Handbook for Clinical Trials of Imaging and Image-Guided Interventions

Description

Handbook for Clinical Trials of Imaging and Image-Guided Interventions is the first single-source, multi-disciplinary reference, based on the didactic sessions presented at the annual ‘Clinical Trials Methodology Workshop’ for radiologists, radiation oncologists and imaging scientists (sponsored by the Radiological Society of North America (RSNA)). It focuses on educating radiologists, radiation oncologists and those involved in imaging research with how to design and conduct clinical trials to evaluate imaging technology and imaging biomarkers. The internationally renowned contributors take a broad approach, starting with principles of technology assessment, and then move into specific topics covering the clinical trials of therapy and clinical research in imaging guided interventions including radiotherapy. They discuss the use of imaging as a predictor of therapeutic response, screening trial design, and the practicalities of how to run an efficient clinical trial and good working practices. Later chapters provide a comprehensive array of quantitative methods including: an introduction to statistical considerations in study design, biostatistical analysis methods and their role in clinical imaging research, methods for quantitative imaging biomarker studies, and an introduction to cost effectiveness analysis. Handbook for Clinical Trials of Imaging and Image-Guided Interventions will educate and prepare radiologists at all levels and in all capacities in planning and conducting clinical imaging trials. Title Page Copyright Page Contents Contributors Chapter 1 Imaging technology assessment 1.1 Six levels of evidence: a model of technology assessments in imaging 1.1.1 Level 1: technical efficacy 1.1.2 Level 2: diagnostic accuracy 1.1.3 Levels 3 and 4: impact on diagnostic thinking and therapeutic planning 1.1.4 Level 5: patient outcomes 1.1.5 Level 6: societal efficacy 1.2 Case examples: introducing technology assessment methods into study design 1.3 Conclusion References CHAPTER 2 Clinical trials of therapy 2.1 Phases I, II, and III: their goals and rationale 2.2 Therapeutic roles: prevention, cure, and palliation 2.3 Roles of different modalities 2.3.1 Surgery 2.3.2 Radiation therapy 2.3.3 Chemotherapy combinations 2.4 Study planning 2.4.1 Sources for protocol ideas 2.4.2 Choice of primary and secondary objectives 2.4.3 Ways to speed protocol writing 2.4.4 Inclusion and exclusion criteria 2.4.5 Subject registration and accrual goals 2.4.6 Evaluation schedule 2.4.7 Treatment schedule 2.4.8 Assessment of toxicity and reporting adverse events 2.4.9 Scheme for dose modification 2.4.10 Data collection and documentation 2.5 The protocol review process 2.6 Funding and budgeting 2.7 Enhancing protocol accrual References Chapter 3 Clinical trials of image-guided interventions including radiotherapy studies 3.1 Introduction 3.2 Establishing the context for IGI clinical trials 3.2.1 Clinical utilities 3.2.2 Translational continuum 3.2.3 Why do IGI clinical trials differ from other therapy or imaging trials? 3.3 A paradigm for considering IGI clinical trial design 3.4 Radiotherapy trials 3.4.1 Preclinical radiotherapy studies 3.4.2 Phase I radiotherapy studies 3.4.3 Phase II/III radiotherapy studies 3.5 Caveats in the design and conduct of IGI trials 3.5.1 Defining the clinical trial cohort 3.5.2 Standardization of the study procedures and quality assurance 3.5.3 IGI trial QA 3.5.4 IGI clinical trial endpoints: measures of success and failure 3.6 Potential impediments to the design and conduct of IGI clinical trials 3.6.1 Evaluation of the placebo effect 3.6.2 Masking or blinding of investigators 3.6.3 Impediments related to “standard of care” control interventions 3.6.4 Rapid evolution of the IGI device and related devices: multiple similar competitive IGI devices 3.6.5 The feasibility of RCTs of IGIs 3.6.6 Impediments to and pitfalls in the conduct of the trial 3.7 Special considerations and the future 3.7.1 Conclusion References CHAPTER 4 Imaging as a predictor of therapeutic response 4.1 Introduction 4.1.1 What is a biomarker? 4.1.2 Why do we need imaging biomarkers to direct therapy? 