Protocol Procedures

8/8/2019 Protocol Procedures

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Introduction to Clinical Trials

Protocols,Manual of Procedures &Data Collection

PHARMASRI www.pharmasri.com


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Protocol vs Manual of Operations Analogy


y Protocol is general blueprint fory investigators + institutional review boardsy sponsory regulatory agencies

y Manual of Procedures is detailed construction documenty clinic staff y data management staff


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Desirable Protocol Characteristics


y C lear

y C onsistent

y C omplete


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S cale Implications


y Simpley One investigator, one patient, one encounter

y

Hardery Multiple investigators, multiple patients, multiple visits,

multiple cultures, multiple languages


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Considerations During ProtocolDevelopment


For exampley R andomization assignment and outcome ascertainment

y how is potential bias minimized?

y Treatment implementationy maximize compliance while minimizing variation

y within and between investigators and clinic staff y patient and their support systemy over study follow-up and calendar time


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Protocol (1)


Should include:

1. Literature R eview (Brief)

Describe the "state of the art" and motivate rationale for this clinical trial

2. Statement of Objectivesy

What is the hypothesis that is being tested,y What endpoints or measurements & observations will be made to evaluate this therapyy e.g. BHATTo determine whether chronic administration of proprandol to pts with at least one MI will reducemortality due to all causes significantly over a 2 yr. follow up period.

y There may be more than one objective,y some primaryy some secondaryy subgroups.

3. Sample SizeAssumptions used, sources of data used & methods used to make the calculations


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Protocol (2)


4 . Study Design

a.R

ecruitmenty Entry C riteria - who are eligibley ExclusionC riteria - among those who are eligible, who should not be further considered for

various reasonsy Statement of InformedC onsent - patient must agree to all aspects of trial, particularly to those

things which will directly involve him b. R andomization Process

Description of the mechanics of how the patient is to be randomized andwhen (preferably as late as possible to avoid problems)

c. Baseline Evaluationy C linical evaluation, history, & physicaly Laboratory evaluation (e.g. EKG, X-ray, etc.)

Should describe what measurements are to be maded. Treatment Description

y Describe exactly how the two treatments are to be administered to the assigned patients, howoften, dosage, etc.

e. Follow Up Schedule & EvaluationHow often are patients to be seen, by whom & what measurementsare to be taken at each visit.


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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 statisticalprocedures

c. Quality C ontrol: statement of procedures to insure data

obtained is of highest quality, usually involves laboratory

results mainly


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Protocol (4)


6 . Analysis Plans

State at least what hypotheses will be tested and how in principle what statisticalmethods will be used to answer these questions.

Primary question

Secondary questions

Subgroup questions

Avoids criticism of "data dredging

Useful in pointing out potential problems in the analysis

A more detailed analysis plan can be included in an appendix or in a separate document StatisticalAnalysis Plan (SAP)


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Protocol (5)


7 . Organizational Structure

Useful because it is then clear to everyone who is in charge of what, lines of authority andwhat are the governing rules

8. List of ParticipatingC enters and Principle Investigators

"good public relations"

9. Data Monitoring &C ommittee Membership

10. Publication Policywho is acknowledged

who does the work

what is editorial processwhat is study material and what belongs to each PI

time schedule of publications

"Area most sensitive to young PI's"


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Protocol Example OutlineDiabetes Control & Complications

Trial (DCCT)


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Contents (DCCT) [1]


SUMMARY ii

SEC TION page1. INTR ODUC TION 1.1

Scope and Impact of Diabetes 1.1Background 1.3Historical Perspective 1.4F uture Directions 1.7

2. OBJEC TIVES AND DESIGN 2.1Objectives 2.1

Design 2.2

3. SAMPLE SIZE 3.1Introduction 3.1Basis of Sample SizeC alculations 3.2


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Contents (DCCT) [2]


4. PATIENT SELE

CTION AND

RE

CRUITMENT

4.1

Introduction 4 .1EligibilityC riteria 4 .1

EligibilityC riteria Applicable to BothC ategories of Subject 4 .1For Patients Without R etinopathy 4 .2For Patients With Minimal BackgroundR etinopathy 4 .3

ExclusionC riteria 4 .4

ExclusionC riteria Applicable to BothC ategories of Subject 4 .4

ExclusionC riteria for Patients WithoutR etinopathy 4 .10Additional ExclusionC riteria for Patients With MinimalBackgroundR etinopathy 4 .10

R ecruitment

5 . INF OR MEDC ONSENT 5 .1General Principles 5 .1Sequence of Procedures 5 .3


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Contents (DCCT) [3]


6 .0 PR E-R ANDOMIZATION EVALUATION 6 .1General Principles 6 .1Laboratory 6 .1Ophthalmologic 6 .2R enal 6 .2Neurologic 6 .3C ardiovascular 6 .3Psychological 6 .4C ompliance/Adherence 6 .5

Dietary 6 .6

ExaminationR esults 6 .6

Quality C ontrol 6 .6

7 .0 R ANDOMIZATION 7 .1Phase IIR andomization 7 .1C onsiderations for Phase III 7 .3Ineligible Patients Who AreR andomized 7 .4


