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Rare Solid Cancers: An Introduction - Slide 3 - P. Bruzzi - Methodological aspects of clinical research in rare cancers

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  • 1. Methodology of Clinical Research in Rare Tumors
    • Paolo Bruzzi
    • Clinical Epidemiology Unit
    • National Cancer Research Institute
    • Genova - Italy
    Stresa, March 31, 2011
  • 2. NCCN National Comprehensive Cancer Network Clinical Practice Guidelines in Oncolog y SOFT TISSUE SARCOMA
  • 3.
    • NCCN Categories of Consensus
    • Category 1
    • Category 2A
    • Category 2B
    • Category 3
  • 4.
    • NCCN Categories of Consensus
    • Category 1 : There is uniform NCCN consensus, ... … based on high-level evidence (ie, high-powered randomized clinical trials or meta-analyses)
  • 5.
    • NCCN Categories of Consensus
    • Category 1 : There is uniform NCCN consensus, …
    • Category 2A: The recommendation is based on lower level evidence and there is uniform NCCN consensus, .. .
  • 6.
    • NCCN Categories of Consensus
    • Category 1 : There is uniform NCCN consensus, …
    • Category 2A: There is uniform NCCN consensus, .. .
    • Category 2B : There is nonuniform NCCN consensus (but no major disagreement), .... based on lower level evidence,… A Category 2B designation should signal to the user that more than one approach can be inferred from the existing data
  • 7.
    • NCCN Categories of Consensus
    • Category 1 : There is uniform NCCN consensus, …
    • Category 2A: There is uniform NCCN consensus, .. .
    • Category 2B : There is nonuniform NCCN consensus (but no major disagreement), ....
    • Category 3 : There is major NCCN disagreement that the recommendation is appropriate
  • 8. 2005
  • 9.  
  • 10.
    • Sarcomas NCCN Guidelines 2005
    • Categories of Consensus:
    • 3: 1 recommendation
    • 2B: 7 recommendations
    • 1: 1 recommendation (STS of the extremities)
    • - Radiotherapy for Stage I T2a,b low grade
    • (Chemotherapy as primary treatment when unresectable: no longer 1)
    • ALL OTHER RECOMMENDATIONS ARE 2A!
  • 11. 2011
  • 12.
    • Sarcomas NCCN Guidelines 2011
    • Categories of Consensus:
    • 3: 0 recommendation
    • 2B: 12 recommendations
    • 1: 1 recommendation (STS of the extremities) For stage IB; final margins <1.0 cm pathway:
    • “ RT (category 1)” changed to “ Consider RT (category 1)”
    • ALL OTHER RECOMMENDATIONS ARE 2A!
  • 13.
    • Sarcomas NCCN Guidelines 2005 - 2011
    • Categories of Consensus:
    • Grade Number of recommendations
    • 2005 2011
    • 1: 1 1
    • 2B: 7 12
    • 3: 1 0
    • ALL OTHER RECOMMENDATIONS ARE 2A!
  • 14.
    • Category 2A.
    • .... , there is uniform consensus that the r ecommendation is appropriate. Lower level evidence is interpreted broadly , and runs the gamut from phase II or large cohort studies (Cook, 1992) to individual practitioner experience . Importantly, in many instances, the retrospective studies are derived from clinical experience of treating large numbers of patients at a member institution, so panel members have first-hand knowledge of the data. Inevitably, some r ecommendations must address clinical situations for which limited or no data exist. In these instances the congruence of experience-based opinions provide an informed if not confirmed direction for optimizing patient care . ......
    • ALL RECOMMENDATIONS ARE 2A UNLESS SPECIFIED
  • 15.
    • Low level of evidence
    • Vs
    • High level of agreement
  • 16. Available Evidence on treatments for Rare Tumors
    • Case Reports
    • Small Studies
    • Uncontrolled (Phase II?) Trials
    • Low quality trials (protocol, selection criteria, assessment of endpoints, exclusions, GCP, etc.)
    • LOW QUALITY EVIDENCE
  • 17. Available Evidence on treatments for Rare Tumors
    • LOW QUALITY EVIDENCE
    • CLINICAL GUIDELINES?