4.1.3 Types of therapeutic biomarkers 4.2 Imaging- versus tissue-based biomarkers 4.3 Examples of imaging biomarker applications 4.3.1 Prognostic markers 4.3.2 Predictive markers 4.3.3 Early response markers 4.3.4 Therapeutic benefit markers and surrogate measurements for long-term outcomes 4.4 Approach to biomarker study design 4.4.1 Standards for biomarker clinical trial design and results reporting 4.4.2 Integral versus integrated biomarkers 4.4.3 Altering therapy with early response markers 4.5 Practical considerations 4.6 Conclusions References CHAPTER 5 Screening trials and design 5.1 Principles of screening 5.1.1 Natural history of disease 5.1.2 Screening test characteristics 5.2 Screening cascade 5.2.1 Negative test results 5.2.2 Positive test results 5.2.3 Incidental findings 5.3 Developing a screening trial protocol 5.3.1 Identify the target population 5.3.2 Determine the screening regimens to be compared 5.3.3 Select clinical efficacy goals 5.3.4 Balance the benefits and potential harms of screening 5.4 Selecting a study design 5.4.1 Randomized controlled trials 5.4.2 Observational studies 5.4.3 Role of computer simulation modeling 5.4.4 Illustrative examples of imaging-based screening trials References CHAPTER 6 Practicalities of running a clinical trial 6.1 Types of clinical trials 6.2 Practical issues in designing a clinical trial 6.2.1 Do the right trial 6.2.2 Define trial complexity 6.3 Case of CCTA 6.3.1 Costs/Budget 6.3.2 Team 6.3.3 Feasibility 6.3.4 Good practices 6.3.5 Multicenter trials 6.4 Operational aspects of conducting a clinical trial 6.4.1 Good clinical practice (GCP) 6.4.2 The principal investigator 6.4.3 Data security 6.4.4 Study management 6.4.5 Processing workflow 6.5 Audits 6.6 Summary References CHAPTER 7 Statistical issues in study design 7.1 Diversity in imaging study designs 7.2 Building blocks of an imaging research study 7.2.1 Turning research questions into study objectives and statistical hypotheses 7.2.2 Sampling from patient and reader populations 7.2.3 What is a reference standard? 7.3 Strategies for efficient studies 7.3.1 Retrospective or prospective? 7.3.2 Paired designs 7.3.3 Augmented and enriched designs 7.3.4 Randomization 7.3.5 Interim analyses 7.4 Common biases in imaging studies 7.4.1 Spectrum bias 7.4.2 Verification bias 7.5 Sample size considerations 7.5.1 Underpowered studies: clinical versus statistical significance 7.5.2 Factors affecting sample size 7.5.3 Sample size calculations 7.5.4 Example sample size considerations for MRMC study References CHAPTER 8 Introduction to biostatistical methods 8.1 Role of biostatistics in clinical imaging research 8.2 Descriptive and exploratory data analysis 8.2.1 Summary statistics 8.2.2 Graphs 8.3 Confirmatory data analysis (i.e., hypothesis testing) 8.3.1 Formulating the null and alternative hypotheses 8.3.2 Significance level, test statistics, and p-values 8.3.3 Types of hypothesis tests 8.4 More on p-values 8.4.1 Problems with p-values 8.4.2 Relationship between p-values and confidence intervals 8.5 Advanced topics: statistical modeling 8.5.1 Linear regression 8.5.2 Logistic regression References CHAPTER 9 Methods for studies of diagnostic tests 9.1 Introduction 9.2 Sources of variation 9.3 Assessing the agreement among multiple tests 9.3.1 Categorical data and the kappa statistics 9.3.2 Example: kappa statistics 9.3.3 Continuous data and the ICC 9.3.4 Example: Bland–Altman plots 9.3.5 Example: ICC 9.4 Assessing the accuracy of diagnostic tests 9.4.1 Notation 9.4.2 Test performance: Sensitivity and specificity 9.4.3 Test performance: positive predictive value and negative predictive value 9.4.4 Receiver operating characteristic curves and the area under the curve 9.4.5 Example: illustration of ROC curves 9.5 Multireader multimodality studies 9.5.1 Example: reader study of computer-aided diagnostic for breast MR interpretation 9.5.2 A note on combining ROC curves or their AUCs 9.5.3 Elements of generalizability 9.6 Logistic regression as tool for obtaining AUCs References CHAPTER 10 Methods for quantitative imaging biomarker studies 10.1 Quantitative imaging biomarkers 10.2 Evaluating the technical performance of QIBs 10.2.1 Bias 10.2.2 Repeatability 10.2.3 Reproducibility 10.3 Evaluating analytical properties of QIBs 10.3.1 Detection capability: limits of blank, detection, and quantification 10.3.2 Linearity and commutability 10.3.3 Measuring (analytical) precision 10.3.4 Measuring change 10.3.5 Special situations: no meaningful zero and/or no ground truth 10.4 Evaluating clinical properties of QIBs 10.4.1 Diagnosis: sensitivity, specificity, and receiver operating characteristic curves 10.4.2 Prediction: positive and negative predictive value 10.4.3 Association with patient outcomes References CHAPTER 11 Introduction to cost-effectiveness analysis in clinical trials 11.1 Introduction 11.2 Types of economic analyses 11.2.1 Cost-effectiveness analysis 11.2.2 Cost–benefit analysis 11.2.3 Cost-minimization analysis 11.2.4 CEAs as part of comparative effectiveness research 11.2.5 Summary 11.3 Defining health-related quality of life 11.3.1 Why measure HRQOL? 11.3.2 Language of HRQOL 11.4 Hierarchy of HRQOL measures 11.4.1 Disease-specific instruments 11.4.2 Generic health status profiles 11.4.3 What constitutes a “good” measure? 11.4.4 Generic measures 11.4.5 Disease-specific measures 11.4.6 Other considerations in measure selection 11.4.7 Summary References Index EULA