臨床試驗 對照組、盲性作業、隨機分派 Control, Blinding, and Randomization 授課老師: 劉仁沛教授 國立台灣大學 與 國家衛生研究院 臨床試驗 對照組、盲性作業、隨機分派 Control, Blinding, and Randomization 【本著作除另有註明外，採取創用CC「姓名標示－非商業性－相同方式分享」台灣3.0版授權釋出】 Jen-pei Liu, PhD

Basic Design Considerations Methods to eliminate bias and to reduce variability Use of a control Blinding Randomization Jen-pei Liu, PhD

Types of controls Three components contained in an observed response The true pharmacological activity of the active ingredient The symptomatic relief provided by the placebo The natural reversible healing process The last two components can not be unbiasedly estimated without inclusion of a concurrent placebo. Jen-pei Liu, PhD

Examples of placebo and active treatment controls Canadian Beclomethasone Dipropoinate Salmethrol Xinafoate Study Group (NEJM, 1997; 337:1659-65) Patient population: 241 children, 6-14 years old with stable asthma Test treatment Long-acting β2-adrenergic-receptor agonist Salmethrol Xinafoate (50 ug twice daily) Active treatment concurrent control Glucocorticoid Beclomethasone (200 ug twice daily) / Placebo concurrent control Jen-pei Liu, PhD

Examples of no-treatment control Barst, et al. (NEJM 1996; 334:296-301) Patient population: 81 patients with primary pulmonary hypertension Treatment Epoprostenol + conventional therapy Concurrent no treatment control + conventional therapy Jen-pei Liu, PhD

Examples of dose-response controls Wernicke, et al (1987, PschB, 23:164-8) Patient population:345 patients satisfied DSMⅢcriteria and Ham-D>=20 Test treatments Fluoxetine:20, 40, and 60 mg Placebo concurrent control Jen-pei Liu, PhD

Bias Bias Operational bias: deviations in conduct The systematic tendency to make the estimate of a treatment effect deviate from its true value. Design, conduct, analysis, evaluation and interpretation of the results. Operational bias: deviations in conduct Statistical bias: deviations in all others Jen-pei Liu, PhD

The Blackwell-Hodges Diagram Selection Bias The Blackwell-Hodges Diagram for Selection Bias Random Assignment (p=1/2) Investigator’s guess Test Drug (a) Placebo (b) Test Drug (a’)  n/2 –  Placebo (b’) n/2 –   n/2 n/2 Jen-pei Liu, PhD

Selection Bias Under random assignment E(na) = n/2 Under Ho: E(Y|a) = E(Y|b) =  Under investigator’s guest E(Y|a’) =  - /2 E(Y|b’) =  + /2 Jen-pei Liu, PhD

Selection Bias The expected selection bias 2( +  - n/2) = 2E(F) 2: Investigator’s bias E(F): the expected bias factor Jen-pei Liu, PhD

Selection Bias Random assignment is independent of patient characteristics and past assignments  E(F) = 0. Investigator’s bias: subjective judgment in the conduct, management, and assessment of patients if he or she knew the treatment assignment of the treatment. Jen-pei Liu, PhD

Blinding Blinding is the only way to prevent subjective judgment bias in the management, conduct, and evaluations of the trial. The inference for the treatment effect can be only unbiasedly made only if all aspects of patient characteristics, management, conduct, and evaluations except for the intervention are identical between the treatment groups. Jen-pei Liu, PhD

Double dummies Occasions Example Treatment A No matching placebo available Different frequencies Example Treatment A Time 6:00 12:00 18:00 24:00 A - A - B Pla B Pla B Pla B Pla Treatment B A Pla - A Pla - B B B Jen-pei Liu, PhD

Correct Guesses for BHAT Study Expected Propranolol Placebo Bias Factor Patient 79.9% 42.8% 380(N=3230) Investigator 69.9% 68.6% 568(N=3398) Clinic Coordinator 67.1% 70.6% 669(N=3552) Morgan (1985) Jen-pei Liu, PhD

Accidental Bias The bias of an estimate of the treatment effect from a model in which one or more important covariates, either known or unknown, is ignored (Efron, 1971, Biometrika; 58: 403-417) Jen-pei Liu, PhD

