Design and analysis of quality of life studies in clinical : trialsinterdisciplinary statistics

Title:

Personal Author:

Fairclough, Diane Lynn

Series:

Interdisciplinary statistics

Publication Information:

Boca Raton : Chapman & Hall/CRC, 2002

ISBN:

9781584882633

Subject Term:

Clinical trials -- Longitudinal studies

Clinical trials -- Statistical methods

Quality of life -- Research -- Methodology

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000004734939	R853.C55 F34 2002	Open Access Book	Book	Searching... Unknown

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.

Author Notes

Diane L. Fairclough is an Associate Professor at the Colorado Health Outcomes Program and Department of Preventive Medicine and Biometry, University of Colorado Health Sciences Center, Denver, Colorado, USA

Preface	p. xv
Acknowledgments	p. xix
1 Introduction	p. 1
1.1 Health-related quality of life	p. 1
1.2 Measuring health-related quality of life	p. 2
Characteristics of various measures	p. 2
Health status vs. patient preferences	p. 2
Objective vs. subjective	p. 4
Generic vs. disease-specific instruments	p. 4
Global index vs. profile of domain-specific measures	p. 4
Response format	p. 6
Period of recall	p. 6
1.3 Example 1: Adjuvant breast cancer trial	p. 7
Patient selection	p. 7
Treatment	p. 7
Quality of life measure	p. 8
Timing of HRQoL assessments	p. 8
Questionnaire completion/missing data	p. 10
1.4 Example 2: Advanced non-small-cell lung cancer (NSCLC)	p. 10
Treatment	p. 12
Quality of life measure	p. 12
Timing of assessments	p. 13
Questionnaire completion/missing data	p. 13
1.5 Example 3: Renal cell carcinoma trial	p. 15
Patient selection	p. 15
Treatment	p. 15
Quality of life measure	p. 15
Timing of HRQoL assessments	p. 16
Questionnaire completion/missing data	p. 17
1.6 Summary	p. 18
2 Study Design and Protocol Development	p. 19
2.1 Introduction	p. 19
2.2 Background and rationale	p. 19
2.3 Research objectives	p. 20
Domains of interest	p. 21
Pragmatic vs. explanatory inference	p. 21
2.4 Selection of subjects	p. 22
2.5 Longitudinal designs	p. 23
Event- or condition-driven designs	p. 23
Time-driven designs	p. 24
Timing of the initial HRQoL assessment	p. 24
Timing of the follow-up HRQoL assessments	p. 24
Timing of HRQoL assessments when therapy is cyclic	p. 25
Trials with different schedules of therapy	p. 25
Frequency of evaluations	p. 25
Duration of HRQoL assessment	p. 26
Assessment after discontinuation of therapy	p. 27
2.6 Selection of a quality of life measure	p. 27
Trial objectives	p. 28
Validity and reliability	p. 29
Appropriateness	p. 30
2.7 Conduct	p. 31
Mode of administration	p. 31
Data collection and management	p. 32
Avoiding missing data	p. 32
Education	p. 33
Forms	p. 34
Explicit procedures for follow-up	p. 34
Scoring instruments	p. 35
Reverse coding	p. 35
Scoring multi-item scales	p. 35
Item nonresponse	p. 37
SAS example	p. 38
2.8 Summary	p. 39
3 Models for Longitudinal Studies	p. 41
3.1 Introduction	p. 41
Repeated measures models	p. 41
Growth curve models	p. 42
Selection between models	p. 42
Adjuvant breast cancer study (Example 1)	p. 42
NSCLC study (Example 2)	p. 43
Renal cell carcinoma study (Example 3)	p. 43
3.2 Building the analytic models	p. 43
Statistics guiding model reduction	p. 44
Likelihood ratio tests	p. 44
Other statistics	p. 46
3.3 Building repeated measures models	p. 46
