A Mixed Model Approach For Intent To Treat Analysis In Longitudinal Clinical Trials With Missing Values

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A mixed model approach for intent-to-treat analysis in longitudinal clinical trials with missing values

Author : Hrishikesh Chakraborty
Publisher : RTI Press
ISBN 13 :
Total Pages : 12 pages
Book Rating : 4./5 ( download)

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Book Synopsis A mixed model approach for intent-to-treat analysis in longitudinal clinical trials with missing values by : Hrishikesh Chakraborty

Download or read book A mixed model approach for intent-to-treat analysis in longitudinal clinical trials with missing values written by Hrishikesh Chakraborty and published by RTI Press. This book was released on 2009-02-28 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing values and dropouts are common issues in longitudinal studies in all areas of medicine and public health. Intent-to-treat (ITT) analysis has become a widely accepted method for the analysis of controlled clinical trials. In most controlled clinical trials, some patients do not complete their intended followup according to the protocol for a variety of reasons; this problem generates missing values. Missing values lead to concern and confusion in identifying the ITT population, which makes the data analysis more complex and challenging. No adequate strategy exists for ITT analyses of longitudinal controlled clinical trial data with missing values. Several ad hoc strategies for dealing with missing values for an ITT analysis are common in the practice of controlled clinical trials. We performed a detailed investigation based on simulation studies to develop recommendations for this situation. We compared sizes (type I errors) and power between some popular ad hoc approaches and the linear mixed model approach under different missing value scenarios. Our results suggest that, for studies with a high percentage of missing values, the mixed model approach without any ad hoc imputation is more powerful than other options.

A Mixed Model Approach for Intent-to-Treat Analysis in Longitudinal Clinical Trials with Missing Values

Author : Hrishikesh Chakraborty
Publisher :
ISBN 13 :
Total Pages : 9 pages
Book Rating : 4.:/5 (856 download)

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Book Synopsis A Mixed Model Approach for Intent-to-Treat Analysis in Longitudinal Clinical Trials with Missing Values by : Hrishikesh Chakraborty

Download or read book A Mixed Model Approach for Intent-to-Treat Analysis in Longitudinal Clinical Trials with Missing Values written by Hrishikesh Chakraborty and published by . This book was released on 2009 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Preventing and Treating Missing Data in Longitudinal Clinical Trials

Author : Craig H. Mallinckrodt
Publisher : Cambridge University Press
ISBN 13 : 1107311365
Total Pages : 185 pages
Book Rating : 4.1/5 (73 download)

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Book Synopsis Preventing and Treating Missing Data in Longitudinal Clinical Trials by : Craig H. Mallinckrodt

Download or read book Preventing and Treating Missing Data in Longitudinal Clinical Trials written by Craig H. Mallinckrodt and published by Cambridge University Press. This book was released on 2013-01-28 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have allowed implementation of a wide array of analyses. In fact, so many methods are available that it can be difficult to ascertain when to use which method. This book focuses on the prevention and treatment of missing data in longitudinal clinical trials. Based on his extensive experience with missing data, the author offers advice on choosing analysis methods and on ways to prevent missing data through appropriate trial design and conduct. He offers a practical guide to key principles and explains analytic methods for the non-statistician using limited statistical notation and jargon. The book's goal is to present a comprehensive strategy for preventing and treating missing data, and to make available the programs used to conduct the analyses of the example dataset.

Principles and Practice of Clinical Trials

Author : Steven Piantadosi
Publisher : Springer Nature
ISBN 13 : 3319526367
Total Pages : 2573 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Principles and Practice of Clinical Trials by : Steven Piantadosi

