1. 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

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

3. 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

4. Examples of placebo and active treatment controls
Canadian Beclomethasone Dipropoinate Salmethrol Xinafoate Study Group (NEJM, 1997; 337: )
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

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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

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

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

15. 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: )
Jen-pei Liu, PhD

16. Accidental Bias
Jen-pei Liu, PhD

17. Accidental Bias
Jen-pei Liu, PhD

18. Accidental Bias
Jen-pei Liu, PhD

19. Accidental Bias
Jen-pei Liu, PhD

20. Accidental Bias
Jen-pei Liu, PhD

21. 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

22. 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

23. 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

24. 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, BABA, 5. ABBA, 6. BAAB
Generate a random permutation of 1-6： ABAB BAAB AABB BABA BBAA ABBA
Jen-pei Liu, PhD

25. 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

26. 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

27. 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

28. Randomization (Minimization)
Covariate Placebo Test Drug N
Age
<
>=
Peak flow rate (mL/s)
<
>=
AUC-7 symptom score
<=
>=
Jen-pei Liu, PhD

29. 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

30. 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

31. Correct Random Assignment
Patient No. Random Code Date
Jen-pei Liu, PhD

32. Incorrect Random Assignment
Patient No. Random Code Date
Jen-pei Liu, PhD

33. 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

34. 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

35. Unequal Allocation Allocation Relative efficiency 1:1 1 6:4 0.96
1:1 1 6: 2: 7: 8:
Jen-pei Liu, PhD

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

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

38. 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

39. 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

40. 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

41. 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

42. 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

43. 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

44. 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

45. 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

46. 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

47. 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

48. 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

49. 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

50. 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

51. 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

52. 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

53. 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

54. Prepared by Jen-pei Liu, PhD
Simon’s design
Procedure
Stage 1: If X1 > r go to stage 2
≦ r 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

55. 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

56. 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

57. 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

58. 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 Dec 4;337(23):
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 Feb 1;334(5): 6
Wernicke JF, Dunlop SR, Dornseif BE, et al. Fixed-dose fluoxetine therapy for depression. Psychopharmacol Bull. 1987;23(1):164-8.

59. 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。 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。 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。 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): 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。

60. 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。 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。 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。 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。 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。

61. 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。
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 )，p 。本作品依據著作權法第 46、52、65 條合理使用。 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。 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 )，p 。本作品依據著作權法第 46、52、65 條合理使用。

62. 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。 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 )，p 。本作品依據著作權法第 46、52、65 條合理使用。
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。

63. 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。 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。 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。 56