Accidental Bias Jen-pei Liu, PhD

Accidental Bias Jen-pei Liu, PhD

Accidental Bias Jen-pei Liu, PhD

Accidental Bias Jen-pei Liu, PhD

Accidental Bias Jen-pei Liu, PhD

Randomization Goals To introduce a deliberate element of chance into assignment of treatments to patients. To avoid bias in selection and allocation of subjects from the predictability of treatment assignments. To minimize the differences in relevant characteristics of the treatment groups and to produce similar distributions of prognostic factors between groups. To provide a sound statistical basis for the quantitative evaluation of the evidence relating to treatment effects. Jen-pei Liu, PhD

Randomization Methods Unrestricted randomization p = 0.5. imbalances E(nE) = n/2 V(nE) = n/4 When n=100, a 60/40 imbalance or greater occurs 5% of the time. Jen-pei Liu, PhD

Randomization Methods Permuted-block randomization Patients characteristics may change over time. Block length of 4 to 8 and not specified in the protocol Assign E to patient (bE + bc +1) in a block with probability P(E) = (BE – bE)/(BE + BC - bE - bc), where BE are BC the total number of assignments in a block and bE + bc the number of assignments already made in a block. Jen-pei Liu, PhD

Example： Generation of random codes by the method of permuted-block randomization # of treatment：2 –– A and B Length of blocks：4 Possible arrangements： 1. AABB, 2. BBAA, 3. ABAB, 4. BABA, 5. ABBA, 6. BAAB Generate a random permutation of 1-6：361425 ABAB BAAB AABB BABA BBAA ABBA Jen-pei Liu, PhD

Randomization Stratification By important prognostic factors: center, gender, age, baseline characteristics. Separate randomization within strata. Too many stratified factors can be infeasible and impractical and defeat the purpose of balanced effects Restricted to at most two factors Jen-pei Liu, PhD

Randomization Multicenter Trials Stratified randomization by center Centralized randomization Interactive Voice Randomization System (IVRS) Allows verification of inclusion and exclusion criteria Avoid attempts to protocol violation Tedious QA and QC to endure the correct assignment Envelope system for timely verification Jen-pei Liu, PhD

Randomization Adaptive randomization Treatment adaptive randomization The chance of the next assignment depends upon the number of patients currently assigned Response adaptive randomization The chance of the next assignment depends upon the response of the current patient. Covariate adaptive randomization (Minimization) The next assignment depends upon the covariates of the current patient. Jen-pei Liu, PhD

Randomization (Minimization) Covariate Placebo Test Drug N 106 107 Age <64 57 56 >=65 49 51 Peak flow rate (mL/s) <9 45 44 >=10 61 63 AUC-7 symptom score <=7 25 26 8-19 52 51 >=20 29 30 Jen-pei Liu, PhD

Randomization (Minimization) Selection of a measure of imbalance with respect to covariates For each patient, compute the value of the measure for each treatment Assign the patient to the treatment with the smaller sum The minimization can be also random with probability depends upon the imbalance. e.g., 3/4 or 2/3 suggested by Pocock (1984) Jen-pei Liu, PhD

Randomization (Minimization) Criteria: Age > 64; flow rate < 9 mL/s and AUC-7 >=20. The next patient is 68 years old with peak flow rate of 7.4 mL/s and a AUA-7 score of 21 points. Placebo: 50, 46, and 30 (total = 126) Test drug: 52, 45, 31 (total = 128) The sum of placebo is smaller Assign this patient to the placebo group. Jen-pei Liu, PhD

Correct Random Assignment Patient No. Random Code Date 001 13 01022007 002 14 01032007 003 15 01042007 004 16 01062007 006 17 01102007 007 18 01152007 008 19 01202007 009 20 02012007 010 21 02022007 013 22 02152007 014 23 02172007 016 24 02202007 Jen-pei Liu, PhD

Incorrect Random Assignment Patient No. Random Code Date 001 20 01022007 002 16 01032007 003 24 01042007 004 14 01062007 006 19 01102007 007 17 01152007 008 15 01202007 009 23 02012007 010 13 02022007 013 22 02152007 014 18 02172007 016 21 02202007 Jen-pei Liu, PhD

Randomization The ratio of the subjects randomized to treatments dose not have to be 1:1 The chance of assignment of the subjects to treatment dose not have to be equal. Jen-pei Liu, PhD

Unequal Allocation Given a total sample size n and assignment probability of p to the test drug and q=(1-p) to the control group The variance of the treatment effect is 2/npq The relative efficiency of equal to unequal allocation is 4pq Jen-pei Liu, PhD