Download or read book Principles and Practice of Clinical Trials written by Steven Piantadosi and published by Springer Nature. This book was released on 2022-07-19 with total page 2573 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a comprehensive major reference work for our SpringerReference program covering clinical trials. Although the core of the Work will focus on the design, analysis, and interpretation of scientific data from clinical trials, a broad spectrum of clinical trial application areas will be covered in detail. This is an important time to develop such a Work, as drug safety and efficacy emphasizes the Clinical Trials process. Because of an immense and growing international disease burden, pharmaceutical and biotechnology companies continue to develop new drugs. Clinical trials have also become extremely globalized in the past 15 years, with over 225,000 international trials ongoing at this point in time. Principles in Practice of Clinical Trials is truly an interdisciplinary that will be divided into the following areas: 1) Clinical Trials Basic Perspectives 2) Regulation and Oversight 3) Basic Trial Designs 4) Advanced Trial Designs 5) Analysis 6) Trial Publication 7) Topics Related Specific Populations and Legal Aspects of Clinical Trials The Work is designed to be comprised of 175 chapters and approximately 2500 pages. The Work will be oriented like many of our SpringerReference Handbooks, presenting detailed and comprehensive expository chapters on broad subjects. The Editors are major figures in the field of clinical trials, and both have written textbooks on the topic. There will also be a slate of 7-8 renowned associate editors that will edit individual sections of the Reference.

Statistical Analysis with Missing Data

Author : Roderick J. A. Little
Publisher : John Wiley & Sons
ISBN 13 : 1118595696
Total Pages : 463 pages
Book Rating : 4.1/5 (185 download)

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Book Synopsis Statistical Analysis with Missing Data by : Roderick J. A. Little

Download or read book Statistical Analysis with Missing Data written by Roderick J. A. Little and published by John Wiley & Sons. This book was released on 2019-03-21 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date, comprehensive treatment of a classic text on missing data in statistics The topic of missing data has gained considerable attention in recent decades. This new edition by two acknowledged experts on the subject offers an up-to-date account of practical methodology for handling missing data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism, and then they apply the theory to a wide range of important missing data problems. Statistical Analysis with Missing Data, Third Edition starts by introducing readers to the subject and approaches toward solving it. It looks at the patterns and mechanisms that create the missing data, as well as a taxonomy of missing data. It then goes on to examine missing data in experiments, before discussing complete-case and available-case analysis, including weighting methods. The new edition expands its coverage to include recent work on topics such as nonresponse in sample surveys, causal inference, diagnostic methods, and sensitivity analysis, among a host of other topics. An updated “classic” written by renowned authorities on the subject Features over 150 exercises (including many new ones) Covers recent work on important methods like multiple imputation, robust alternatives to weighting, and Bayesian methods Revises previous topics based on past student feedback and class experience Contains an updated and expanded bibliography The authors were awarded The Karl Pearson Prize in 2017 by the International Statistical Institute, for a research contribution that has had profound influence on statistical theory, methodology or applications. Their work "has been no less than defining and transforming." (ISI) Statistical Analysis with Missing Data, Third Edition is an ideal textbook for upper undergraduate and/or beginning graduate level students of the subject. It is also an excellent source of information for applied statisticians and practitioners in government and industry.

Missing Data in Clinical Studies

Author : Geert Molenberghs
Publisher : John Wiley & Sons
ISBN 13 : 9780470510438
Total Pages : 526 pages
Book Rating : 4.5/5 (14 download)

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Book Synopsis Missing Data in Clinical Studies by : Geert Molenberghs

Download or read book Missing Data in Clinical Studies written by Geert Molenberghs and published by John Wiley & Sons. This book was released on 2007-04-04 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing Data in Clinical Studies provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents the reader with approaches to effectively address them. The text provides a critique of conventional and simple methods before moving on to discuss more advanced approaches. The authors focus on practical and modeling concepts, providing an extensive set of case studies to illustrate the problems described. Provides a practical guide to the analysis of clinical trials and related studies with missing data. Examines the problems caused by missing data, enabling a complete understanding of how to overcome them. Presents conventional, simple methods to tackle these problems, before addressing more advanced approaches, including sensitivity analysis, and the MAR missingness mechanism. Illustrated throughout with real-life case studies and worked examples from clinical trials. Details the use and implementation of the necessary statistical software, primarily SAS. Missing Data in Clinical Studies has been developed through a series of courses and lectures. Its practical approach will appeal to applied statisticians and biomedical researchers, in particular those in the biopharmaceutical industry, medical and public health organisations. Graduate students of biostatistics will also find much of benefit.