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Contents (DCCT) [4]


8.0 METABOLIC C ONTR OLIntervention Strategy in the Standard Group 8.1

Intervention Strategy 8.1Insulin 8.3Diet 8.4

Exercise 8.5

Urine Tests 8.5

Self Blood Glucose Monitoring 8.5C linic 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 8.10Exercise 8.10Urine Tests 8.10Self Blood Glucose Monitoring 8.11C linic Visits 8.11

General Procedures to Maximize Adherence to Protocol 8.12


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Contents (DCCT) [5]


9. F OLLOW-UP PR OC EDUR ESF OR ENDPOINT VISITS 9.1General Principles 9.1Blood GlucoseC ontrol 9.1Ophthalmologic 9.2R enal 9.3Neurologic 9.3C ardiovascular 9.4

Psychological 9.4C ompliance/Adherence 9.5

Dietary 9.6ExaminationR esults 9.6

Missed Visits 9.6

Transfer 9.6


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Contents (DCCT) [6]


10. MONITOR ING PERF OR MAC E 10.1

General Principles 10.1C entral Biochemistry Laboratory & Hemoglobin Alc Laboratory 10.1C entral OphthalmologicR eading Unit 10.2

Other C entral Units 10.3

Local Procedures 10.3C linicalC enters 10.3C oordinatingC enter 10.3C orrection of Deficiencies 10.4

11. MANAGEMENT OF INTERC UR ENT EVENTS 11.1General Principles 11.1

Guidelines 11.2


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Contents (DCCT) [7]


12. DEVIATIONSFR OM ASSIGNED TR EATMENT 12.1

Introduction 12.1Deviations for Experimental Treatment 12.1

Mandatory Situations 12.1Allowable Situations 12.2Treatment 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. R ESULTS AND STATISTIC AL ANALYSIS 13.1General Principles 13.1BaselineR esults and Analyses 13.1Outcome Variables 13.2Analysis Plan 13.3Interim Analyses 13.5


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Contents (DCCT) [8]


14 . PUBLIC ATIONS AND PR ESENTATIONS 14 .1Introduction 14 .1Duties of the Publications and PresentationsC ommittee 14 .1Implementation 14 .3

15 . ANC IALLARY STUDIES 15 .1

Introduction 15

.1Definition of an Ancillary Study 15 .1R eason forR equirement Approval 15 .2

Levels of ApprovalR equired for Ancillary Studies 15 .2F unding of Ancillary StudyR esults 15 .3Publication of Ancillary StudyR esults 15 .3

Implementation 15

.4

16 . PR OTOC OL C HANGES 16 .1Introduction 16 .1Policy 16 .1Procedures 16 .1


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Contents (DCCT) [9]


17

. ADMINISTR

ATIVE STR

UC

TUR

E 17

.1Introduction 17 .1Structure 17 .1

18. DISPOSITION OF DOC UMENTS, DATA, AND MATER IALS 18.1Documents 18.1

DataF

orms 18.1Tapes of Data and AnalysisF iles 18.2Laboratory Specimens 18.2Photographs and Other Materials 18.3

Appendix page

A. A.1

B. B.1


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Trial Organization


y C omponentsy sponsory clinical centersy central resource units

y Administrationy SteeringC ommitteey Independent Data MonitoringC ommittee

y responsibilitiesy composition and independence


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Central Units(Labs, )

Clinical Centers

NIH

InstitutionalReview Board

Policy Board

Data MonitoringCommittee

SteeringCommittee

Coordinating Center

NIH Model

Patients


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Central Units(Labs, )

Clinical Centers

Patients

Data ManagementCenter (Sponsor or Contract Research

Organization)

PharmaceuticalIndustry Sponsor

InstitutionalReview Board

IndependentData Monitoring

Committee (IDMC)

SteeringCommittee

Statistical AnalysisCenter (SAC)

Regulatory Agencies

Industry-Modified NIH Model


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Trial Data Collection


y Data collection forms orC ase R eport Forms (CRF s)y Data C ompletenessy Data Integrity

Important Note: Off Treatment does not mean Off Study


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Database S ize (1)


y Number of subjectsy screenedy enrolled

y Length of follow-up/ number of subject visits

y Number central resource itemsy C entral blood measurementsy C entral pathology


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Database S ize (2)


y Number of forms/patient

y Amount of coding of free texty adverse eventsy concomitant medicationsy logs, journals, recalls, .

y C entral adjudicationy

clinical eventsy cause of deathy severity of bleeding, ...


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Data Collection


Also See MeinertR eference!

y Data collection must cover key questions or aspects1. R ecruitment Process/Eligibility Screen2. BaselineC ovariates

Who was studied? (Eligibility) Trt Balance? (C omparability)

3. C ompliance How did design get implemented?

4 . Toxicity5 . Primary and Secondary Outcomes6 . Ancillary

y Two points in time- At or before randomization- Sometime after randomization

y Most trials collect too much data!


