and further challenges in these three areas for longitudinal data analysis. This volume is the first outlet with current research in three important areas in the longitudinal setup. The nine papers presented in three parts clearly reveal the similarities and differences in inference techniques used for three different longitudinal setups. Because the research problems considered in this volume are encountered in many real life studies in biomedical, clinical, epidemiology, socioeconomic, econometrics, and engineering fields, the volume should be useful to the researchers including graduate students in these areas.
Multiple Imputation And Its Application
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Author : James Carpenter
language : en
Publisher: John Wiley & Sons
Release Date : 2012-12-21
Multiple Imputation And Its Application written by James Carpenter and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-21 with Medical categories.
A practical guide to analysing partially observeddata. Collecting, analysing and drawing inferences from data iscentral to research in the medical and social sciences.Unfortunately, it is rarely possible to collect all the intendeddata. The literature on inference from the resultingincomplete data is now huge, and continues to grow both asmethods are developed for large and complex data structures, and asincreasing computer power and suitable software enable researchersto apply these methods. This book focuses on a particular statistical method foranalysing and drawing inferences from incomplete data, calledMultiple Imputation (MI). MI is attractive because it is bothpractical and widely applicable. The authors aim is to clarify theissues raised by missing data, describing the rationale for MI, therelationship between the various imputation models and associatedalgorithms and its application to increasingly complex datastructures. Multiple Imputation and its Application: Discusses the issues raised by the analysis of partiallyobserved data, and the assumptions on which analyses rest. Presents a practical guide to the issues to consider whenanalysing incomplete data from both observational studies andrandomized trials. Provides a detailed discussion of the practical use of MI withreal-world examples drawn from medical and social statistics. Explores handling non-linear relationships and interactionswith multiple imputation, survival analysis, multilevel multipleimputation, sensitivity analysis via multiple imputation, usingnon-response weights with multiple imputation and doubly robustmultiple imputation. Multiple Imputation and its Application is aimed atquantitative researchers and students in the medical and socialsciences with the aim of clarifying the issues raised by theanalysis of incomplete data data, outlining the rationale for MIand describing how to consider and address the issues that arise inits application.
The Prevention And Treatment Of Missing Data In Clinical Trials
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Author : National Research Council
language : en
Publisher: National Academies Press
Release Date : 2010-12-21
The Prevention And Treatment Of Missing Data In Clinical Trials written by National Research Council and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-12-21 with Medical categories.
Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable. The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data.