Mean structure	p. 46
SAS example of a cell means model	p. 47
Covariance structures	p. 48
Unstructured covariance	p. 48
Structured covariance	p. 49
SAS example of a repeated measures model	p. 50
Hypothesis testing	p. 52
3.4 Building growth curve models	p. 54
Model for means	p. 54
Polynomial models	p. 54
Piecewise linear regression	p. 54
Covariance structure	p. 57
Variance of random effects	p. 57
Variance of residual errors	p. 58
SAS example of a polynomial growth curve model	p. 59
Fully parameterized model for the means	p. 59
Covariance structure	p. 59
Model reduction	p. 63
Estimation	p. 63
Hypothesis testing	p. 64
SAS example of a piecewise linear regression model	p. 65
Fully parameterized model for the means	p. 65
Covariance structure	p. 65
Model reduction	p. 66
Estimation	p. 66
Testing	p. 67
3.5 Summary	p. 68
4 Missing Data	p. 69
4.1 Introduction	p. 69
Terminology	p. 69
Why are missing data a problem?	p. 69
How much data can be missing?	p. 70
Similar patterns of dropout among intervention arms	p. 71
Prevention	p. 71
4.2 Patterns of missing data	p. 71
Example: NSCLC study	p. 72
4.3 Mechanisms of missing data	p. 72
Notation	p. 72
Example	p. 73
The concept	p. 74
MCAR: Missing completely at random	p. 75
The concept	p. 75
Covariate-dependent dropout	p. 75
Identifying covariate-dependent missingness	p. 76
Example: NSCLC study	p. 76
Analytic methods	p. 77
MAR: Missing at random	p. 77
The concept	p. 77
Identification of dependence on observed data (Y[subscript i superscript obs])	p. 78
Analytic methods	p. 80
A test of MCAR vs. MAR for multivariate normal data	p. 81
Notation	p. 81
Test statistic	p. 81
NSCLC example	p. 81
Implementing in SAS	p. 82
MNAR: Missing not at random	p. 84
The concept	p. 84
Analytic methods	p. 84
Identification of dependence on unobserved data, Y[subscript i superscript mis]	p. 85
Example: NSCLC study	p. 85
4.4 Renal cell carcinoma study	p. 87
Plotting outcome by dropout	p. 89
4.5 Summary	p. 90
5 Analytic Methods for Ignorable Missing Data	p. 93
5.1 Introduction	p. 93
Hypothetical example	p. 93
5.2 Repeated univariate analyses	p. 94
NSCLC example	p. 96
5.3 Multivariate methods	p. 96
Complete case analysis (MANOVA)	p. 97
NSCLC example	p. 98
Maximum likelihood estimation with all available data	p. 101
NSCLC example	p. 102
Further comments	p. 104
Exclusion of subjects	p. 104
Exclusion of observations	p. 105
5.4 Baseline assessment as a covariate	p. 105
NSCLC example	p. 107
5.5 Change from baseline	p. 108
NSCLC example	p. 109
5.6 Adding other baseline covariates	p. 110
NSCLC example	p. 111
5.7 Empirical Bayes estimates	p. 112
5.8 Summary	p. 114
6 Simple Imputation	p. 115
6.1 Introduction	p. 115
Limitations of simple imputation	p. 116
NSCLC example	p. 116
6.2 Mean value substitution	p. 117
6.3 Explicit regression models	p. 118
Identification of the imputation model	p. 119
Simple univariate regression	p. 120
Conditional predicted values	p. 123
6.4 Last value carried forward	p. 125
[delta]-Adjustments	p. 126
Arbitrary high or low value	p. 127
6.5 Underestimation of variance	p. 128
6.6 Sensitivity analysis	p. 130
6.7 Summary	p. 130
7 Multiple Imputation	p. 131
7.1 Introduction	p. 131
7.2 Overview of multiple imputation	p. 131
Step 1 Selection of the imputation procedure	p. 131
Step 2 Generation of M imputed data sets	p. 132
Step 3 Analysis of M data sets	p. 132
