Transcript Clinical Trials Why?

Controlling Errors in Medical
Studies: Overview
李世昌
銘傳大學 應用統計與資訊學系
December 01, 2011
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Agenda
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Statistical Designs in Medical Studies
Selection of the Control Group
Sample Size Determination
Randomization
Statistical Analysis
Quality Assurance (QA)
Non-inferiority Trials
International Conference for Harmonisation
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Experimental Errors
• Controlling and Minimizing
– Quality by Design
– QA in Medical Studies
• Controlling the Errors in Industry and Medical
Sectors
– Drugs/medical devices/vaccine/…
– Clinical Trials
• Why? How?
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Pharmaceutical Industry
• Research & Development
– Non-clinical Studies
• Search for treatments
• Lab studies
– Pre-clinical Studies
• Animal studies
• Pharmacokinetic (PK) studies
– Clinical Trials
• Human studies
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Objectives versus Conclusions
• The Essence of Rational Medical Study is to
Ask Important Questions and Answer them
With Appropriate Studies
– A study should be designed, conducted and
analysed according to sound scientific
principles to achieve their objectives; and
should be reported appropriately
• Statistical Approach in Design and Analysis
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Inferential Statistics
A Population of the Random Variable of X
A Random of Sample Size of n
Descriptive Estimates and
Statistical Analysis
Results and Conclusions
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Inferential Statistics
• A Population of Subjects
– A characteristic of interest, X ~ F(x; q)
• A Random Sample of Size n
– Each size of n in a population has an equal probability
to be selected
^ ~ G(x; e)
– Θ
• Descriptive and Statistical Methodology
– Graphs/charts, estimates, confidence intervals, tests of
hypothesis
– Statistical Models
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A Medical Study
All Patients with a Specific Disease
The Treatment of the Disease
A Study Group of Patients
Clinical Evidence of the Treatment
Clinical Conclusions
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Statistics versus Medical Studies
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The Human Experiment?
A Population? A Specific Disease?
The Treatments of Disease?
Clinical Response and Indices?
Staggered Entry?
Sample Size?
Randomization?
…
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Human Experiments
• Medical Ethics?
– Placebo? Standard/New Treatment?
– Informed Consent Form
• Patient Benefit and Risk?
– Efficacy and Safety Issues
• Clinical Evidence?
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Complicated Issues
• A Specific Disease to Study?
• A New Treatment of a Disease
– Any current standard treatments?
• How to Quantify a Clinical Benefit and
Minimized Adverse Effects?
– Efficacy and safety
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Existence of Errors and Bias
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Patients
Investigational Team
The Treatment
Clinical Instrument and Measurement
Unknown Factors
…
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Statistics in a Medical Study
Study Objectives:
Clinical indexes, efficacy variables/endpoints
Protocol and Design:
Clinical and design issues
Conduct Trial and Collect Data:
Ethic, accurate, validate, and reliable data
The Analysis and Results:
Interpretations and Conclusions
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The Distinction
• Medical Studies
– Objectives  IRB/DOH*  Conduction  Publication (literature
review)
• Clinical Trials
– Objectives  IRB/DOH  Conduction 
NDA+/Marketing/Publication
• Statistics and Regulatory Issues (IRB, DOH, CDE ++, …)
*IRB/DOH: Institutional Review Board/Department of Health
+NDA: New Drug Application
++ CDE: Center of Drug Evaluation
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Medical Studies and Clinical Trials
• Regulated Studies or Trials?
• Clinical Trials
– A medical study sponsored by a pharmaceutical
company or …
– A system of combing the variety of expertise
– New Drug Application (NDA) oriented
– Declaration of Helsinki
• Trial Quality Assured?
