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El. knyga: Design and Analysis of Quality of Life Studies in Clinical Trials

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(University of Colorado Health Sciences Center, Denver, USA)
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Design Principles and Analysis Techniques for HRQoL Clinical Trials SAS, R, and SPSS examples realistically show how to implement methods

Focusing on longitudinal studies, Design and Analysis of Quality of Life Studies in Clinical Trials, Second Edition addresses design and analysis aspects in enough detail so that readers can apply statistical methods, such as mixed effect models, to their own studies. The author illustrates the implementation of the methods using the statistical software packages SAS, SPSS, and R.

New to the Second Edition











Data sets available for download online, allowing readers to replicate the analyses presented in the text New chapter on testing models that involve moderation and mediation Revised discussions of multiple comparisons procedures that focus on the integration of health-related quality of life (HRQoL) outcomes with other study outcomes using gatekeeper strategies Recent methodological developments for the analysis of trials with missing data New chapter on quality adjusted life-years (QALYs) and QTWiST specific to clinical trials Additional examples of the implementation of basic models and other selected applications in R and SPSS

This edition continues to provide practical information for researchers directly involved in the design and analysis of HRQoL studies as well as for those who evaluate the design and interpret the results of HRQoL research. By following the examples in the book, readers will be able to apply the steps to their own trials.

Recenzijos

The book is written for a wide range of researchers interested in HRQoL research, including clinicians, epidemiologists, psychologists and statisticians. the author did her best to make the material accessible to a larger audience through the chapter structure, the datasets, the software code and programs available from the authors website. She should be commended for her efforts and improvements since the first edition. Every researcher involved in the design and analysis of HRQoL studies will benefit from having this book on their shelf. Stephane Heritier, Australian & New Zealand Journal of Statistics, 2013

I found that the use of well-placed comment statements and titles, as well as additional coding [ on the authors website], enhanced my understanding considerably. Cynthia A. Rodenberg, Journal of Biopharmaceutical Statistics, 21, 2011

It is a well-organized and nicely written book, which should be quite useful for researchers involved in HRQoL studies. it may serve as a textbook for a graduate-level course in applied statistics focused on clinical epidemiology and health services research. Another bonus for students and instructors refer to the example programs in SAS, SPSS and R provided in the book, in addition to full data sets available for download online, which was not offered with the first edition. Biometrics, 67, September 2011

Professor Fairclough has succeeded in writing a book which can be used by trial statisticians for the valid analysis of quality of life data. It is a remarkable combination of theory and practical advice. The second edition [ includes] examples in R and SPSS as well as SAS, and gives links to download all the data and much of the code in the book. excellent book. All in all, this is a useful resource for statisticians working in the areas of quality of life, clinical trials, and/or missing data. ISCB News, No. 51, June 2011

this book offers unique perspectives and insights that reflect decades of hands-on experience with HRQoL trials and that will certainly benefit researchers in this area. Written clearly and concisely, the book is a pleasure to read. The technical level is reasonable for statistical practitioners and medical researchers with a good understanding of basic statistical concepts and methods. I would definitely recommend the book to researchers in HRQoL studies, and I think it is worth reading by anyone interested in clinical trials, because many of the issues discussed extend far beyond HRQoL studies. Statistics in Medicine, 2011, 30

The book sits well in the Interdisciplinary Statistics Series, containing much insightful discussion of the issues and not too much mathematics. It is carefully written and well organized and likely to become a standard reference in the area, taking its place on many a bookshelf, both personal and library-based. International Statistical Review (2010), 78, 3