  • 18. Available Evidence on treatments for Rare Tumors
    • LOW QUALITY EVIDENCE
    • CLINICAL GUIDELINES?
    • CLINICAL DECISION?
  • 19. CONSEQUENCES
    • Clinical guidelines
      • Not directive
      • Questionable (arbitrary ‘subjective’ criteria)
      • Heterogeneous
  • 20. CONSEQUENCES
    • Clinical guidelines
      • Not directive
      • Questionable (arbitrary ‘subjective’ criteria)
      • Heterogeneous
    • Clinical Decision
      • Arbitrary
      • Patient’s Information & choice?
  • 21. Causes of these problems
    • Conventional statistical reasoning
    • Organizational issues
  • 22. Conventional Statistical Reasoning
    • Starting hypothesis (H0):
    • new treatment = standard one
  • 23. Conventional Statistical Reasoning
    • Starting hypothesis (H0):
    • new treatment = standard one
    • 2. To demonstrate: new treatment >> standard, reject null hypothesis
  • 24. Conventional Statistical Reasoning
    • Starting hypothesis (H0):
    • new treatment = standard one
    • To demonstrate: new treatment >> standard, reject null hypothesis
    • To reject null Hypothesis: Large Sample Size
  • 25. Foundations of statistics commonly used in medicine
    • Experiment : Only source of scientific knowledge (empirical knowledge)
    • Dominating theory: true until shown to be false (by the results of an experiment)
  • 26. Scientific Method Dominating Theory
  • 27. Scientific Method Dominating Theory Observations New Theory
  • 28. Scientific Method Dominating Theory Observations Experiment
  • 29. Scientific Method Dominating Theory Observations Experiment
  • 30. Scientific Method Dominating Theory Observations Experiment New Theory
  • 31. Scientific Method in Medicine Standard Therapy
  • 32. Scientific Method in Medicine Standard Therapy Laboratory New standard? or trials in other diseases
  • 33. Scientific Method in Medicine Standard Therapy Laboratory Clinical TRIAL or trials in other diseases
  • 34. Scientific Method in Medicine Standard Therapy Laboratory Clinical TRIAL or cl. studies H0?
  • 35. Scientific Method in Medicine Standard Therapy Laboratory Clinical TRIAL or cl. studies New Standard Th. H0? H1?
  • 36. Scientific Method in Medicine Standard Therapy Laboratory Clinical TRIAL or cl. studies New Standard Th. H0? H1?
  • 37. Scientific Method in Medicine Standard Therapy Laboratory Clinical TRIAL or cl. studies New Standard Th. H0? H1?
  • 38. Scientific Method in Medicine Standard Therapy Laboratory Clinical TRIAL or cl. studies New Standard Th. H1? H0?
  • 39. Scientific Method in Medicine Standard Therapy Laboratory Clinical TRIAL or cl. studies New Standard Th. H1? H0?
  • 40. Scientific Method in Medicine Standard Therapy Laboratory Clinical TRIAL or cl. studies New Standard Th. H1? H0?
  • 41. Scientific Method in Medicine Standard Therapy Laboratory Clinical TRIAL or cl. Studies New Standard Therapy H0?