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Regulatory History in Medical Studies
• Declaration of Helsinki
– Ethics and Science
• Medical Journals
– Requirements on a submitted manuscript
• Regulatory Agency
– US/Food and Drug Administration (US/FDA), …
– Department of Health (DOH), Taiwan (TFDA)
• Nonprofit Organizations
– NIH, CDC, NCI, …
– Cancer center (MD Anderson, Mayo, Johns-Hopkins, SloanKettering, …)
• International Conference on Harmonisation (ICH)
– Guidelines on Efficacy, Safety, Quality, Multi-discipline
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Quality Assurance
• A System of a Process of Tasks being Done
– Designing, monitoring, documenting,
organizing, analyzing, and concluding
• Medical Research
– Ethics + IRB + Journal Review
• Clinical Trials
– Ethics + IRB + Regulations + ICH +…
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QA: Concepts
• Quality Assurance (QA)
– The systematic monitoring and evaluation of the various
aspects of a process and management to maximize the
probability that minimum standards of quality are being
attained by the entire process
(1) The Intended Purpose
(2) Minimize the Errors and Bias
(3) Systematic Approach
(4) Valid and Reliable Conclusions
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QC: Quality Control
• Statistical Quality Control (SQC)
– Accuracy of specifications
– Integrity and precision
• Total Quality of Management
– QC + QA + SOPs
– Monitoring and Auditing
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QA: Clinical Trials
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A Well-designed Protocol
Study Conduction and Adherence
Documentation
Data Management
Analysis and Interpretations
• Regulations
– International Conference on Harmonisation (ICH)
– Taiwan Food and Drug Administration (TFDA)
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QA: Education and Resources
• Education
– Trainings and Experience
– Academic Education
– Vocational Education and Training
• Resources
– Industry sector
– Government sector
– Scientific expertise
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Statistical Methodology
Point Estimate
Interval Estimation
Test of Hypothesis
Statistical Models
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Three Basic Statistical Methods
• Point Estimation
– No valid statement is made
• Interval Estimation
– (1-a)100% confidence of correctness
– The upper and lower bounds for estimation
• Test of Hypotheses
– Two hypothesis (Ho: no effect vs. Ha: effect size)
– Type I error rate (a)
– The power of test (1-b)
• Practical Meanings?
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Analysis of a Sample Data
• Is the Variable Well-defined?
• How are the Sample Data Collected From?
• Whether the Sample Data Represent the
Study Population?
• What is the Appropriate Analysis?
• How to Interpret the Results?
• Do the Conclusion Validated?
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A Medical Study?
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A Population of Patients?
A Group of enrolled Patients?
Are the Collected Clinical Data representative?
How to Reach the Scientific Evidence?
Are the Clinical Conclusions Valid and Reliable?
• Statistical Tools!!!
• How to Use the Statistical Methodology?
• How to Accomplish the Scientific Evidence?
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Design a Protocol
• Objectives
– Efficacy and/or Safety
– Primary/secondary variables
• Important Elements
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Controlled?
Number of patients?
Randomization and blindness?
Statistical methodology?
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Selection of a Control Group
• Purpose
– Minimize the bias in assessing the effect of test
treatment
• Choice of a Control Group
– Placebo or no treatment
– Active control
– Historical control
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Types of Comparisons
• Superiority
– Treatment A is better than treatment B
• Bioequivalence
– Treatment A is equivalent to treatment B
• Non-inferiority
– Treatment A is not inferior to treatment B
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A Population and A Sample
• A Population of Patients
– Objectives + inclusive criteria
• A Sample Clinical Data Sets
– Number of patients
• Enrolled? Evaluable?
– Exclusive criteria
• Safety issues, …
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A Population Model
All Patients
Control patients
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A random sample
from control patients
Test patients
A random sample
from test patients
Clinical data
of two groups
Statistical
Analysis
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An Invoked Model
All Patients
A subgroup
of patients
Control group
Test group
A random sample
from test group
A random sample
from control group
Statistical
Analysis
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Minimise Bias/Error and Assess Efficacy
• Statistical Principles and Data Integrity
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Selection of a control treatment
Sample size determination
Patients recruitment
Randomisation
Blinding
Compliance
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Clinical Designs
• Comparisons of Two Treatments (T vs. A)
– Equality
• Ho: mT-mA=0 vs. Ha: mT-mA 0
– Superiority
• Ho: mT-mA=0 vs. Ha: mT-mA>0
– Equivalence
• Ho: mT-mA L or mT-mA  U vs. Ha: L< mT-mA < U
• Bioequvalence (BE) studies
– Non-inferiority
• Ho: |mT-mA| M vs. Ha: |mT-mA|< M
• Designs
– Parallel (two independent samples)
– Crossover (blocking samples)
– Factorial (many independent samples)
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Clinical Endpoints
• Scientific Evidence?
– Valid Conclusions?
– Primary or Secondary?
• Statistical Concerns
– Type I error rate (a)
– Power
– Multiplicity adjustment of a
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Inclusions/Exclusions
• Define a Population and a Sample Data
– Clinical judgment?