Preface xvii
Introduction and Examples
1(28)
Health-Related Quality of Life (HRQoL)
1(2)
Measuring Health-Related Quality of Life
3(5)
Health Status Measures
3(1)
Patient Preference Measures
4(1)
Objective versus Subjective Questions
5(1)
Generic versus Disease-Specific Instruments
5(1)
Global Index versus Profile of Domain-Specific Measures
6(1)
Response Format
6(1)
Period of Recall
7(1)
Adjuvant Breast Cancer Trial
8(4)
Patient Selection and Treatment
9(1)
Quality of Life Measure and Scoring
9(2)
Timing of HRQoL Assessments
11(1)
Questionnaire Completion/Missing Data
12(1)
Migraine Prevention Trial
12(4)
Patient Selection and Treatment
13(1)
Quality of Life Measure and Scoring
14(1)
Timing of HRQoL Assessments
15(1)
Questionnaire Completion/Missing Data
15(1)
Advanced Lung Cancer Trial
16(3)
Treatment
16(1)
Quality of Life Measure and Scoring
17(1)
Timing of Assessments
18(1)
Questionnaire Completion/Missing Data
19(1)
Renal Cell Carcinoma Trial
19(4)
Patient Selection and Treatment
20(1)
Quality of Life Measures and Scoring
20(1)
Timing of HRQoL Assessments
21(1)
Questionnaire Completion/Missing Data
22(1)
Chemoradiation (CXRT) Trial
23(2)
Patient Selection and Treatment
23(1)
Patient Reported Outcomes
24(1)
Timing and Frequency of HRQoL Assessments
25(1)
Osteoarthritis Trial
25(2)
Patient Selection and Treatment
25(1)
Timing and Frequency of Assessments
25(2)
Summary
27(2)
Study Design and Protocol Development
29(24)
Introduction
29(2)
Purpose of the Protocol
29(2)
Background and Rationale
31(1)
Research Objectives and Goals
31(3)
Role of HRQoL in the Trial
33(1)
Pragmatic versus Explanatory Inference
33(1)
Selection of Subjects
34(1)
Longitudinal Designs
35(4)
Event- or Condition-Driven Designs
35(1)
Time-Driven Designs
36(1)
Timing of the Initial HRQoL Assessment
36(1)
Timing of the Follow-Up HRQoL Assessments
36(1)
Frequency of Evaluations
37(1)
Duration of HRQoL Assessments
38(1)
Assessment after Discontinuation of Therapy
38(1)
Selection of Measurement Instrument(s)
39(4)
Trial Objectives
40(1)
Validity and Reliability
41(1)
Appropriateness
42(1)
Conduct of HRQoL Assessments
43(5)
Order and Place of Administration
43(1)
Mode of Administration and Assistance by Third Parties
44(1)
Data Collection and Key Personnel
44(1)
Avoiding Missing Data
45(3)
Scoring Instruments
48(3)
Reverse Coding
48(1)
Scoring Multi-Item Scales
48(1)
Item nonresponse
49(2)
Summary
51(2)
Models for Longitudinal Studies I
53(30)
Introduction
53(3)
Repeated Measures Models
53(1)
Growth Curve Models
54(1)
Selection between Models
54(2)
Building Models for Longitudinal Studies
56(2)
Advantages of the General Linear Model (GLM)
56(1)
Building a General Linear Model
57(1)
Statistics Guiding Model Reduction
57(1)
Building Repeated Measures Models: The Mean Structure
58(9)
Treatment and Time
58(3)
Common Baseline
61(1)
Change from Baseline
61(1)
Covariates
61(1)
Modeling the ``Mean'' Structure in SAS
62(2)
Modeling the ``Mean'' Structure in SPSS
64(2)
Modeling the ``Mean'' Structure in R
66(1)
Building Repeated Measures Models: The Covariance Structure
67(7)
Unstructured Covariance
68(1)
Structured Covariance
69(1)
Building the Covariance Structure in SAS
70(2)
Building the Covariance Structure in SPSS
72(1)
Building the Covariance Structure in R
72(2)
Estimation and Hypothesis Testing
74(7)
Estimation and Hypothesis Testing in SAS
75(2)
Estimation and Hypothesis Testing in SPSS
77(1)
Estimation and Hypothesis Testing in R
78(3)
Summary
81(2)
Models for Longitudinal Studies II
83(22)
Introduction
83(1)
Building Growth Curve Models: The ``Mean'' (Fixed Effects) Structure
84(5)
Polynomial Models
84(1)
Piecewise Linear Regression
85(2)
Modeling the ``Mean'' Structure in SAS
87(1)
Modeling the ``Mean'' Structure in SPSS
88(1)
Modeling the ``Mean'' Structure in R
88(1)
Building Growth Curve Models: The Covariance Structure
89(6)
Variance of Random Effects (G)
90(1)
Variance of Residual Errors (Ri)
91(2)
Building the Covariance Structure in SAS
93(1)
Building the Covariance Structure in SPSS
94(1)
Building the Covariance Structure in R
95(1)
Model Reduction
95(1)
Hypothesis Testing and Estimation
96(5)
Estimation and Testing in SAS
99(1)
Estimation and Testing in SPSS
99(1)
Estimation and Testing in R
100(1)
An Alternative Covariance Structure
101(3)
Model for the Means
102(1)
Model for the Variance