  • 42. Progress of knowledge in Medicine (conventional statistics)
    • Dominant theory is true (=standard therapy is better) until sufficient evidence becomes available against it
  • 43. Progress of knowledge in Medicine (conventional statistics)
    • Dominant theory is true (=standard therapy is better) until sufficient evidence becomes available against it
    • To this purpose, only evidence collected within one or more trials aimed at falsifying it can be used
  • 44. Progress of knowledge in Medicine (conventional statistics)
    • Dominant theory is true (=standard therapy is better) until sufficient evidence becomes available against it
    • To this purpose, only evidence collected within one or more trials aimed at falsifying it can be used
    • No use of
      • External evidence
      • Evidence in favor of…
  • 45. Consequence of conventional statistical reasoning
    • Large Amounts
    • of HIGH-QUALITY EVIDENCE
    • are needed to show that the new therapy is
    • More effective than the standard (if any)
    • EFFECTIVE
  • 46. Example Mortality Tumor X Nil vs A 15% vs 12.5% N=12000 P = 0.0007 H0 Rejected: A is effective in X
  • 47. Example Mortality Tumor X Nil vs A 15% vs 12.5% N=12000 P = 0.0007 Tumor Y Nil vs A 15% vs 7.5% N= 240 P=0.066 H0 not rejected: A not shown effective in y
  • 48. Conventional Statistical Rules
    • A study must have an adequate size
  • 49. Conventional Statistical Rules
    • A study must have an adequate size
    • Required Size, based on:
      • Significance level (usually 5%)
      • Minimal clinically worthwhile difference
      • Power (usually 80-90%)
  • 50. Conventional Statistical Rules
    • A study must have an adequate size
    • Required Size, based on:
      • Significance level (usually 5%)
      • Minimal clinically worthwhile difference
      • Power (usually 80-90%)
    • Results: Test of significance
      • P<0.05 = Positive Study
      • P>0.05 = Negative Study
  • 51. Adequate size
    • Test of significance
      • To have a good chance to reject the null hypothesis when wrong (= power) large sample size or large difference
    • Point Estimates +/- 95% CI’s
      • To reduce uncertainty, large sample size
  • 52. How large? – Needed number of events  =0.05, power = 80%
  • 53. Sample Size in cancer clinical trials
    • In trials in early disease, cumulative mortality from 10% to 70%: 500-5000 pts
    • In trials in advanced disease, cumulative mortality from 50% to 90%: 3 00-1000 pts
  • 54. Rare tumours ?
    • Incidence ?
    • Site, Histology, Stage
    • Patients characteristics (age, sex )
    • P revious treatments
    • Biology, Genetics
    • INCIDENCE OF CLINICAL CONDITIONS
  • 55. Example: Adjuvant therapy in N- triple negative premenopausal BC
  • 56. Example: Adjuvant therapy in N- triple negative premenopausal BC
    • New cases each year in Italy 30,000
    • Premenopausal: 1/3 10,000
    • Upfront radical surgery 8/10 8,000
    • HER2 neg: 4/5 6,400
    • ER- PgR- 1/4 1.600
    • N- 1/2 800
    • N. of pts needed for the trial = 1500/2000
    • Rate of acceptance to enter a RCT?
  • 57. Consequences
    • If
    • it is possible to assemble (in a reasonable time) only a limited number of patients,
    • and the efficacy of a new treatment is not outstanding,
    • This efficacy CANNOT be demonstrated
  • 58.
    • For the large majority of rare tumors, there are no treatments of proven efficacy
    • (according to standard EBM criteria)
  • 59. Conventional Statistical Rules
    • A study must have an adequate size
  • 60. Conventional Statistical Rules
    • A study must have an adequate size
    Unjustified Implication
    • If an adequate size cannot be attained, no methodological ties
    • Small size Poor quality
  • 61. Poor Quality?
    • Study protocol
    • (Classified as Phase II trials)
    • No Randomised controls
    • Lack of planned comparisons with historical controls
    • Primary endpoint: Objective response
    • No statistical plan
  • 62. Common Solutions
    • National, European, worldwide cooperations
    • Prolonged accrual ?
    • Prolonged follow-up ?
  • 63. International Cooperations
    • In several rare tumors, necessary/sufficient to answer relevant clinical questions
    • Problems
      • Sponsor/Funds
  • 64. International Cooperations
    • In several rare tumors, necessary/sufficient to answer relevant clinical questions
    • Problems
      • Sponsor/Funds
      • Bureaucracy
  • 65. International Cooperations
    • In several rare tumors, necessary/sufficient to answer relevant clinical questions
    • Problems
      • Sponsor/Funds
      • Bureaucracy
      • Relevant clinical questions?
      • Need of hypothesis-generating trials
  • 66. Recommendations
    • - Encourage the implementation of network-based prospective clinical databases and tissue banks
    • - Call upon the research community to consider the testing of new agents in rare cancers patients as an essential part of the clinical drug development process of new drugs.