Preventing and Treating Missing Data in Longitudinal Clinical Trials

Author : Craig Mallinckrodt
Publisher :
ISBN 13 : 9781107301832
Total Pages : pages
Book Rating : 4.3/5 (18 download)

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Book Synopsis Preventing and Treating Missing Data in Longitudinal Clinical Trials by : Craig Mallinckrodt

Multiple Imputation for Nonresponse in Surveys

Author : Donald B. Rubin
Publisher : John Wiley & Sons
ISBN 13 : 0470317361
Total Pages : 258 pages
Book Rating : 4.4/5 (73 download)

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Book Synopsis Multiple Imputation for Nonresponse in Surveys by : Donald B. Rubin

Download or read book Multiple Imputation for Nonresponse in Surveys written by Donald B. Rubin and published by John Wiley & Sons. This book was released on 2009-09-25 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demonstrates how nonresponse in sample surveys and censuses can be handled by replacing each missing value with two or more multiple imputations. Clearly illustrates the advantages of modern computing to such handle surveys, and demonstrates the benefit of this statistical technique for researchers who must analyze them. Also presents the background for Bayesian and frequentist theory. After establishing that only standard complete-data methods are needed to analyze a multiply-imputed set, the text evaluates procedures in general circumstances, outlining specific procedures for creating imputations in both the ignorable and nonignorable cases. Examples and exercises reinforce ideas, and the interplay of Bayesian and frequentist ideas presents a unified picture of modern statistics.

Design of Primary and Sensitivity Analyses for Handling Non-future Dependence Missing Data in Clinical Trials with an Emphasis on the Type-i Error Rate Using Multiple Imputation and Pattern Mixture Model Approach

Download Design of Primary and Sensitivity Analyses for Handling Non-future Dependence Missing Data in Clinical Trials with an Emphasis on the Type-i Error Rate Using Multiple Imputation and Pattern Mixture Model Approach PDF Online Free

Author : Lixian Peng
Publisher :
ISBN 13 :
Total Pages : 130 pages
Book Rating : 4.:/5 (922 download)

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Book Synopsis Design of Primary and Sensitivity Analyses for Handling Non-future Dependence Missing Data in Clinical Trials with an Emphasis on the Type-i Error Rate Using Multiple Imputation and Pattern Mixture Model Approach by : Lixian Peng

Download or read book Design of Primary and Sensitivity Analyses for Handling Non-future Dependence Missing Data in Clinical Trials with an Emphasis on the Type-i Error Rate Using Multiple Imputation and Pattern Mixture Model Approach written by Lixian Peng and published by . This book was released on 2015 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing data is a common problem in longitudinal clinical trials. Substantial missing data could introduce potential biases and undermine the scientific credibility of causal conclusions from clinical trials. To handle the missing data issue, it is always required by the regulatory agencies to pre-specify a primary analysis and sensitivity analysis in protocol or statistical analysis plan (SAP). Recent National Research Council (NRC) report questioned the reasonableness of the missing at random (MAR) setting as the primary analysis since MAR is a very special and doubtful assumption for the missing data mechanism, and the report encourages to use not missing at random (NMAR) setting as the primary analysis. One of the NMAR mechanisms is non-future dependence missing data (NFD-NMAR). It is also one of the recommended methods in the NRC report. This dissertation addressed this issue and proposed a process to investigate the mean-shift model with NFD-NMAR mechanism (NFD-Delta method). The goal is to provide, via the investigation process, a method of finding an appropriate shift parameter to specify the primary NMAR analysis in study protocol or SAP based on the maintenance of the type-I error rate for any late phase trial by simulations. The simulation set-up should be based on either early phase data or information from interim data of the current trial. The shift parameter of the NFD-Delta method constitutes the sensitivity analysis. Several components were considered for the NFD shift parameter in this dissertation: the metric/unit, magnitude, and the algorithm to place the shift to examine the effect of these components on the type-I error rate (alpha) under the null hypothesis of no treatment effect. For the metric factor, four different metric units were considered: constant STD1, constant RSD1, STDk, RSDk; for the magnitude factor, different values of shift parameter f were considered to investigate which f value is the appropriate shift parameter to control the type-I error rate to the nominal level; for the algorithm to implement the delta shift, three different methods were proposed: sequential, non-sequential and single adjustment method. Extensive simulations were conducted to investigate the type-I error rate. Correctness and robustness of the results were examined.