– Might involve in violations/deviations
• Ethics and selection bias
– Sample size
• Intent-to-treat (ITT) and per protocol (PP)
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Examples
• 1. Parallel Design
– Two independent samples
• 2. Cross-over Design
– Paired samples
• 3. One-way Analysis of Variance
– Comparison of more than treatments
• 4. …
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Number of Patients
Formulas and Charts
Practical Meanings
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Number of Patients
• Sample Size Determination
– Information of (a, 1-b, s, treatment difference)
– Conclusion
• Intent-to-Treat/Per Protocol data set
• Inclusion/exclusion
• Violation/deviation
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Sample Size Calculation
• Primary Endpoint
• Precision or power approaches
– Parameters: Type I error rate, power, variance,
margin of error
– Formula or charts
• Consideration in survival trials
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Formulas
• Precision
( z2/a)2 (2s2)
• n= ---------------- ;
[mD0 – mDa]2
Power
(z2/a+zb)2 (2s2 )
n= --------------------[mD0 - mDa]2
• Practical Meanings?
• Survival Studies?
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Power Approach
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Randomization
Patient Allocations
Imbalance Issues
Prognostics
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Randomization
• Tradition
– A random sample of size n from a population
– Completely randomized design of Analysis of
Variance (ANOVA)
• Clinical trials
– Complete randomization
– Randomization using prognostic factors
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Maximum Power
• Equal Sample Size for t-test
• Balance Issue in Analysis of Variance
• Simple Randomization
• Bias Coin Randomization
• Stratified Randomization
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Randomization
A Sequence of Random Numbers which a Treatment
Assignment is based on
– Code, date, and time-point
1. Non-Adherence
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Human error
Training problem
Management problem
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2. Examples
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Simple Randomization
• Random Number Generator
– No prognostic factor considered
– Predictability
– Balance of treatment groups
• Example: Treatments A and B
– A sequence of random number generated by a validated computer
software
– 1 8 6 2 6 3 5 8 7 0 …
– Assign A if the random digit is 1-5, otherwise assign B
• Imbalance between Treatments A and B
– P[2:8] ≥ 0.05; P[40:60] ≥ 0.05; P[469:531] ≥ 0.05
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Biased Coin Design
• At Each Treatment Assignment, Assign the
Least Treatment with a Higher Probability
– Say, if D(i)=|n(A)-n(B)|≥ 2, then assign the
treatment to the least number with p=2/3 or 3/5
– If D(i)=0, then use p=1/2 to assign treatment
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Random Permuted Blocks
• Patient No. Treatment
1001
A
1002
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1003
B
1004
B
1005
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1006
B
1007
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1008
B
1009
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1010
B
1011
A
1012
A
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With a block size of 4
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Covariate-Adaptive Randomization
• Use of Prognostic Factors in Patient
Allocation
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Zelen’s Rule
Stratified Randomization
Taves’ Minimization
Pocock-Simon’s Procedure
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Stratified Randomization
Male
III
IV
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B
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B
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Female
III
IV
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B
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B
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B
B
B
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B
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B
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Tave’s Minimization
• Gender
– Female
– Male
Test
Control
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• Age
– 18-30
– 31-45
– 46-65
• Smoking
– Yes
– No
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Practical Issues
• Computer Resource to Implement
• Inactive Voice Response System (IVRS)
• Randomization Code
• Patient Log
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Informed consent and randomized dates
Dates and its sequence
Dates of subsequence visits
Termination date and related information
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QA Revisited
• Quality Assurance is a wide ranging concept
which covers all matters which individually
or collectively influence the quality of a
clinical trial
• Regulations
– GCP
• ICH E/S/Q/M Guidance's
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An Example of Statistical
Analysis
A Non-inferiority Trial
Test vs. Active Control
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Non-Inferiority Trials
• In Active Controlled Non-Inferiority (NI) Trials
– M=the effect size of active control
– Ho: |A-T|  M
• T has an effective size of M or more
– Ha: |A-T| < M (non-inferiority)
• T is non-inferior to the control by less than M
• The Effect Size of a Treatment
– Concurrent Placebo-Controlled Trials
– Ho: T-P ≤ 0
– Ha: T-P > 0 (superiority to show effect size of T group)
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Logics
• Comparative Effectiveness
– Test and Active-control groups: T and A
– Estimate Effect Size of the active-control
– Assumptions: assay sensitivity and constancy
• The NI Margin (M)
– M1: the entire effect of the active control assumed to be
present in the NI study
– M2: the largest clinically acceptable difference (degree
of inferiority) of the test drug compared to the active
control
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A Two-Step Process in a NI study
• M1: the effect size of active control
• M2: a specified portion of M1, based upon clinical
judgment
• The lower bound of a 95% confidence interval of
A-P
• The upper bound of a 95% confidence interval of
A-T obtained from the current study
– If the upper bound < M2, then the conclusion of noninferiority is declared
– The loss by the test product must be ruled out
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Example: Determination of an NI Margin
• SPORTIF V is a NI study that tested the novel
anticoagulant ximelagatran against the active control
warfarin.