Step 4 Combining results of M analyses	p. 132
7.3 Explicit univariate regression	p. 133
Identification of the imputation model	p. 133
Computation of imputed values	p. 134
Practical considerations	p. 135
Extensions to longitudinal studies	p. 135
Assumptions	p. 135
NSCLC example	p. 136
7.4 Closest neighbor and predictive mean matching	p. 140
Closest neighbor	p. 142
Predictive mean matching	p. 142
7.5 Approximate Bayesian bootstrap	p. 142
The basic procedure	p. 143
Extensions to longitudinal studies	p. 144
Propensity scores	p. 144
Practical issues	p. 144
The assumptions	p. 145
Nonignorable missing data	p. 145
7.6 Multivariate procedures for nonmonotone missing data	p. 146
NSCLC example	p. 146
7.7 Combining the M analyses	p. 147
SAS example	p. 148
7.8 Sensitivity analyses	p. 150
7.9 Imputation vs. analytic models	p. 151
7.10 Implications for design	p. 152
7.11 Summary	p. 152
8 Pattern Mixture Models	p. 153
8.1 Introduction	p. 153
NSCLC example	p. 154
8.2 Bivariate data (two repeated measures)	p. 155
NSCLC example	p. 156
Complete-case missing variable (CCMV) restriction	p. 156
NSCLC example	p. 158
Brown's protective restrictions	p. 158
NSCLC example	p. 159
Sensitivity analyses with intermediate restrictions	p. 160
Large-sample inferences for [mu subscript 2]	p. 160
NSCLC example	p. 161
8.3 Monotone dropout	p. 161
NSCLC study	p. 162
Complete-case missing value restriction	p. 162
Available case missing value restriction	p. 164
Neighboring case missing value restriction	p. 164
Comparison of CCMV, ACMV, and NCMV estimates	p. 167
8.4 Parametric models	p. 167
Linear trends	p. 168
Variance estimation	p. 172
SAS example of a pattern mixture model	p. 174
Step 1 Estimates of [pi superscript P subscript h]	p. 174
Step 2 Estimates of [beta superscript P subscript h]	p. 174
Step 3 Pooling estimates and computing variance	p. 175
Step 4 Hypothesis testing	p. 177
8.5 Additional reading	p. 178
Extensions of bivariate case	p. 178
Extensions of the sensitivity analysis	p. 178
Nonparametric analyses	p. 178
8.6 Algebraic details	p. 178
Simple linear regression of Y on X	p. 178
Complete-case missing variable restriction	p. 179
Equation 8.9	p. 179
Equation 8.11	p. 179
Brown's protective restriction	p. 179
Equation 8.17	p. 179
Equation 8.19	p. 180
Other	p. 180
8.7 Summary	p. 181
9 Random-Effects Mixture, Shared-Parameter, and Selection Models	p. 183
9.1 Introduction	p. 183
Mixture models	p. 183
Selection models	p. 184
Overview	p. 184
9.2 Conditional linear model	p. 185
Testing MAR vs. MNAR under assumptions of conditional linear model	p. 187
NSCLC example	p. 187
Estimation of the standard errors	p. 192
Assumptions	p. 192
Random-coefficient mixture model	p. 192
9.3 Joint mixed-effects and time to dropout	p. 193
Testing MAR vs. MNAR under the assumptions of the joint model	p. 194
Selection or mixture model?	p. 195
NSCLC example	p. 195
Initial estimates	p. 197
Extension to more complex mixed-effects models	p. 198
Renal cell carcinoma example	p. 198
9.4 Selection model for monotone dropout	p. 198
Outcome-dependent selection model	p. 201
NSCLC example	p. 201
Oswald program	p. 206
Longitudinal model	p. 206
Dropout model	p. 208
Oswald warnings	p. 210
9.5 Advanced readings	p. 210
Intermittent missing data	p. 210
More selection models	p. 210
Heckman probit stochastic dropout model	p. 210
Wu and Carroll	p. 210
Mori	p. 210