Preventing and Treating Missing Data in Longitudinal Clinical Trials

Author : Craig Mallinckrodt
Publisher :
ISBN 13 :
Total Pages : 165 pages
Book Rating : 4.:/5 (111 download)

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Book Synopsis Preventing and Treating Missing Data in Longitudinal Clinical Trials by : Craig Mallinckrodt

Download or read book Preventing and Treating Missing Data in Longitudinal Clinical Trials written by Craig Mallinckrodt and published by . This book was released on 2013 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have allowed implementation of a wide array of analyses. In fact, so many methods are available that it can be difficult to ascertain when to use which method. This book focuses on the prevention and treatment of missing data in longitudinal clinical trials. Based on his extensive experience with missing data, the author offers advice on choosing analysis methods and on ways to prevent missing data through appropriate trial design and conduct. He offers a practical guide to key principles and explains analytic methods for the non-statistician using limited statistical notation and jargon. The book's goal is to present a comprehensive strategy for preventing and treating missing data, and to make available the programs used to conduct the analyses of the example dataset"

The Prevention and Treatment of Missing Data in Clinical Trials

Author : National Research Council
Publisher : National Academies Press
ISBN 13 : 030918651X
Total Pages : 163 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis The Prevention and Treatment of Missing Data in Clinical Trials by : National Research Council

Download or read book The Prevention and Treatment of Missing Data in Clinical Trials written by National Research Council and published by National Academies Press. This book was released on 2010-12-21 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Analysis of Observational Health Care Data Using SAS

Author : Douglas E. Faries
Publisher : SAS Press
ISBN 13 : 9781607642275
Total Pages : 0 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Analysis of Observational Health Care Data Using SAS by : Douglas E. Faries

Download or read book Analysis of Observational Health Care Data Using SAS written by Douglas E. Faries and published by SAS Press. This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book guides researchers in performing and presenting high-quality analyses of all kinds of non-randomized studies, including analyses of observational studies, claims database analyses, assessment of registry data, survey data, pharmaco-economic data, and many more applications. The text is sufficiently detailed to provide not only general guidance, but to help the researcher through all of the standard issues that arise in such analyses. Just enough theory is included to allow the reader to understand the pros and cons of alternative approaches and when to use each method. The numerous contributors to this book illustrate, via real-world numerical examples and SAS code, appropriate implementations of alternative methods. The end result is that researchers will learn how to present high-quality and transparent analyses that will lead to fair and objective decisions from observational data. This book is part of the SAS Press program.

Longitudinal Data Analysis

Author : Jason Newsom
Publisher : Routledge
ISBN 13 : 1136705473
Total Pages : 407 pages
Book Rating : 4.1/5 (367 download)

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Book Synopsis Longitudinal Data Analysis by : Jason Newsom

Download or read book Longitudinal Data Analysis written by Jason Newsom and published by Routledge. This book was released on 2013-06-19 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides accessible treatment to state-of-the-art approaches to analyzing longitudinal studies. Comprehensive coverage of the most popular analysis tools allows readers to pick and choose the techniques that best fit their research. The analyses are illustrated with examples from major longitudinal data sets including practical information about their content and design. Illustrations from popular software packages offer tips on how to interpret the results. Each chapter features suggested readings for additional study and a list of articles that further illustrate how to implement the analysis and report the results. Syntax examples for several software packages for each of the chapter examples are provided at www.psypress.com/longitudinal-data-analysis. Although many of the examples address health or social science questions related to aging, readers from other disciplines will find the analyses relevant to their work. In addition to demonstrating statistical analysis of longitudinal data, the book shows how to interpret and analyze the results within the context of the research design. The methods covered in this book are applicable to a range of applied problems including short- to long-term longitudinal studies using a range of sample sizes. The book provides non-technical, practical introductions to the concepts and issues relevant to longitudinal analysis. Topics include use of publicly available data sets, weighting and adjusting for complex sampling designs with longitudinal studies, missing data and attrition, measurement issues related to longitudinal research, the use of ANOVA and regression for average change over time, mediation analysis, growth curve models, basic and advanced structural equation models, and survival analysis. An ideal supplement for graduate level courses on data analysis and/or longitudinal modeling taught in psychology, gerontology, public health, human development, family studies, medicine, sociology, social work, and other behavioral, social, and health sciences, this multidisciplinary book will also appeal to researchers in these fields.

