STATISTICS 542 Introduction to Clinical Trials Protocols and Manual of Procedures

1
STATISTICS 542Introduction to Clinical
TrialsProtocols andManual of Procedures
2
Protocol vs Manual of Operations Analogy

• Protocol is general blueprint for
• investigators institutional review boards
• sponsor
• regulatory agencies
• Manual of Procedures is detailed construction
  document
• clinic staff
• data management staff

3
Desirable Protocol Characteristics

• Clear
• Consistent
• Complete

4
Scale Implications

• Simple
• One investigator, one patient, one encounter
• Harder
• Multiple investigators, multiple patients,
  multiple visits, multiple cultures, multiple
  languages

5
Considerations During Protocol Development

• For example
• Randomization assignment and outcome
  ascertainment
• how is potential bias minimized?
• Treatment implementation
• maximize compliance while minimizing variation
• within and between investigators and clinic staff
• patient and their support system
• over study follow-up and calendar time

6
Protocol (1)

• Should include
• 1. Literature Review (Brief)
• Describe the "state of the art" and motivate
  rationale for this clinical trial
• 2. Statement of Objectives
• What is the hypothesis that is being tested, and
  what endpoints or measurements observations
  will be made to evaluate this therapy
• e.g. BHAT
• To determine whether chronic administration of
  proprandol to pts with at least one MI will
  reduce mortality due to all causes significantly
  over a 2 yr. follow up period.
• There may be more than one objective, some
  primary and some secondary.
• 3. Sample Size
• Assumptions used, sources of data used methods
  used to make the calculations

7
Protocol (2)

• 4. Study Design
• a. Recruitment
• Entry Criteria - who are eligible
• Exclusion Criteria - among those who are
  eligible, who should not be further considered
  for various reasons
• Statement of Informed Consent - patient must
  agree to all aspects of trial, particularly to
  those things which will directly involve him
• b. Randomization Process
• Description of the mechanics of how the
  patient is to be randomized and
• when (preferably as late as possible to avoid
  problems)
• c. Baseline Evaluation
• Clinical evaluation, history, physical
• Laboratory evaluation (e.g. EKG, X-ray, etc.)
• Should describe what measurements are to be
  made
• d. Treatment Description
• Describe exactly how the two treatments are to be
  administered to the assigned patients, how often,
  dosage, etc.
• e. Follow Up Schedule Evaluation
• How often are patients to be seen, by whom
  what measurements
• are to be taken at each visit.

8
Protocol (3)

• 5. Data Monitoring
• a. Toxicity look for possible harmful effects
  what variables will
• be considered
• b. Early Stopping what mechanism, what
  endpoint will be
• watched to assess whether a large early benefit
  has been detected, what statistical procedures
• c. Quality Control statement of procedures to
  insure data
• obtained is of highest quality, usually
  involves laboratory
• results mainly

9
Protocol (4)

• 6. Analysis Plans
• State at least in rough terms what hypotheses
  will be tested and how in principle statistical
  methods will be used to answer these questions.
• C Avoids criticism of "data dredging
• C Useful in pointing out potential problems in
  the analysis
• problems some may be avoided!

10
Protocol (5)

• 7. Organizational Structure
• Useful because it is then clear to everyone who
  is in charge of what, lines of authority and what
  are the governing rules
• 8. List of Participating Centers and Principle
  Investigators
• "good public relations"
• 9. Data Monitoring Committee Membership
• 10. Publication Policy
• C who is acknowledged
• C who does the work
• C what is editorial process
• C what is study material and what belongs to
  each PI
• C time schedule of publications
• "Area most sensitive to young PI's"

11
Protocol Example OutlineDiabetes Control
ComplicationsTrial (DCCT)
12
Contents (DCCT) 1

• SUMMARY ii
• SECTION page
• 1. INTRODUCTION 1.1
• Scope and Impact of Diabetes 1.1
• Background 1.3
• Historical Perspective 1.4
• Future Directions 1.7
• 2. OBJECTIVES AND DESIGN 2.1
• Objectives 2.1
• Design 2.2
• 3. SAMPLE SIZE 3.1
• Introduction 3.1
• Basis of Sample Size Calculations 3.2

13
Contents (DCCT) 2

• 4. PATIENT SELECTION AND RECRUITMENT 4.1
• Introduction 4.1
• Eligibility Criteria 4.1
• Eligibility Criteria Applicable to Both
  Categories of Subject 4.1
• For Patients Without Retinopathy 4.2
• For Patients With Minimal Background
  Retinopathy 4.3
• Exclusion Criteria 4.4
• Exclusion Criteria Applicable to Both
  Categories of Subject 4.4
• Exclusion Criteria for Patients Without
  Retinopathy 4.10
• Additional Exclusion Criteria for Patients
  With Minimal
• Background Retinopathy 4.10
• Recruitment
• 5. INFORMED CONSENT 5.1
• General Principles 5.1
• Sequence of Procedures 5.3

