3. What’s The Question?
•
•
•
•
•
•
What’s the outcome?
What’s the intervention?
When and for how long?
For whom?
How many participants are needed?
How can we optimize potential benefit (and what
we learn) while minimizing potential harm?
4. Answering the Question
• Response variable selection and
measurement
• Defining the intervention
• Study design
• Eligibility criteria
• Sample size estimate
• Patient management procedures
• Monitoring for safety and benefit
• Data analysis approaches
8. What’s the Response Variable?
• Used to answer primary/secondary questions
• Characteristics for primary/secondary outcomes
1. Well defined & stable
2.
Ascertained in all subjects
3.
Unbiased
4.
Reproducible
5.
Specificity to question
9. Response Variable (1)
• Examples
1. MILIS
Infarct size measurement?
- Enzymes (area under curve or peaks)
- Radionuclide imaging
- EKG
Issues of definition, ascertainment, reproducible
2. NOTT
Quality of Life?
- POMS (Profile of Mood)
- SIP (Sickness Impact Profile)
- Pulmonary Function
- Survival
11. Surrogate Response Variables
• Used as alternative to desired or ideal clinical response
• Examples
– Suppression of arrhythmia (sudden death)
– T4 cell counts (AIDS or ARC)
• Used often - therapeutic exploratory
(Phase I, Phase II)
• Use with caution - therapeutic confirmatory
(Phase III)
12. Surrogate Response Variables (2)
• Frequent Criticism of Clinical Trials
– Too long
– Too large
– Too expensive
• Advantages
– Perhaps smaller sample size
– Detect earlier effect → shorter trial
– Easier
13. Examples of FDA Approval of Drugs Using
Surrogates (1)
• Lower cholesterol without evidence of survival
benefit
• Lower blood pressure without evidence of
benefit for stroke, MI, congestive heart failure,
or survival
• Increase bone density without evidence of
decreased fractures in osteoporosis
14. Examples of FDA Approval of Drugs Using
Surrogates (2)
• Increase cardiac function in congestive heart
failure without evidence of survival benefit
• Decrease rate of arrhythmias (VPBs) without
evidence of survival benefit
• Lower blood glucose and glycosylated
hemoglobin without evidence about diabetic
complications or survival benefit
15. Surrogate Response Variables (3)
• Requirements (Prentice, 1989)
T = True clinical endpoint
S = Surrogate
Z = Treatment
• H0: P(T|Z) = P(T) ⇔ P(S|Z) = P(S)
• Sufficient Conditions
1.
2.
S is informative about T (predictive)
P(T|S) ≠ P(T)
S fully captures effect of Z on T
P(T|S,Z) = P(T|S)
16. Concerns About Surrogates
1. Relationship between surrogate and true
endpoint may not be causal, but coincidental
to a third factor
2. Other unfavorable effects of the drug
3. Effect on surrogate may correlate with one
clinical endpoint, but not others
18. Time
Reasons for failure of
surrogate end points.
A. The surrogate is not
in the causal pathway
of the disease process.
B. Of several causal
pathways of disease,
the intervention
affects only the
pathway mediated
through the surrogate.
C. The surrogate is not
in the pathway of the
intervention’s effect or
is insensitive to its
effect.
D. The intervention
has mechanisms for
action independent of
the disease process.
Dotted lines =
mechanisms of action
that might exist.
A
Disease
Surrogate
End Point
True Clinical
Outcome
Intervention
B
Disease
Surrogate
End Point
True Clinical
Outcome
Intervention
C
Disease
Surrogate
End Point
True Clinical
Outcome
Intervention
D
Disease
Surrogate
End Point
True Clinical
Outcome
20. Nocturnal Oxygen Therapy Trial
(NOTT)
• Hypothesis
– Is continuous oxygen therapy better than nocturnal oxygen therapy in
chronic obstructive lung disease patients?
• Possible Surrogates
• Quality of Life
• Survival
• Design
–
–
–
–
–
203 patients
Two-sided 0.05 Type I error
Randomized
Multicenter
Sequential data monitoring
21. Possible NOTT Surrogates
PaO2
• Mean Pulmonary
Artery Pressure
Hematocrit
FEV1 % Predicted • Cardiac Index
FVC % Predicted • Pulmonary
Vascular
Maximum
Resistance
Workload
• Heart Rate
•
•
•
•
•
22. Concluding Remarks on Surrogates
• Surrogates play an important role in the development of
Phase I, II, and pilot Phase III studies
• Treatments may affect more than one mechanism
• “Surrogates” do not reliably predict treatment on clinical
outcome
• Continued success in a given field is not even guaranteed
• Reliance on “surrogates” should be minimized
23. Composite Outcomes
• Defined as having occurred if any one of several
components is observed
– e.g. death, MI, stroke, change in severity,…..
•
•
•
•
Should be clinically relevant
Each component ascertainable without bias
Must be sensitive to intervention
Made up of fatal & nonfatal events
24. Composite Endpoint Rationale
• May reduce Sample Size by increasing event
rates
– Assumes each component sensitive to
intervention
– Otherwise, power can be lost
• Avoids competing risk problem
– Death is a competing risk to all other morbid
events, probably not independent
25. Problems with
Composite Outcomes
• Interpretability if individual components go in different
directions
– e.g. WHI global index–
• Death: similar
• Fractures: positive
• DVTs, PEs: negative
• Relevance of a mixed set of components
– Adding softer outcomes
• Could have a loss of power
• Failure to ascertain components
26. Data and Safety Monitoring Boards
Why?
• Participant Safety
• Policy Review
27. Data and Safety Monitoring Boards
New Treatment for Lung Cancer
• Totally New Compound
• Possible Liver Toxicity in Animals
28. Data and Safety Monitoring Boards
New Treatment for Lung Cancer
Ten Patients Enrolled – 6 Months FU
• Placebo Group
Two Dead from Lung Cancer
• Treated Group
None Dead from Lung Cancer
Two Dead from Liver Failure
29. Data and Safety Monitoring Boards
Who?
• Independent of Sponsor/Investigators
• Appointed by Sponsor
• Members Without Conflict of Interest
• Different Areas of Expertise
30. Data and Safety Monitoring Boards
What? – Study Monitoring
• Review Protocols and Procedures
• Review Study Design
• Monitor Ongoing Quality
• Monitor Patient Accrual and Drop-out
• Monitor Clinic and Patient Compliance
31. Data and Safety Monitoring Boards
What? – Data Monitoring
• Identify Major Response Variables
• Identify Possible Adverse Outcomes
• Develop Stopping Guidelines
32. Sequential Study Monitoring
O’Brian - Flemming
5
4
3
2
1
Test
0
Statistic
-1
-2
-3
-4
-5
Reject Null Hypothesis
Continue Study
Accept
Null
Hypothesis
Continue Study
Reject Null Hypothesis
0
20
40
60
Number of Events
80
33. Data and Safety Monitoring Boards
Study Outcomes
• Planned Termination
• Harm – Early Termination
• Early Benefit – Early Termination
• Futility – Early Termination
• Study Extension with Changes