    • - Promote formal collaborations within the disease-oriented research communities to explore the potential of new agents in specific rare cancers
  • 67. International Cooperations
    • In many rare tumors
    • Low incidence
    • Stage – Age – Clinical history
    • Histological/Molecular Variants
    • International Cooperation: <50 cases/year
  • 68. Other solutions
    • Uncontrolled trials
    • Relaxed alfa error
    • Surrogate endpoints
  • 69. Uncontrolled trials
    • Marginal gains: 50% less patients
    • VALIDITY/RELIABILITY:
    • OVERWHELMING EVIDENCE AGAINST
    • Acceptable only for paradigm-changing treatments
  • 70. Relaxed alfa error
    • Risk of false positive results
    • Precision of the estimates
    • Marginal gains
      • For alfa = 0.1 (e.g. 1-sided tests)
      • 22% less patients are needed (78 instead of 100)
  • 71. Surrogate endpoints (SES)
    • Potentially substantial gains !
      • e.g.
      • if Objective resp. from 30% to 60% (100 pts )
      • and Objective resp.doubles survival (in responders),
      • (Initial) Hazard Ratio  0.82, > 800 events
  • 72. Problems with SES
    • Validation: Large RCT or meta-analysis, statistical problems (demonstration of no difference)
    • Extrapolations: Different diseases, different treatments
    • Few validated cancer SES are available, none for rare tumors
  • 73. Consequence In rare tumors/conditions, it is not possible to evaluate the effectiveness of a (new) treatment (?!?)
  • 74. What can be done?
  • 75. What can be done?
    • Recent developments (<10 yrs)
    • Bayesian Statistics
    • New types of systematic reviews
    • Adaptive trials
  • 76. What can be done?
    • Bayesian Statistics
    • New types of systematic reviews
    • Adaptive trials
  • 77. What can be done?
    • Bayesian Statistics
    • New types of systematic reviews
    • Adaptive trials
  • 78. Conventional Statistical Reasoning
    • Starting hypothesis (H0):
    • new treatment = standard one
    • To demonstrate: new treatment >> standard, reject null hypothesis
    • To reject null Hypothesis: Large Sample Size
    • Only information collected within the experiment used in design and interpretation of study results
  • 79. Weakness of conventional approach
    • The evidence supporting the study rationale is ignored in its design and analysis (H0)
    • Focus on significance testing (rejection of H0)
  • 80. Null Hypothesis (H0)?
    • Biological rationale
    • Evidence of activity
    • Efficacy in other diseases with similarities
    • Efficacy in other stages of the same disease
  • 81. Bayesian Approach
    • Focus on estimates of effect
    • Formal, explicit use of prior information
  • 82. Test of significance Mortality Tumor X Nil vs A P = 0.0007 Tumor Y Nil vs A P=0.066
  • 83. Test of significance vs Estimates of effect Mortality Tumor X Nil vs A 15% vs 12.5% N=12000 P = 0.0007 Tumor Y Nil vs A 15% vs 7.5% N= 240 P=0.066
  • 84. Estimates of effect + Prior Evidence Tumor X 5yrs mortality Trial 1 Nil vs A 15% vs 12.5% (advanced disease) N=12000 P = 0.0007 Trial 2 Nil vs A 15% vs 7.5% (early disease) N= 240 P=0.066
  • 85. What if A has a molecular taget present both in X and Y? Mortality Tumor X Nil vs A 15% vs 12.5% N=12000 P = 0.0007 Tumor Y Nil vs A 15% vs 7.5% N= 240 P=0.066
  • 86. Interpretation of the two trials CONVENTIONAL Tumor X: P = 0.0007 Tumor Y : P= 0.066 Efficacy of treatment A is proven in X, undemonstrated in Y
  • 87. Interpretation of the two trials CONVENTIONAL Efficacy of treatment A is proven in X, undemonstrated in Y BAYESIAN (Posterior) Probability that treatment A significantly (HR<0.9) lowers mortality in tumor X: 80% in tumor Y: 90%
  • 88. Proposed (Bayesian) methodology
    • Prior information  probability distribution of the likely effect of the exeprimental treatment
    • +
    • Trial results (if necessary and possible)
    • Posterior Probability distribution of the likely effect of the experimental treatment
    • (range of plausible effects)
    =
  • 89. Prior Evidence and Scientific Evidence
    • Prior evidence is a crucial component in the interpretation of any finding (e.g. X-ray)
    • Less direct evidence is required for decision when prior evidence is taken into account
    • Bayesian statistics allows to conjugate prior evidence with trial results
  • 90. Prior evidence
    • Already (implicitly) used in clinical guidelines and decisions in rare tumors
    • No explicit criteria in
      • Selection of evidence
      • Weighing of evidence
    • Non-quantitative approaches
  • 91. What can be done?