14
Contents (DCCT) 3

• 6.0 PRE-RANDOMIZATION EVALUATION 6.1
• General Principles 6.1
• Laboratory 6.1
• Ophthalmologic 6.2
• Renal 6.2
• Neurologic 6.3
• Cardiovascular 6.3
• Psychological 6.4
• Compliance/Adherence 6.5
• Dietary 6.6
• Examination Results 6.6
• Quality Control 6.6
• 7.0 RANDOMIZATION 7.1
• Phase II Randomization 7.1
• Considerations for Phase III 7.3
• Ineligible Patients Who Are Randomized 7.4

15
Contents (DCCT) 4

• 8.0 METABOLIC CONTROL
• Intervention Strategy in the Standard Group 8.1
• Intervention Strategy 8.1
• Insulin 8.3
• Diet 8.4
• Exercise 8.5
• Urine Tests 8.5
• Self Blood Glucose Monitoring 8.5
• Clinic Visits 8.6
• Educational Program 8.6
• Protection of Subjects 8.6
• Intervention Strategy in the Experimental
  Group 8.7
• General Guidelines 8.7
• Diet 8.10
• Exercise 8.10
• Urine Tests 8.10
• Self Blood Glucose Monitoring 8.11
• Clinic Visits 8.11

16
Contents (DCCT) 5

• 9. FOLLOW-UP PROCEDURES FOR ENDPOINT VISITS 9.1
• General Principles 9.1
• Blood Glucose Control 9.1
• Ophthalmologic 9.2
• Renal 9.3
• Neurologic 9.3
• Cardiovascular 9.4
• Psychological 9.4
• Compliance/Adherence 9.5
• Dietary 9.6
• Examination Results 9.6
• Missed Visits 9.6
• Transfer 9.6

17
Contents (DCCT) 6

• 10. MONITORING PERFORMACE 10.1
• General Principles 10.1
• Central Biochemistry Laboratory Hemoglobin
  Alc Laboratory 10.1
• Central Ophthalmologic Reading Unit 10.2
• Other Central Units 10.3
• Local Procedures 10.3
• Clinical Centers 10.3
• Coordinating Center 10.3
• Correction of Deficiencies 10.4
• 11. MANAGEMENT OF INTERCURENT EVENTS 11.1
• General Principles 11.1
• Guidelines 11.2

18
Contents (DCCT) 7

• 12. DEVIATIONS FROM ASSIGNED TREATMENT 12.1
• Introduction 12.1
• Deviations for Experimental Treatment 12.1
• Mandatory Situations 12.1
• Allowable Situations 12.2
• Treatment Policy 12.3
• Deviations from the Standard Treatment 12.4
• Mandatory Situations 12.4
• Allowable Situations 12.4
• Treatment Policy 12.4
• Transfer to Inactive Status (both treatment
  groups) 12.5
• Procedures for Deviation or Transfer to
  Inactive Status 12.6
• 13. RESULTS AND STATISTICAL ANALYSIS 13.1
• General Principles 13.1
• Baseline Results and Analyses 13.1
• Outcome Variables 13.2
• Analysis Plan 13.3
• Interim Analyses 13.5

19
Contents (DCCT) 8

• 14. PUBLICATIONS AND PRESENTATIONS 14.1
• Introduction 14.1
• Duties of the Publications and Presentations
  Committee 14.1
• Implementation 14.3
• 15. ANCIALLARY STUDIES 15.1
• Introduction 15.1
• Definition of an Ancillary Study 15.1
• Reason for Requirement Approval 15.2
• Levels of Approval Required for Ancillary
  Studies 15.2
• Funding of Ancillary Study Results 15.3
• Publication of Ancillary Study Results 15.3
• Implementation 15.4
• 16. PROTOCOL CHANGES 16.1
• Introduction 16.1
• Policy 16.1
• Procedures 16.1

20
Contents (DCCT) 9

• 17. ADMINISTRATIVE STRUCTURE 17.1
• Introduction 17.1
• Structure 17.1
• 18. DISPOSITION OF DOCUMENTS, DATA, AND
  MATERIALS 18.1
• Documents 18.1
• Data Forms 18.1
• Tapes of Data and Analysis Files 18.2
• Laboratory Specimens 18.2
• Photographs and Other Materials 18.3
• Appendix page
• A. A.1
• B. B.1