Unequal Allocation Allocation Relative efficiency 1:1 1 6:4 0.96 1:1 1 6:4 0.96 2:1 0.89 7:3 0.84 8:2 0.64 Jen-pei Liu, PhD

Reading Chow and Liu (2013) Chapter 4 (randomization and blinding) Jen-pei Liu, PhD

Design II Early Phase Cancer Trials Prepared by Jen-pei Liu, PhD

Early Phase Cancer Trials Introduction Phase 0 Trials Phase I Trials Traditional 3+3 Accelerated Titration Design Continual Reassessment Method Phase II Trials Simon Two-stage Design Randomized Phase II Design Adaptation of Molecular Targeted Agents Prepared by Jen-pei Liu, PhD

Cancer Phase 0 Trials (Exploratory IND) Conduct before phase I trials To confirm endpoints of mechanism of action, bioavailability, pharmacodynamics, metabolic, and microdose assessments based on human, not extrapolated from animal studies More of a discovery, rather than development Number of patients: 10-15 Dose: subtherapeutic Prepared by Jen-pei Liu, PhD

Prepared by Jen-pei Liu, PhD Cancer Phase I Studies Objectives of cancer phase I trials for cytotoxic agents Determine the maximum dose and schedule of an investigational agent that patients can tolerate Provide the adverse events associated with agent administration in a dose-dependent fashion Use a variety of dose-escalation strategies for a target of a toxicity rate of 33% (?) or less Dose-limiting toxicity (DLT): unacceptable or unmanageable safety profile using some criteria such as grade 3 or greater according to US NCI Common Toxicity Criteria (CTC) DLT is usually evaluated at the first cycle of chemotherapy – acute toxicity (not chronic or cumulative effects) Prepared by Jen-pei Liu, PhD

Issues of Phase I Designs for MTD Complete the trials with Minimum amount of patients, and Minimum amount of time Recognize differential dosing-limiting clinical toxicity Ineffective at lower doses but fatal at higher doses Heterogeneous patients with different tumor types Include a stopping rule to allow flexibility to extend to higher or lower dose levels Investigators and regulatory agencies dictate the dose level for the first patient Prepared by Jen-pei Liu, PhD

Prepared by Jen-pei Liu, PhD Cancer Phase I Studies Designs for determination of maximum tolerable dose For PhaseⅠcancer chemotherapy (cytotoxic) Pre-selected fixed dose levels Maximum Tolerable Dose (MTD) Quantitative Definition Some percentile of a tolerance distribution w.r. to some definitive dose-limiting clinical toxicity, Storer (1989), Korn, et al (1994) Prepared by Jen-pei Liu, PhD

Drawbacks of the Current Practice for Standard Design No room for de-escalation No further analysis of data No objective estimation of MTD with statistical models No sampling error and no confidence interval. Prepared by Jen-pei Liu, PhD

Accelerated Titration Designs Richard Simon (1997) Rationale Address the flaws of traditional designs Attempt to obtain information about interpatient variability and cumulative toxicity stay for 3 courses to allow for intra-patient dose modifications Distinguish between moderate and dose-limiting toxicities Prepared by Jen-pei Liu, PhD

Accelerated Titration Designs Richard Simon (1997) Scheme The first stage 1 patient per level until 1 DLT or 2 moderate toxicities The second stage Traditional design, i.e. add 2 patients to the current dose that triggered the switch. Prepared by Jen-pei Liu, PhD

Accelerated Titration Designs Richard Simon (1997) MTD Estimated as the highest dose where at most 1/6 patients developed DLT Compared to traditional designs Go through the lower doses quickly, and thus reduces under-treated patients in absolute sense and speed up the completion Obtain similar estimate of MTD Provide more information. Upon completion, a model can be fitted to estimate inter- and intra-patient variability Require careful patient management to track the toxicity over multiple course Prepared by Jen-pei Liu, PhD

Bayesian Sequential Design The Continual Reassessment Method (CRM) O’Quigley, Pepe, Fisher (1990), O’Quigley (1992), Moller (1995) Step 1 Determine the dose-toxicity relationship Select fixed dose levels Determine the prior probability of slope Choose a fixed sample size Step 2 Determine the dose for the first patient as the dose level which produces the prior probability of dose-limiting clinical toxicity closest to p. Prepared by Jen-pei Liu, PhD