    • Bayesian Statistics
    • New types of systematic reviews
    • Adaptive trials
  • 92. Sources of prior evidence
    • Randomised Trials
    • Biological & Preclinical Studies
    • Case-reports
    • Uncontrolled studies
    • Studies with surrogate endpoints
    • Studies on other similar cancers
    • Studies on the same cancer in different stages
    • Others?
  • 93. Conventional meta-analyses
    • Randomised Trials
    • Biological & Preclinical Studies
    • Case-reports
    • Uncontrolled studies
    • Studies with surrogate endpoints
    • Studies on other similar cancers
    • Studies on the same cancer in different stages
    • Others?
  • 94. Conventional meta-analyses
    • Randomised Trials
    • Weighted exclusively based on their size
    • (and quality)
  • 95. Rare Tumors
    • Randomised Trials
    • Biological & Preclinical Studies
    • Case-reports
    • Uncontrolled studies
    • Studies with surrogate endpoints
    • Studies on other similar cancers
    • Studies on the same cancer in different stages
    • Others?
  • 96. Prior evidence and clinical trials
    • Need to develop and validate new (meta-analytic) approaches to summarize prior information in rare tumors
    • Requirements
      • Explicit
      • Quantitative
      • Reproducible
  • 97. Meta-analyses in rare tumors
    • NEED TO USE INFORMATION FROM STUDIES <100% VALID AND <100% PERTINENT TO THE QUESTION OF INTEREST, i.e.
    • Different cancers, treatments, endpoints
  • 98. Summarizing prior information in rare tumors
    • Each piece of information (study) has to be used, weighted according to its:
      • Precision
      • Quality
      • Pertinence (relevance to the study question )
  • 99. PERTINENCE ?
    • CANCER
    • TREATMENT CONTRAST
    • ENDPOINT
    • Arbitrary but explicit weights
  • 100. Advantages
    • All available (including indirect evidence) evidence is fully and explicitly exploited in
      • Summary estimates of effect (meta.-analyses)
      • Clinical Guidelines
      • Shared Decision making
    • Randomised Trials of small size (50-100 pts) may be sufficient to discard or accept as standard the new treatment
  • 101. Once the available evidence has been summarised, it is possible to estimate the probability that the new treatment, when compared to the standard is: a) Definitely worse: Stop b) Much better: RCT not ethical, confirmatory uncontrolled trials (e.g. GIST) c) Neither : RCT necessary and ethically justified
  • 102. How to use this approach in planning a new RCT
    • Realistic sample size projection (e.g. 50 events)
    • Review of the (pertinent?) literature
    • Construction of the prior
    • Consider possible scenarios for hypothetical results of the trial (e.g optimistic, neutral and pessimistic)
    • Update prior to give hypothetical posterior distributions
    • Examine possible impact of the new trial
  • 103.  
  • 104. How to use this approach in analysing a RCT
    • Summarize study results
    • Combine trial results (likelihood) and prior distribution to obtain posterior probability distribution of treatment effect
    • Decision
      • Adequate evidence against: Stop
      • Adequate evidence in favor: Stop
      • Still large uncertainty: Study Continues
  • 105. What can be done?