21
Trial Organization

• Components
• sponsor
• clinical centers
• central resource units
• Administration
• Steering Committee
• Independent Data Monitoring Committee
• responsibilities
• composition and independence

22
NIH Model
Steering Committee
NIH

Policy Board Data Monitoring Committee
Central Units (Labs, )
Coordinating Center
Clinical Centers
Institutional Review Board
Patients
23
Industry-Modified NIH Model
Pharmaceutical Industry Sponsor
Steering Committee
Regulatory Agencies

Independent Data Monitoring Committee (IDMC)
Central Units (Labs, )
Data Management Center (Sponsor or Contract
Research Organization)
Statistical Analysis Center (SAC)
Clinical Centers
Institutional Review Board
Patients
24
Manual of Operations/Procedures

• Multiple may be needed
• investigators
• central resource units
• laboratory
• Events Classification Committee ...
• Purpose - standardization of procedures
• laboratory - quality of reagent, equipment
  replacement, temporal drift, ...

25
Trial Data Collection

• Data collection forms or Case Report Forms (CRFs)
• Data Completeness
• Data Integrity
• Important Note Off Treatment does not mean Off
  Study

26
Database Size (1)

• Number of subjects
• screened
• enrolled
• Length of follow-up/ number of subject visits
• Number central resource items
• Central blood measurements
• Central pathology

27
Database Size (2)

• Number of forms/patient
• Amount of coding of free text
• adverse events
• concomitant medications
• logs, journals, recalls, .
• Central adjudication
• clinical events
• cause of death
• severity of bleeding, ...

28
Database Requirements

• Integration of multiple data sources
• clinic based
• central resources
• process
• Unique identification of patient
• Audit trails

29
Data Collection

• Also See Meinert Reference!
• Data collection must cover key questions or
  aspects
• 1. Recruitment Process/Eligibility Screen
• 2. Baseline Covariates
• Who was studied? (Eligibility)
• Trt Balance? (Comparability)
• 3. Compliance
• How did design get implemented?
• 4. Toxicity
• 5. Primary and Secondary Outcomes
• 6. Ancillary
• Two points in time
• - At or before randomization
• - Sometime after randomization
• Most trials collect too much data!

30
Data CollectionRecruitment

• Over optimism
• Investigators usually overestimate number of
  patients they can recruit
• Recruitment Goals
• Need to establish recruitment goals and have
  contingency plans
• Good planning and interim monitoring
• Review Patient Admissions
• Ask investigators to show patient admissions
  which meet entry criteria, if possible
• Poor Recruitment Center
• Usual reason is not enough patients screened
• If a center can't recruit effectively, it may
  have to be dropped from further efforts BUT don't
  throw out enrolled patients

31
Data CollectionEligibility

• Modify Criteria
• Changing entry criteria doesn't usually improve
  recruitment that dramatically!
• Big Net
• Need to screen "10 to 20" patients for every one
  randomized Big net required
• Can't "catch up
• Patient exposure to treatment lost due to lagging
  recruitment

32
Baseline Variables (On Study Information)

• All baseline data should be measured prior to
  randomization and start of therapy.
• Uses
• 1. Eligibility (Based on a subset)
• 2. Group comparability
• 3. Stratified randomization
• 4. Subgroup analysis
• 5. Establish prognostic variables
• 6. Evaluate changes from baseline for outcome or
  toxicity
• 7. Comparing centers different studies
• Timing
• Should be measured as close to start of therapy
  as possible
• May not be able to ascertain some variables
• e.g. MILIS "infarct size" not possible at
  baseline
• May need 2 visits to confirm eligibility

33
Data CollectionPrimary-Secondary Outcome

• Clear definitions
• Complete Ascertainment
• Possible Adjudication

34
Data CollectionAdverse Effects

• Many possible adverse effects may be monitored.
• A Multiple Comparisons Problem
• Not always as well defined (too many perhaps)
• Anticipated Unexpected
• Natural history effects
• BHAT 66 placebo patients shortness of breath
• only 6 at baseline had history
• ? Need control group
• Ascertainment
• Eliciting vs. volunteer response
• Length of follow-up
• Frequency of patient contact

35
Data CollectionSubject Compliance

• Perfect Compliance Unusual
• 1. Patients will not absolutely adhere to
  planned protocol
• Recruit "good" compliers
• 2. Try not to enter patients who would not be
  able to comply (Hard to predict!)
• 3. Once entered, try to minimize compliance
  problems
• 4. Patients can't be dropped from analysis
  because of non-compliance
• 5. Patient adherence must be carefully monitored
• a. visits
• b. pill count, amount of therapy consumed
• c. physiologic measurements
• d. tracers
• 6. Off treatment does not mean off study!