– Warfarin is a highly effective, orally active anticoagulant for the
treatment of patients with non-valvular atrial fibrillation at risk of
thromboembolic complications (e.g., stroke, TIA, etc.).
– Six placebo-controlled studies of warfarin involving the treatment of
patients with non-valvular atrial fibrillation, all published between
the years 1989 and 1993.
– The primary results of these studies are summarized in Table 1 and
provide the basis for choosing the NI margin for SPORTIF V.
• FDA: Guidance for Non-inferiority Trials (March, 2010)
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Effect Size of an Active Control
• The Non-inferiority Margin
– The 1722 relative risks in each of the six studies
were combined to M=1.378
• In the SPORTIF V study
– The point estimate of the relative risk was 1.39
and the two-sided 95% CI for the relative risk
was (0.91, 2.12). The upper limit (2.12) is greater
than M (=1.378)
• Non-inferiority of ximelegatran to warfarin
is not demonstrated
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QA Revisited
• Quality Assurance is a wide ranging concept
which covers all matters which individually
or collectively influence the quality of a
clinical trial
• Data Management
– ICH Guidance
– GCP
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Data Management
Sponsor and Investigator
Quality Control
Quality Assurance
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Sponsor and Investigator
• A Protocol
– Many Procedures
– Data handling and Record keeping
• Trial Management
– Investigational team
– IRB
– Sponsor/CRO
• Package of reports to organize and present, …
• Clinical Study Report
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Quality Assurance (QA)
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Audit Policy
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SOPs
Independent unit or IRB
Monitoring System
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CRFs and medical records, physician notes,
documents, minutes
CRO, data query
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Documentations
• Administrative Documents
- Data lock/unlock, data clean, …
• Standard Operation Procedures (SOPs)
- Data management
- Key-in, security, …
- Computer facility
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ICH Documents
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Es: Efficacy (E1-E15)
Ss: Safety
Qs: Quality (Q1-Q13)
Ms: Multi-discipline
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ICH: Efficacy
• The work carried out by ICH under the
Efficacy heading is concerned with the design,
conduct, safety, and reporting of clinical trials.
It also covers novel types of medicines derived
from biotechnological processes and the use of
pharmacogenetics/genomics techniques to
produce better targeted medicines.
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ICH: Safety
• ICH has produced a comprehensive set of
safety guidelines to uncover potential risks
like carcinogenicity, genotoxicity and
reprotoxicity. A recent breakthrough has
been a non-clinical testing strategy for
assessing the QT interval prolongation
liability: the single most important cause of
drug withdrawals in recent years.
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ICH: Quality
• Harmonisation achievements in the Quality
area include pivotal milestones such as the
conduct of stability studies, defining
relevant thresholds for impurities testing and
a more flexible approach to pharmaceutical
quality based on Good Manufacturing
Practice (GMP) risk management.
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ICH: Multi-discipline
• Those are the cross-cutting topics which do
not fit uniquely into one of the Quality,
Safety and Efficacy categories. It includes
the ICH medical terminology (MedDRA),
the Common Technical Document (CTD)
and the development of Electronic
Standards for the Transfer of Regulatory
Information (ESTRI).
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ICH Efficacy Guidelines
• Guidelines Related to Statistical Issues
– Efficacy
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E8 (General Considerations)
E10 (Choice of Control Group)
E9 (GSP)
E6 (GCP)
E3 (CSR)
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Good Clinical Practice (GCP)
ICH E6
An Article on GCP Inspection
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Contemporary Clinical Trials
• A comparative method of evaluating quality of
international clinical studies in China: Analysis
of site visit reports of the Clinical Research
Operations and Monitoring Center (Chang, Xu,
and Fan, 2008)
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Conclusions
• The implemented China CIPRA program was at least
comparable and equivalent to the US studies in GCP
adherence
• The program's GCP performance was satisfactory in
overall and for the selected critical GCP items. Protocol
adherence was the major area that the China CIPRA
program did more satisfactory than US sites; however,
China and US sites both need close attention and more
improvements in the areas of protocol adherence and
essential documents/patient records.
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Have a Nice Evening!
李世昌
(02) 2882-4564 分機3494
[email protected]
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