    • Bayesian Statistics
    • New types of systematic reviews
    • Adaptive trials
  • 106. Conventional Trials Methodology
    • Rigid separation of PHASES of development
    • Phase I -> MTD -> Dose increases in different groups of patients
    • Phase II -> Activity -> Uncontrolled trials
    • Phase III -> Efficacy -> RCT’s
  • 107. Conventional Trials Methodology
    • Within each phase/trial
    • One fixed primary aim/endpoint
    • Fixed eligibility criteria
    • Fixed treatment protocol
    • Prespecified type/number/timing of analyses: Results kept secret
    • Prespecified sample size
  • 108. Conventional Trials Methodology
    • Philosophy: Preserve alfa error (= control the risk of false positive results)
  • 109. Conventional Trials Methodology
    • Philosophy: Preserve alfa error (= control the risk of false positive results)
    • Consequence in rare tumors:
    • Alfa error is unknown, but very large
    • Substantial biases
    • Lack of statisticl planning
  • 110. Adaptive trials
  • 111. Adaptive trials
    • Within each phase/trial
    • One primary aim/endpoint
    • Fixed eligibility criteria
    • Fixed treatment protocol
    • Prespecified type/number/timing of analyses
    • Prespecified sample size
  • 112.  
  • 113. Adaptive design
    • FDA’s draft guidance for industry on adaptive design clinical trials
    • ( http://www.fda.gov/downloads / Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM201790.pdf ).
  • 114. FDA’s Guidance applies to A&WC studies
    • A&WC
    • “ Adequate and well-controlled effectiveness studies intended to provide substantial evidence of effectiveness required by law to support a conclusion that a drug is effective”
    • = Pivotal trials: BOTH PHASE II & III
    • (Exploratory Studies)?
  • 115. Adaptive design clinical trial
    • FDA’s Definition:
    • “… a study that includes a prospectively planned opportunity for modification of one or more specified aspects of the study design and hypotheses based on analysis of data (usually interim data) from subjects in the study”
  • 116. Why adaptative designs are so attractive ?
    • Early endpoints
    • Molecular Endpoints
    • Response rate
    • It is possible to obtain, during the trial,
    • crucial information to improve some of its design features
  • 117. Most conventional trials have an adaptative component
    • Stopping rules based on interim analyses :
    • Toxicity
    • Rejection of null-hypothesis
    • Futility
  • 118. Examples of well-understood adaptive designs
    • Adaptation of eligibility criteria.
    • Adaptations to maintain study power.
    • Adaptations for stopping early .
  • 119. Less well-understood types of adaptations
    • Adaptations for Dose Selection Studies.
    • Adaptive Randomization Based on Relative Treatment Group Responses
    • Adaptation of Sample Size Based on Interim-Effect Size Estimates
    • Adaptation of Patient Population Based on Treatment-Effect Estimates
    • Adaptation for Endpoint Selection Based on Interim Estimate of Treatment Effect
    • Adaptation of Multiple-Study Design Features in a Single Study
    • Adaptations in Non-Inferiority Studies
  • 120. I-SPY2
    • Phase II trial in neoadjuv. setting (>3cm BC)
    • Primary Endpoint: pCR
    • Adaptive design
    • Primary Aim: Compare the efficacy of new drugs in addition to standard CTX vs CTX
    • Seconday Aim: Test, validate and qualify biomarkers of activity of new drugs
    • GOAL: Identify subgroup-specific treatments
  • 121. I-SPY2 Design
  • 122.  
  • 123. I-SPY2 – Main features
    • Bayesian design
      • Pre-trial-> predictive (posterior) probability of efficacy
      • High predictive P: Regimens graduated with their corresponding biomarker
      • Low predictive P: Regimens dropped
    • Biomarkers:
      • Standard (Eligibility)
      • Qualifying
      • Exploratory
  • 124. I-SPY2 – Main features
    • Adaptations:
      • Bayesian Adaptative randomization based on accumulating data in biomarkers subgroups
      • Futility analyses to drop regimens uneffective in all subgroups
      • Sequential monitoring to graduate a drug+biomarker pair when adequate predictive P is achieved -> phase III trial
    • Informatics Portal to integrate and interpret huge amounts of complex and disparate data
  • 125. Concerns associated with adaptative design
    • Bias Associated with the Multiplicity of Options (False Positive results)
    • Difficulty in Interpreting Results When a Treatment Effect is Shown (Overestimate of effect size)
    • Operational Bias
  • 126. Concerns associated with adaptative design
    • Blinded interim analyses
    • Unblinded interim analyses
  • 127. Blinded interim analyses
    • Adaptations of eligibility criteria ….