102(1)
Estimation and Testing
103(1)
Summary
104(1)
Moderation and Mediation
105(20)
Introduction
105(2)
Moderation
107(8)
Moderation across Repeated Measures
107(5)
Change from Baseline
112(1)
Centering Covariates
113(2)
Mediation
115(5)
Mediation with Treatment as the Predictor
116(2)
Mediation with Time as the Predictor
118(2)
Other Exploratory Analyses
120(3)
Mediation in Mixed Effects Models
120(1)
Non-Linear Relationships
121(2)
Summary
123(2)
Characterization of Missing Data
125(24)
Introduction
125(3)
Terminology
126(1)
Why are Missing Data a Problem?
126(1)
How Much Data Can Be Missing?
126(1)
Prevention
127(1)
Patterns and Causes of Missing Data
128(2)
Mechanisms of Missing Data
130(2)
The Concept
130(1)
Notation
131(1)
Missing Completely at Random (MCAR)
132(3)
The Concept
132(1)
Covariate-Dependent Dropout
133(1)
Identifying Covariate-Dependent Missingness
133(1)
Analytic Methods
134(1)
MAR: Missing at Random
135(4)
The Concept
135(1)
Identifying Dependence on Observed Data (Yobsi)
135(4)
Analytic Methods
139(1)
MNAR: Missing Not at Random
139(4)
The Concept
139(1)
Identifying Dependence on Unobserved Data (Ymisi)
140(3)
Analytic Methods
143(1)
Example for Trial with Variation in Timing of Assessments
143(2)
Example with Different Patterns across Treatment Arms
145(1)
Summary
146(3)
Analysis of Studies with Missing Data
149(14)
Introduction
149(1)
Missing Completely at Random
149(5)
Complete Case Analysis (MANOVA)
151(1)
Repeated Univariate Analyses
151(3)
Ignorable Missing Data
154(6)
Maximum Likelihood Estimation (MLE)
155(1)
Empirical Bayes Estimates
156(1)
Multiple Imputation
157(1)
Lung Cancer Trial (Study 3)
157(1)
Baseline Assessment as a Covariate
157(1)
Adding Other Baseline Covariates
158(1)
Final Comments
159(1)
Non-Ignorable Missing Data
160(2)
Selection Models
160(1)
Mixture Models
161(1)
Final Comments
161(1)
Summary
162(1)
Simple Imputation
163(18)
Introduction to Imputation
163(2)
Simple versus Multiple Imputation
164(1)
Imputation in Multivariate Longitudinal Studies
164(1)
Missing Items in a Multi-Item Questionnaire
165(2)
Regression Based Methods
167(5)
Mean Value Substitution
167(1)
Explicit Regression Models
168(4)
Other Simple Imputation Methods
172(4)
Last Value Carried Forward (LVCF)
172(2)
δ-Adjustments
174(1)
Arbitrary High or Low Value
174(1)
Hot Deck and Other Sampling Procedures
175(1)
Imputing Missing Covariates
176(1)
Underestimation of Variance
176(2)
Final Comments
178(1)
Sensitivity Analysis
178(1)
Summary
179(2)
Multiple Imputation
181(28)
Introduction
181(1)
Overview of Multiple Imputation
181(2)
Explicit Univariate Regression
183(9)
Identification of the Imputation Model
183(1)
Computation of Imputed Values
184(1)
Practical Considerations
185(1)
Extensions to Longitudinal Studies
186(1)
Extensions to Multiple HRQoL Measures
186(1)
Assumptions
187(1)
Lung Cancer Trial (Study 3)
187(2)
Implementation
189(3)
Closest Neighbor and Predictive Mean Matching
192(2)
Closest Neighbor
192(1)
Predictive Mean Matching
192(2)
Approximate Bayesian Bootstrap (ABB)
194(2)
Practical Considerations
194(2)
The Assumptions
196(1)
A Variation for Non-Ignorable Missing Data
196(1)
Multivariate Procedures for Non-Monotone Missing Data
196(4)
Implementation in SAS
197(1)
Implementation in SPSS
197(2)
Implementation in R
199(1)
Analysis of the M Datasets
200(5)
Univariate Estimates and Statistics
200(2)
Multivariate Tests
202(1)
Analysis of M Datasets in SAS
202(1)
Analysis of M Datasets in SPSS
203(1)
Analysis of M Datasets in R
204(1)
Miscellaneous Issues
205(3)
Sensitivity Analyses
205(2)
Imputation after Death
207(1)
Imputation versus Analytic Models
207(1)
Implications for Design
207(1)
Summary
208(1)
Pattern Mixture and Other Mixture Models
209(30)
Introduction
209(4)
General Approach
209(1)
Illustration
210(3)
Pattern Mixture Models
213(1)
Specifying Patterns
213(1)
Restrictions for Growth Curve Models
214(12)
Linear Trajectories over Time
215(2)
Estimation of the Parameters
217(2)
Nonlinear Trajectories over Time
219(1)
Implementation in SAS
220(3)
Implementation in R
223(3)
Restrictions for Repeated Measures Models
226(9)
Bivariate Data (Two Repeated Measures)
226(5)
Monotone Dropout
231(4)
Standard Errors for Mixture Models
235(3)
Delta Method
236(1)
Bootstrap Methods
237(1)
Summary
238(1)
Random Effects Dependent Dropout
239(28)