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Data CollectionRecruitment


y Over optimismy Investigators usually overestimate number of patients they can recruit

y R ecruitment Goalsy Need to establish recruitment goals and have contingency plansy Good planning and interim monitoring

y R eview Patient Admissionsy Ask investigators to show patient admissions which meet entry criteria, if possible

y Poor R ecruitment C entery Usual reason is not enough patients screenedy If a center can't recruit effectively, it may have to be dropped from further efforts BUT

don't throw out enrolled patients


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Data CollectionEligibility


y ModifyC riteriay C hanging entry criteria doesn't usually improve recruitment that

dramatically!

y Big Nety Need to screen "10 to 20" patients for every one randomized | Big

net required

y C an't "catch upy Patient exposure to treatment lost due to lagging recruitment


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B aseline Variables(On S tudy Information)


y

All baseline data should be measured prior to randomization and start of therapy.y Uses

1. Eligibility (Based on a subset)2. Group comparability3. Stratified randomization4. Subgroup analysis5 . Establish prognostic variables

6 . Evaluate changes from baseline for outcome or toxicity7 . C omparing centers & different studies

y Timing

Should be measured as close to start of therapy as possibleMay not be able to ascertain some variablese.g. MILIS "infarct size" not possible at baseline

y May need 2 visits to confirm eligibility


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Data CollectionPrimary- S econdary Outcome


y C lear definitions

y C omplete Ascertainment

y Possible Adjudication


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Data CollectionAdverse Effects


y Many possible adverse effects may be monitored.A MultipleC omparisons Problem

y Not always as well defined (too many perhaps)y Anticipated + Unexpectedy Natural history effects

BHAT 66% placebo patients shortness of breathonly 6% at baseline had history

Need control group

y Ascertainmenty Eliciting vs. volunteer responsey Length of follow-upy F requency of patient contact


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Data Collection-Quality Control (1)


No study is better than quality of its dataFocus energy on selected key variables

Strategiesy Proper data collection formsy Data editing

a. Missing data

b.R

ange checksc. Visual inspectiond. C onsistency


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a a o ec on-Quality Control (2)


y Training/ C ertification

a. Sites b. Items- C linics - Protocol

- C entral labs - Data forms- Data center - Procedures

- Information flowy Manual of operations

-C lear definitions & instructionsy QC Procedures

- C orrect problems ASAP


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Data Form Construction


1. Need standard forms, either paper or electronic

2. Safeguards in construction

y Allow time for developing & testingy Solicit content advicey R eview other RC T forms in similar trialsy Pre-test before usingy R esearch record{ medical recordy Link each item with stated objectivey R equire adequate review before adding new items


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Data Item Construction


1. Every item should force a responseMinimize free text

2. Terminology

Keep it simpleProvide key definitions on formIf answer requires judgement or rating, provide basis

Use "yes" to indicate "presence of" (No double negative)Indicate time frame


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Data Item Construction (2)


3. Use of ExistingFormsDon't reinvent the wheel if already used elsewhereDon't use an entire form just because it existsGet permission

4 . Avoid open form - Use closed formUse response checklistSpecify units of measurement (lbs. or kg.)Enough boxes to specify adequate precision __._

Minimize calculations - obtain raw data

5 . Use STOP & SKIP instructions


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Quality Assurance


y Timely R eviewy 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 asCR As visited sitesy Now QC can be an ongoing process

y 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. C omparison of clinics

4 . C omputer edit for admissible values 9. R e-certification of clinic personnel

5 . Data edit queries back to clinic 10. Minimize lag time patient visitp data entry

11. Audits


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Data Editing


1. Patient Identification &R ecord Linkage- Need internal check

2. Legibility

3. Form admissibility- C orrect form, correct time window

4 . Missing Information

5 . C onsistency- C onsistent answers from form to form-Within one form/section to section

6 . R anges andC ode C heck- C odes legal- R esponses within "acceptable" or "reasonable" range


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Audits/Data Integrity Check


1. C omparison of data on computer file to data form (within data center)Electronic data capture largely eliminates this problem

2.C

omparison of computer file to original medical record(at clinicalcenter)

3. Often 10% random sample used (military audit)-Do not disclose which 10% ahead of time

4 . Data center integrity


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Typical Case Report Form



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Data Collection


Web

E-Ma il

Da taFax

a ta lle ctiand Managemen t

O r ac lt dy e cific

a taba s e

US Ma il

Fax

Data submitted on case report forms

C linical Sites C oordinating C enter

Alternatives

Primary Method


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ONCORE: A Web B ased S ystem


y Secure, fast web based systemy Manages portfolio of cancer oriented protocolsy Many functionalities

y R eview & approvaly Subject registrationy Data collectiony Analysis

y

Started at UWCCC

, now in 15

C

entersy Local software company, Percipenzy www.oncore.org


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Conclusion


y Data collection personnel -- no matter how well-trained, careful,and proficient -- should not be expected to resolveall errors before their data are transmitted to a central database managementsite

y C entrally, someone must have big picture and also the little detailsy Not paying attention to this can be the downfall of any trial

Protocol Procedures

Documents

Transcript of Protocol Procedures