Nonparametric analyses	p. 211
9.6 Summary	p. 211
10 Summary Measures	p. 213
10.1 Introduction	p. 213
Addressing multiplicity of endpoints	p. 213
Summary measures vs. summary statistics	p. 213
Strengths and weaknesses	p. 215
Easier interpretation	p. 215
Increased power	p. 215
Weakness	p. 215
10.2 Choosing a summary measure	p. 215
10.3 Constructing summary measures	p. 217
Notation	p. 220
Missing data	p. 220
Average rate of change (slopes)	p. 222
NSCLC example	p. 222
Missing data	p. 222
Area under the curve	p. 225
Missing data	p. 225
Differences at baseline	p. 227
Average of ranks	p. 227
Missing data	p. 227
Univariate analysis of summary measures	p. 228
Stratified analysis of summary measures	p. 228
NSCLC example	p. 229
10.4 Summary statistics across time	p. 231
Notation	p. 231
Area under the curve	p. 231
Repeated measures	p. 231
Growth curve models	p. 232
10.5 Summarizing across HRQoL domains or subscales	p. 235
Summary measures	p. 235
Weighting proportional to the number of questions	p. 236
Factor analytic weights	p. 236
Patient weights	p. 237
Statistically derived weights: Inverse correlation	p. 238
Summary statistics	p. 239
10.6 Advanced notes	p. 240
Nonparametric procedures	p. 240
Combining HRQoL and time to event	p. 240
Area under the curve	p. 240
Latent variable models	p. 241
10.7 Summary	p. 241
11 Multiple Endpoints	p. 243
11.1 Introduction	p. 243
Limiting the number of confirmatory tests	p. 243
Summary measures and statistics	p. 244
Multiple comparison procedures	p. 244
11.2 Background concepts and definitions	p. 245
Univariate vs. multivariate test statistics	p. 245
Familywise and experimentwise error rates	p. 245
Global vs. individual tests	p. 246
11.3 Multivariate statistics	p. 246
Global tests	p. 246
Closed multivariate testing procedures	p. 246
Limitations	p. 248
11.4 Univariate statistics	p. 249
NSCLC example	p. 249
Alpha adjustments for K univariate tests	p. 249
Bonferroni adjustment	p. 249
Ruger's inequality	p. 252
Simes' global test	p. 252
Sequential rejective Bonferroni procedure	p. 253
p-value adjustments	p. 253
11.5 Resampling techniques	p. 254
11.6 Summary	p. 255
12 Design: Analysis Plans	p. 257
12.1 Introduction	p. 257
12.2 General analysis plan--Who is included?	p. 258
12.3 Models for longitudinal data	p. 259
Ignorable missing data	p. 259
Nonignorable missing data	p. 259
Event-driven designs and repeated measures models	p. 259
Time-driven designs and growth curve models	p. 260
Modification of analysis plan	p. 260
12.4 Multiplicity of endpoints	p. 260
Primary vs. secondary endpoints	p. 260
Summary measures	p. 261
Multiple comparison procedures	p. 261
12.5 Sample size and power	p. 262
Simple linear combinations of [beta]	p. 262
Basic assumptions	p. 264
Incomplete designs	p. 264
Example 1 Repeated measures	p. 266
Example 2 Growth curve model	p. 268
Other considerations	p. 270
Intermittent missing data patterns and time-varying covariates	p. 270
Unequal allocations of subjects to treatment groups	p. 270
Multivariate tests	p. 271
Small sample size approximations	p. 272
Restricted maximum likelihood estimation	p. 272
12.6 Reporting results	p. 272
12.7 Summary	p. 274
Appendix I Abbreviations	p. 275
Appendix II Notation	p. 277
Appendix III Formal Definitions for Missing Data	p. 281
References	p. 285
Index	p. 295

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