Bayesian Sequential Design Step 3 Update the posterior distribution of slope after each patient’s toxicity result becomes available. The dose level for the next patient is the one which gives the posterior probability of dose-limiting clinical toxicity closest to p. Step 4 Repeat Step 3 until the results of the last patient are available. Step 5 The estimated MTD is determined as the dose which minimizes some pre-selected criterion such as some quadratic error loss function with respect to the probability of dose-limiting clinical toxicity. Prepared by Jen-pei Liu, PhD

Advantages of Continual Reassessment Method Try to accommodate the situations Patients at high risk of death Fatal toxicity of new drug at high doses No efficacy at lower doses No information about dose range A well-defined goal of estimating a percentile of the dose-toxicity relationship It should converge to percentile with increasing sample size. Prepared by Jen-pei Liu, PhD

Issues of Continual Reassessment Method Assumption of a homogeneous patient population for the prior distribution of parameters It treats patients in cohorts of 1 It takes too long to complete the trial It is less conservative so that it may treat patients at very high dose levels Difficulty in choice of a criterion metric Prepared by Jen-pei Liu, PhD

Prepared by Jen-pei Liu, PhD Modifications of CRM Goodman, Zahurak & Piantadosi (1995) > 1 patient per cohort, dose increase is limited to 1 level, start at the lowest level Moller (1995) Combined with a preliminary up-and-down design, limit escalation to 1 level. Piantadosi & Liu (1996) Incorporate pharmacokinetics parameters Some of other simulation studies for comparing CRM’s with nonparametric approach: O’Quigley & Chevret (1991), Chevret (1993), Ahn (1996) A special issue of CRM in Statistics in Medicine was published in 2011 Prepared by Jen-pei Liu, PhD

Prepared by Jen-pei Liu, PhD Cancer Phase II Trials Endpoints: Response/tumor shrinkage measurements Most commonly used in phase II cancer trials Changes in radiographic measurements 4 categories: complete response, partial response, stable, and progression Progression-free survival Time to progression or death whichever occurs early Prepared by Jen-pei Liu, PhD

Prepared by Jen-pei Liu, PhD Cancer Phase II Trials A screening trial to allow early termination for inactivity or high activity. Define P0: undesirable response (CR+PR) rate (5-10%) P1: target response rate (> 25%) Prepared by Jen-pei Liu, PhD

Prepared by Jen-pei Liu, PhD Simon’s design Procedure Stage 1: If X1 > r1 go to stage 2 ≦ r1 stop and reject the drug Stage 2: If X1+X2 is ≦ r reject the drug > r accept the drug Given p0, p1, α, β, then (n1, n2, r1, r) are optimized to minimize either The expected sample size under p0, or The maximal sample size n1 + n2 Not readily evaluable, but tables of designs under different values of parameter are available from the paper. Prepared by Jen-pei Liu, PhD

Randomized Phase II Cancer Designs Reasons: Simon 2-stage design is a single arm trial Biased No control Estimated response rates treated as population rates Traditional phase II trials required large sample sizes Prepared by Jen-pei Liu, PhD

Randomized Phase II Cancer Designs Pick-the-winner selection designs Apply statistical methods for ranking and selection to choose a promising new agent for phase III confirmatory trials Not designed and no power to detect statistical significant differences in responses between treatments Randomization to eliminate bias To select the treatment with the greatest responses rate regardless of how small the differences Extension to survival and PFS Prepared by Jen-pei Liu, PhD

Adaptation of Molecular Targeted Agents Issues: Failure to translate the tumor shrinkage into patient benefit such as survival Different mechanisms from cytotoxic agents Quality of assays for biomarkers References: Chapter 6 of Chow and Liu (2013) Clinical Cancer Research: Vol. 15(6) March 15, 2009 Vol. 16(6) March 15, 2010 Prepared by Jen-pei Liu, PhD

版權聲明 頁碼 作品 版權圖示 來源/作者 1-63 本作品轉載自Microsoft Office 2010 PowerPoint 設計主題範本-Blends，依據Microsoft 服務合約及著作權法第46、52、65條合理使用。 4 Simons FE. A comparison of beclomethasone, salmeterol, and placebo in children with asthma. Canadian Beclomethasone Dipropionate-Salmeterol Xinafoate Study Group. N Engl J Med. 1997 Dec 4;337(23):1659-65. 本作品依據著作權法第 46、52、65 條合理使用。 5 Barst RJ, Rubin LJ, Long WA, et al. A comparison of continuous intravenous epoprostenol (prostacyclin) with conventional therapy for primary pulmonary hypertension. N Engl J Med. 1996 Feb 1;334(5):296-301. 6 Wernicke JF, Dunlop SR, Dornseif BE, et al. Fixed-dose fluoxetine therapy for depression. Psychopharmacol Bull. 1987;23(1):164-8.