Statistical Power Analysis with Missing Data

Author : Adam Davey
Publisher : Routledge
ISBN 13 : 1135269300
Total Pages : 328 pages
Book Rating : 4.1/5 (352 download)

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Book Synopsis Statistical Power Analysis with Missing Data by : Adam Davey

Download or read book Statistical Power Analysis with Missing Data written by Adam Davey and published by Routledge. This book was released on 2009-08-20 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling. It answers many practical questions such as: How missing data affects the statistical power in a study How much power is likely with different amounts and types of missing data How to increase the power of a design in the presence of missing data, and How to identify the most powerful design in the presence of missing data. Points of Reflection encourage readers to stop and test their understanding of the material. Try Me sections test one’s ability to apply the material. Troubleshooting Tips help to prevent commonly encountered problems. Exercises reinforce content and Additional Readings provide sources for delving more deeply into selected topics. Numerous examples demonstrate the book’s application to a variety of disciplines. Each issue is accompanied by its potential strengths and shortcomings and examples using a variety of software packages (SAS, SPSS, Stata, LISREL, AMOS, and MPlus). Syntax is provided using a single software program to promote continuity but in each case, parallel syntax using the other packages is presented in appendixes. Routines, data sets, syntax files, and links to student versions of software packages are found at www.psypress.com/davey. The worked examples in Part 2 also provide results from a wider set of estimated models. These tables, and accompanying syntax, can be used to estimate statistical power or required sample size for similar problems under a wide range of conditions. Class-tested at Temple, Virginia Tech, and Miami University of Ohio, this brief text is an ideal supplement for graduate courses in applied statistics, statistics II, intermediate or advanced statistics, experimental design, structural equation modeling, power analysis, and research methods taught in departments of psychology, human development, education, sociology, nursing, social work, gerontology and other social and health sciences. The book’s applied approach will also appeal to researchers in these areas. Sections covering Fundamentals, Applications, and Extensions are designed to take readers from first steps to mastery.

A Continuous Latent Factor Model for Non-ignorable Missing Data in Longitudinal Studies

Author : Jun Zhang
Publisher :
ISBN 13 :
Total Pages : 139 pages
Book Rating : 4.:/5 (87 download)

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Book Synopsis A Continuous Latent Factor Model for Non-ignorable Missing Data in Longitudinal Studies by : Jun Zhang

Download or read book A Continuous Latent Factor Model for Non-ignorable Missing Data in Longitudinal Studies written by Jun Zhang and published by . This book was released on 2013 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many longitudinal studies, especially in clinical trials, suffer from missing data issues. Most estimation procedures assume that the missing values are ignorable or missing at random (MAR). However, this assumption leads to unrealistic simplification and is implausible for many cases. For example, an investigator is examining the effect of treatment on depression. Subjects are scheduled with doctors on a regular basis and asked questions about recent emotional situations. Patients who are experiencing severe depression are more likely to miss an appointment and leave the data missing for that particular visit. Data that are not missing at random may produce bias in results if the missing mechanism is not taken into account. In other words, the missing mechanism is related to the unobserved responses. Data are said to be non-ignorable missing if the probabilities of missingness depend on quantities that might not be included in the model. Classical pattern-mixture models for non-ignorable missing values are widely used for longitudinal data analysis because they do not require explicit specification of the missing mechanism, with the data stratified according to a variety of missing patterns and a model specified for each stratum. However, this usually results in under-identifiability, because of the need to estimate many stratum-specific parameters even though the eventual interest is usually on the marginal parameters. Pattern mixture models have the drawback that a large sample is usually required. In this thesis, two studies are presented. The first study is motivated by an open problem from pattern mixture models. Simulation studies from this part show that information in the missing data indicators can be well summarized by a simple continuous latent structure, indicating that a large number of missing data patterns may be accounted by a simple latent factor. Simulation findings that are obtained in the first study lead to a novel model, a continuous latent factor model (CLFM). The second study develops CLFM which is utilized for modeling the joint distribution of missing values and longitudinal outcomes. The proposed CLFM model is feasible even for small sample size applications. The detailed estimation theory, including estimating techniques from both frequentist and Bayesian perspectives is presented. Model performance and evaluation are studied through designed simulations and three applications. Simulation and application settings change from correctly-specified missing data mechanism to mis-specified mechanism and include different sample sizes from longitudinal studies. Among three applications, an AIDS study includes non-ignorable missing values; the Peabody Picture Vocabulary Test data have no indication on missing data mechanism and it will be applied to a sensitivity analysis; the Growth of Language and Early Literacy Skills in Preschoolers with Developmental Speech and Language Impairment study, however, has full complete data and will be used to conduct a robust analysis. The CLFM model is shown to provide more precise estimators, specifically on intercept and slope related parameters, compared with Roy's latent class model and the classic linear mixed model. This advantage will be more obvious when a small sample size is the case, where Roy's model experiences challenges on estimation convergence. The proposed CLFM model is also robust when missing data are ignorable as demonstrated through a study on Growth of Language and Early Literacy Skills in Preschoolers.