    • Adaptations to maintain study power….
    • …… based on blinded interim analyses of aggregate data.
    • No bias
    • No need to be (but better if) planned in advance
  • 128.
    • Adaptations based on unblinded analyses
      • for stopping early
      • for dose selection studies
      • of patient subgroups based on treatment-effect estimates
      • for end-point selection based on interim estimates of treatment effect
    • Bias if not addressed in study design
  • 129. Requirements of adaptative trials
    • Adaptations based on unblinded analyses
    • Planned in advance (study design –protocol)
    • Statistical adjustments
  • 130. Requirements of adaptative trials
    • Adaptations for stopping early
    • Standard techniques for interim analyses
  • 131. Requirements of adaptative trials
    • Others Adaptations based on unblinded analyses
    • Planned in advance (study design-protocol-statistical plan-SOPs)
    • Statistical adjustments (larger sample size – stricter p values – Bayesian approach)
    • Specialised statistical support
  • 132. Unplanned adaptations based on unblinded analyses
    • Undermine the statistical validity of the study
    • Compromise the possibility to interpret its results
    • ALSO IN EXPLORATORY STUDIES
  • 133. Differences between the present and the proposed approach
    • Present :
      • Rational but informal integration of the available knowledge
    • Proposed
      • Formal, explicit and quantitative integration of the available knowledge
        • Verifiable quantitative methods
        • Sensitivity analyses
        • Focus on summary effect estimates
  • 134. Efficacy trials in rare tumors
      • Uncontrolled (phase II ) trials making unethical further efficacy (RCT) trials
      • Randomized activity trials followed by uncontrolled efficacy trials (with historical controls
      • RCT’s with surrogate endpoints
      • Adaptive, Bayesian, activity/efficacy RCT’s based on unconventional Systemtic Reviews
  • 135. References
    • Contact
    • [email_address]
  • 136. Examples of well-understood adaptive designs
    • Adaptation of eligibility criteria.
    • When enrolment is problematic or very slow, eligibility criteria can be altered to improve enrolment.
    • .
  • 137. Examples of well-understood adaptive designs
    • Adaptations to maintain study power based on blinded interim analyses of aggregate data.
    • Blinded analyses of interim data can provide clues that the sample size or the study duration needs to be increased in order to maintain the study power
  • 138. Examples of well-understood adaptive designs
    • Adaptations for stopping early .
    • Sometimes it may be necessary/convenient/useful to stop patients’ accrual and/or treatment and/or follow-up earlier than planned.
  • 139. Examples of less well-understood adaptive designs
    • Adaptations for dose selection studies
    • Adaptations of patient population based on treatment-effect estimates (subgroup analyses)
    • Adaptation for end-point selection based on interim estimates of treatment effect
  • 140. Examples of less well-understood adaptive designs
    • Adaptations for dose selection studies.
    • Studying a wide range of doses in a conventional trial design can require large sample sizes.
    • Adaptive designs can start with a wide range of doses and then drop those that are not producing beneficial effects,
    • Subsequent patients can be assigned to the more promising doses.
  • 141. Examples of less well-understood adaptive designs
    • Adaptation of patient population based on treatment-effect estimates.
    • Interim analyses of trial data
    • -> preferential benefit from a given therapeutic regimen in certain patient subgroups (e.g. biomarker status)
    • -> modification of inclusion criteria BASED ON TRIAL DATA
  • 142. Examples of less well-understood adaptive designs
    • Adaptation for end-point selection based on interim estimates of treatment effect.
    • Adaptations can be planned to enable the primary and secondary end points to be switched, or even to introduce a brand new primary end point.