36
Data Collection-Quality Control (1)

• No study is better than quality of its data
• Focus energy on selected key variables
• Strategies
• Proper data collection forms
• Data editing
• a. Missing data
• b. Range checks
• c. Visual inspection
• d. Consistency

37
Data Collection-Quality Control (2)

• Training/Certification
• a. Sites b. Items
• - Clinics - Protocol
• - Central labs - Data forms
• - Data center - Procedures
• - Information flow
• Manual of operations
• -Clear definitions instructions
• QC Procedures
• - Correct problems ASAP

38
Data Form Construction

• 1. Need standard forms
• 2. Safeguards in construction
• Allow time for developing testing
• Solicit content advice
• Review other RCT forms in similar trials
• Pre-test before using
• Research record ? medical record
• Link each item with stated objective
• Require adequate review before adding new items

39
Data Item Construction (1)

• 1. Every item should force a response
• 2. Terminology
• Keep it simple
• Provide key definitions on form
• If answer requires judgement or rating, provide
  basis
• Use "yes" to indicate "presence of" (No double
  negative)
• Indicate time frame

40
Data Item Construction (2)

• 3. Use of Existing Forms
• Don't reinvent the wheel if already used
  elsewhere
• Don't use an entire form just because it exists
• Get permission
• 4. Avoid open form - Use closed form
• Use response checklist
• Specify units of measurement (lbs. or kg.)
• Enough boxes to specify adequate precision __._
• Minimize calculations - obtain raw data
• 5. Use STOP SKIP instructions

41
Quality Assurance

• Timely Review
• Until recently, S.O.P. for industry was to
  collect data until trial was finished, then try
  to clean it up or do it in batches as CRAs
  visited sites
• Now QC can be an ongoing process
• QC Procedures
• 1. Visual check at clinic 6. Periodic QA reports
  v feedback
• 2. Visual check at data center 7. Submission of
  duplicate records
• 3. Double data entry 8. Comparison of clinics
• 4. Computer edit for admissible
  values 9. Re-certification of clinic personnel
• 5. Data edit queries back to clinic 10. Minimize
  lag time patient visit ? data entry
• 11. Audits

42
Data Editing

• 1. Patient Identification Record Linkage
• - Need internal check
• 2. Legibility
• 3. Form admissibility
• - Correct form, correct time window
• 4. Missing Information
• 5. Consistency
• - Consistent answers from form to form
• - Within one form/section to section
• 6. Ranges and Code Check
• - Codes legal
• - Responses within "acceptable" or "reasonable"
  range

43
Audits/Data Integrity Check

• 1. Comparison of data on computer file to data
  form (within data center)
• 2. Comparison of computer file to original
  medical record(at clinical center)
• 3. Often 10 random sample used (military audit)
• -Do not disclose which 10 ahead of time
• 4. Data center integrity

44
QC Report

• Patient visits on schedule
• Procedures completed
• Timeliness of forms
• Clinic v data center
• Data center v data file
• Edit messages (by form)
• Completeness
• Legibility
• Errors
• Split/duplicate sample
• Audits

45
Quality Assurance Activities

• Monthly Reports
• Clinical studies
• Patient accrual information
• Data Corrections
• Data monitoring and management
• Database management
• Monthly Clinical Activity Summary Reports
• Program area
• Clinical Disease Group
• Treating Physician

46
(No Transcript)
47
(No Transcript)
48
Typical Case Report Form
49
Data Collection

• Data submitted on paper case report forms

Clinical Sites
Coordinating Center
Primary Method
Alternatives
50
ONCORE A Web Based System

• Secure, fast web based system
• Manages portfolio of cancer oriented protocols
• Many functionalities
• Review approval
• Subject registration
• Data collection
• Analysis
• Started at UWCCC, now in 15 Centers
• Local software company, Percipenz
• www.oncore.org

51
Conclusion

• Data collection personnel -- no matter how
  well-trained, careful, and proficient -- should
  not be expected to resolve all errors before
  their data are transmitted to a central database
  management site
• Centrally, someone must have big picture and also
  the little details
• Not paying attention to this can be the downfall
  of any trial

52
Informed Consent Process
53
Informed Consent Process

• A required process
• Failure to comply can result in serious
  consequences
• Closure of trial
• Closure of institution

54
Informed ConsentBasic Elements

• Basic description of study
• Description of risks
• Description of benefits
• Alternative therapies
• Patient confidentiality
• Compensation for injury
• Contact Person
• Voluntary participation

55
Informed ConsentAdditional Elements

• Any risk to fetus
• Circumstances of termination of treatment
• Additional costs
• Consequences of patient withdrawal
• Patient update of new relevant results
• Number of subjects in trial