Analyzing Longitudinal Clinical Trial Data

Author : CRAIG. LIPKOVICH MALLINCKRODT (ILYA.)
Publisher : CRC Press
ISBN 13 : 9780367736583
Total Pages : 302 pages
Book Rating : 4.7/5 (365 download)

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Book Synopsis Analyzing Longitudinal Clinical Trial Data by : CRAIG. LIPKOVICH MALLINCKRODT (ILYA.)

Download or read book Analyzing Longitudinal Clinical Trial Data written by CRAIG. LIPKOVICH MALLINCKRODT (ILYA.) and published by CRC Press. This book was released on 2020-12-18 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analyzing Longitudinal Clinical Trial Data: A Practical Guide provides practical and easy to implement approaches for bringing the latest theory on analysis of longitudinal clinical trial data into routine practice.The book, with its example-oriented approach that includes numerous SAS and R code fragments, is an essential resource for statisticians and graduate students specializing in medical research. The authors provide clear descriptions of the relevant statistical theory and illustrate practical considerations for modeling longitudinal data. Topics covered include choice of endpoint and statistical test; modeling means and the correlations between repeated measurements; accounting for covariates; modeling categorical data; model verification; methods for incomplete (missing) data that includes the latest developments in sensitivity analyses, along with approaches for and issues in choosing estimands; and means for preventing missing data. Each chapter stands alone in its coverage of a topic. The concluding chapters provide detailed advice on how to integrate these independent topics into an over-arching study development process and statistical analysis plan.

Cancer Disparities

Author : Ronit Elk
Publisher : Springer Publishing Company
ISBN 13 : 0826108822
Total Pages : 582 pages
Book Rating : 4.8/5 (261 download)

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Book Synopsis Cancer Disparities by : Ronit Elk

Download or read book Cancer Disparities written by Ronit Elk and published by Springer Publishing Company. This book was released on 2012 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt: There has been remarkable progress in understanding, preventing, detecting, diagnosing, and treating cancer, resulting in a reduction of cancer incidence and mortality in the United States. Despite this, the cancer burden varies considerably by race/ethnicity and socioeconomic status. Cancer incidence rates vary markedly between racial/ethnic groups, but even more startling are the differences in outcome across groups. Cancer Disparities: Causes and Evidence-Based Solutions helps readers understand the scope and causes of this inequity by providing a detailed analysis of the many factors that result in cancer disparities across the cancer continuum, including the role of race/ethnicity, socioeconomic status, access to and use of services, insurance status, geographic variables, and differences in treatment provided to patients. Further, it is the first book to describe evidence-based, concrete solutions that can be used to reduce or even eliminate cancer health disparities. Fifteen previously unpublished studies of interventions designed specifically to achieve health equality are described. These studies focus on contextually and culturally appropriate strategies to enhance cancer prevention, screening and early detection, treatment, symptom management, and quality of life in underserved populations.