Introduction
239(2)
Conditional Linear Model
241(8)
Assumptions
242(1)
Testing MAR versus MNAR under the Assumptions of Conditional Linear Model
243(1)
Lung Cancer Trial (Study 3)
243(5)
Estimation of the Standard Errors
248(1)
Varying Coefficient Models
249(4)
Assumptions
250(1)
Application
251(1)
General Application
252(1)
Joint Models with Shared Parameters
253(13)
Joint versus Conditional Linear Model
255(1)
Testing MAR versus MNAR under the Assumptions of the Joint Model
255(1)
Alternative Parameterizations
255(1)
Implementation
256(1)
Implementation in SAS
257(5)
Implementation in R
262(1)
Multiple Causes of Dropout
263(2)
Other Model Extensions
265(1)
Summary
266(1)
Selection Models
267(8)
Introduction
267(1)
Outcome Selection Model for Monotone Dropout
268(6)
Lung Cancer Trial (Study 3)
270(4)
Summary
274(1)
Multiple Endpoints
275(20)
Introduction
275(2)
Aims and Goals/Role of HRQoL
276(1)
Other Critical Information
276(1)
General Strategies for Multiple Endpoints
277(3)
Limiting the Number of Confirmatory Tests
278(1)
Summary Measures and Statistics
279(1)
Multiple Testing Procedures
279(1)
Background Concepts and Definitions
280(2)
Univariate versus Multivariate Test Statistics
280(1)
Global Tests
281(1)
Error Rates
281(1)
Single Step Procedures
282(3)
Global Tests Based on Multivariate Test Statistics
282(1)
Equally Weighted Univariate Statistics
283(1)
Importance Weighting/Spending α
284(1)
Sequentially Rejective Methods
285(2)
Equal Weighting of Endpoints
285(1)
Implementation in SAS
286(1)
Implementation in SPSS
286(1)
Implementation in R
287(1)
Importance Weighting
287(1)
Closed Testing and Gatekeeper Procedures
287(7)
Closed Testing Based on a Bonferroni Correction
288(1)
Sequential Families
289(2)
Shortcuts for Closed Testing Procedures
291(2)
A Closed Testing Procedure Based on a Multivariate Test
293(1)
Summary and Composite Measures
294(1)
Summary
294(1)
Composite Endpoints and Summary Measures
295(28)
Introduction
295(3)
Composite Endpoints versus Summary Measures
295(2)
Strengths and Weaknesses
297(1)
Choosing a Composite Endpoint or Summary Measure
298(1)
Summarizing across HRQoL Domains or Subscales
299(6)
Weighting Proportional to the Number of Questions
301(1)
Factor Analytic Weights
301(2)
Patient Weights
303(1)
Statistically Derived Weights: Inverse Correlation
303(2)
Summary Measures across Time
305(6)
Notation
306(1)
Simple Linear Functions
306(3)
Area Under the Curve (AUC)
309(2)
Composite Endpoints across Time
311(9)
Notation
312(1)
Missing Data
313(1)
Average Rate of Change (Slopes)
314(1)
Area Under the Curve (AUC)
315(3)
Average of Ranks
318(1)
Analysis of Composite Endpoints
319(1)
Summary
320(3)
Quality Adjusted Life-Years (QALYs) and Q-TWiST
323(14)
Introduction
323(1)
QALYs
323(5)
Estimation of QALYi
324(3)
Analysis of QALYi
327(1)
Q-TWiST
328(7)
Kaplan-Meier Estimates of Time in Health States
329(2)
Case 1: Known Estimates of UTOX and UREL
331(1)
Case 2: Two Unknown Weights That Are Equal across Treatments
332(2)
Proportional Hazards Estimates of Time in Health States
334(1)
Summary
335(2)
Analysis Plans and Reporting Results
337(20)
Introduction
337(1)
General Analysis Plan
338(4)
Goals of the Trial and Role of HRQoL
338(1)
Primary versus Secondary Endpoints
338(1)
Composite Endpoints or Summary Measures
339(1)
Comparison Procedures
339(1)
Who Is Included?
340(1)
Models for Longitudinal Data
341(1)
Missing Data
341(1)
Sample Size and Power
342(14)
Simple Linear Combinations of β
344(1)
Longitudinal Studies with Repeated Measures
345(5)
Longitudinal Data and Growth Curve Model
350(2)
Other Considerations
352(4)
Reporting Results
356(1)
Summary
356(1)
Appendix C: Cubic Smoothing Splines 357(2)
Appendix P: PAWS/SPSS Notes 359(4)
Appendix R: R Notes 363(6)
Appendix S: SAS Notes 369(8)
References 377(18)
Index 395
Diane L. Fairclough is a professor in the Department of Biostatistics and Informatics in the Colorado School of Public Health and director of the Biostatistics Core of the Colorado Health Outcomes Program at the University of Colorado in Denver. She is also President of the International Society for Quality of Life Research. Dr. Faircloughs prior appointments include St. Jude Childrens Research Hospital, Harvard School of Public Health, and AMC Cancer Research Center.
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