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Design and Analysis of Quality of Life Studies in Clinical Trials (Chapman & Hall/CRC Interdisciplinary Statistics)
 
 
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Design and Analysis of Quality of Life Studies in Clinical Trials (Chapman & Hall/CRC Interdisciplinary Statistics) [Hardcover]

Diane L. Fairclough (Author)
4.0 out of 5 stars  See all reviews (1 customer review)

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Book Description

Chapman & Hall/CRC Interdisciplinary Statistics March 28, 2002
More and more frequently, clinical trials include the evaluation of Health-Related Quality of Life (HRQoL), yet many investigators remain unaware of the unique measurement and analysis issues associated with the assessment of HRQoL. At the end of a study, clinicians and statisticians often face challenging and sometimes insurmountable analytic problems.

Design and Analysis of Quality of Life Studies in Clinical Trials details these issues and presents a range of solutions. Written from the author's extensive experience in the field, it focuses on the very specific features of QoL data: its longitudinal nature, multidimensionality, and the problem of missing data. The author uses three real clinical trials throughout her discussions to illustrate practical implementation of the strategies and analytic methods presented.

As Quality of Life becomes an increasingly important aspect of clinical trials, it becomes essential for clinicians, statisticians, and designers of these studies to understand and meet the challenges this kind of data present. In this book, SAS and S-PLUS programs, checklists, numerous figures, and a clear, concise presentation combine to provide readers with the tools and skills they need to successfully design, conduct, analyze, and report their own studies.

Product Details

  • Hardcover: 328 pages
  • Publisher: Chapman and Hall/CRC; 1 edition (March 28, 2002)
  • Language: English
  • ISBN-10: 1584882638
  • ISBN-13: 978-1584882633
  • Product Dimensions: 9.7 x 6.2 x 0.9 inches
  • Shipping Weight: 1.4 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #2,605,840 in Books (See Top 100 in Books)

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28 of 28 people found the following review helpful:
4.0 out of 5 stars excellent coverage on quality of life, January 22, 2008
This review is from: Design and Analysis of Quality of Life Studies in Clinical Trials (Chapman & Hall/CRC Interdisciplinary Statistics) (Hardcover)
This is really the first really good book for statisticians on quality of life issues and methods with authoritative references. The author provides practical advice for qol studies in a clinical trials setting and provides sage advice on design of the study. Actual quality of life surveys are discussed and the importance of defining objectives is emphasized. Some real cancer trials are discussed and used as examples throughout the book. There is detailed treatment of missing data and how to deal with it including the latest statistical methodology. Much of the methodological development could apply to hard endpoints as well as qol endpoints.

I got this book and a good taste of its contents in a short course presented by the author at the Joint Statistical meetings in New York in August 2002. The only drawback of the book is that it does not spend much time on the issue of instrument validation. Also the author's experience is with cancer trials in an academic setting. The issues related to conducting qol surveys for a manufacturer that needs labeling or FDA approval efficacy and safety approvals is a prospective that I would like to see but the author does not have.

Excellent examples illustrated in SAS and SPlus.
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Inside This Book (learn more)
First Sentence:
Traditionally, clinical trials have focused on endpoints that are physical or laboratory measures of response. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
adjuvant breast cancer study, renal cell carcinoma study, conditional linear model, renal cell carcinoma example, hth group, dropout model, nonrandom missing data, nonignorable missing data, piecewise regression model, missing data mechanism, predictive mean matching, pattern mixture models, missing assessments, imputed scores, imputation model, random dropout, carcinoma trial, missing data patterns, missing data process, design matrix corresponding, impute missing values, repeated measures model, simple imputation, univariate test statistics, informative dropout
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Imputed Score, Estimate Error, Functional Assessment of Cancer Therapy, Covariance Parameter Estimates, Breast Chemotherapy Questionnaire, Eastern Cooperative Oncology Group, Months Post Randomization Figure, Simple Equal, Trial Outcome Index, Group Specific Estimates, Longitudinal Data Analysis Model, Mean Parameters, National Cancer Institute Grant, Statement Results Source, Taxol Baseline, Taxol Mean, Test Differences
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