版權聲明 頁碼 作品 版權圖示 來源/作者 8 《Design and analysis of clinical trials: concepts and methodologies》， 作者: Chow, SC, Liu, JP，出版社: Wiley(second edition )，p128。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html 13 《Design and analysis of clinical trials: concepts and methodologies》， 者: Chow, SC, Liu, JP ，出版社: Wiley(third edition )，p156。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html 14 《Design and analysis of clinical trials: concepts and methodologies》， 作者: Chow, SC, Liu, JP ，出版社: Wiley(third edition )，p160。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html 15 The bias of an estimate of the treatment … 《Forcing a sequential experiment to be balanced》， 作者: BRADLEY EFRON，出版：Biometrika (1971) 58 (3): 403-417. 本作品依據著作權法第 46、52、65 條合理使用。 22 Method for unrestricted randomization 《Design and analysis of clinical trials: concepts and methodologies》， 作者: Chow, SC, Liu, JP，出版社: Wiley(third edition )，p.130 。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html

版權聲明 頁碼 作品 版權圖示 來源/作者 23 Method for Permuted-block randomization 《Design and analysis of clinical trials: concepts and methodologies》， 作者:Chow, SC, Liu, JP，出版社: Wiley(third edition )，p.133。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html 24 《Design and analysis of clinical trials: concepts and methodologies》， 作者:Chow, SC, Liu, JP，出版社: Wiley(third edition )，p.134 。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html 25 《Design and analysis of clinical trials: concepts and methodologies》， 作者:Chow, SC, Liu, JP ，出版社: Wiley(third edition )，p123。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html 26 《Design and analysis of clinical trials: concepts and methodologies》， 作者:Chow, SC, Liu, JP，出版社: Wiley(third edition )，p238 。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html 27 《Design and analysis of clinical trials: concepts and methodologies》， 作者:Chow, SC, Liu, JP ，出版社: Wiley(third edition )，p137。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html

版權聲明 頁碼 作品 版權圖示 來源/作者 28 《Design and analysis of clinical trials: concepts and methodologies》， 作者:Chow, SC, Liu, JP，出版社: Wiley(third edition )，p139 。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html 29-30 Selection of a measure of imbalance … 《Design and analysis of clinical trials: concepts and methodologies》， 作者:Chow, SC, Liu, JP ，出版社: Wiley(third edition )，p139-140。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html 34 Given a total sample size n and assignment probability of p … 《Design and analysis of clinical trials: concepts and methodologies》， 作者:Chow, SC, Liu, JP，出版社: Wiley(third edition )，p161 。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html 40 Objectives of cancer phase I trials for cytotoxic agents… 《Design and analysis of clinical trials: concepts and methodologies》， 作者:Chow, SC, Liu, JP ，出版社: Wiley(third edition )，p212-213。本作品依據著作權法第 46、52、65 條合理使用。 http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html

版權聲明 頁碼 作品 版權圖示 來源/作者 41 Recognize differential dosing-limiting clinical toxicity 《Design and analysis of clinical trials: concepts and methodologies》， 作者:Chow, SC, Liu, JP ，出版社: Wiley(third edition )，p213。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html 42 Designs for determination of maximum tolerable dose 44-46 《Design and analysis of clinical trials: concepts and methodologies》， 作者:Chow, SC, Liu, JP ，出版社: Wiley(third edition )，p217-291。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html 47-48 The Continual Reassessment Method (CRM) 《Design and analysis of clinical trials: concepts and methodologies》， 作者:Chow, SC, Liu, JP ，出版社: Wiley(third edition )，p221。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html

版權聲明 頁碼 作品 版權圖示 來源/作者 51 《Design and analysis of clinical trials: concepts and methodologies》， 作者:Chow, SC, Liu, JP，出版社: Wiley(third edition )，p221。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html 52 《Design and analysis of clinical trials: concepts and methodologies》， 作者:Chow, SC, Liu, JP ，出版社: Wiley(third edition )，p229。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html 53 《Design and analysis of clinical trials: concepts and methodologies》， 作者:Chow, SC, Liu, JP ，出版社: Wiley(third edition )，p232。本作品依據著作權法第 46、52、65 條合理使用。http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470887656.html 56