Transcript Clinical Trials Overview - Winona State University

Clinical Trials Overview
Clinical Trials
• A clinical trial is a prospectively planned
experiment for the purpose of evaluating
one or more potentially beneficial
therapies or treatments
• In general these studies are conducted
under as many controlled conditions as
possible in order to provide definitive
answers to well-defined questions
Primary vs. Secondary Questions
• Primary
– most important, central question
– ideally, only one
– stated in advance
– basis for design and sample size
• Secondary
– related to primary
– stated in advance
– limited in number
Examples
• Physicians Health Study (PHS) started in fall 1982
– risks and benefits of aspirin and beta carotene in the
prevention of cardiovascular disease and cancer
– low-dose aspirin vs placebo
– Primary: total mortality
– Secondary: fatal + nonfatal myocardial infarction
• Eastern Cooperative Oncology Group (ECOG)
– tamoxifen vs placebo
– Primary: tumor recurrence/relapse, disease-free
survival
– Secondary: total mortality
Definitions
• Single Blind Study: A clinical trial where
the participant does not know the identity
of the treatment received
• Double Blind Study: A clinical trial in
which neither the patient nor the treating
investigators know the identity of the
treatment being administered.
Definitions
• Placebo:
– Used as a control treatment
1. An inert substance made up to physically
resemble a treatment being investigated
2. Best standard of care if “placebo” unethical
3. “Sham control”
Definitions
• Adverse event:
– An incident in which harm resulted to a
person receiving health care.
– Examples: Death, irreversible damage to
liver, nausea
– Not always easy to specify in advance
because many variables will be measured
– May be known adverse effects from earlier
trials
Adverse Events
• Challenges
– Long term follow-up versus early benefit
– Rare AEs may be seen only with very large
numbers of exposed patients and long term followup
• Example – COX II inhibitors
– Vioxx & Celebrex
– Immediate pain reduction vs longer term increase
in cardiovascular risk
Surrogate Endpoints
• Response variables used to address
questions often called endpoints
• Surrogates used as alternative to desired or
ideal clinical response to save time and/or
resources
• Examples
– Suppression of arrhythmia (sudden death)
– T4 cell counts (AIDS or ARC)
• Often used in therapeutic exploratory trials
• Use with caution in therapeutic confirmatory
trials
The General Flow of
Statistical Inference
Patient
Population
Sample
Protocol to
Obtain
Participants
Observed
Results
Inference about Population
Sample protocol / design key to analysis and inference and
may redefine the population for future experiments
Types of Clinical Trials
• Randomized
• Non-Randomized
• Single-Center
• Multi-Center
• Phase I, II, III Trials
Phase I Trial
• Objective : To determine an acceptable
range of doses and schedules for a new
drug
• Usually seeking maximum tolerated dose
(MTD)
• Participants often those that have failed
other treatments
• Important, however, that they still have
“normal” organ functions
Phase II Trial
• Objective: To determine if new drug has
any beneficial activity and thus worthy of
further testing / investment of resources.
• Doses and schedules may not be optimum
• Begin to focus on population for whom this
drug will likely show favorable effect
Phase III Trial
• Objective : To compare experimental or
new therapies with standard therapy or
competitive therapies.
• Very large, expensive studies
• Required by FDA for drug approval
• If drug approved, usually followed by
Phase IV trials to follow-up on long-range
adverse events – concern is safety
Characterization of Trials
Phase
Single Center
Multi Center
Randomized Non-Rand. Randomized Non-Rand.
I
Never
Yes
Never
Sometimes
II
Rare
Yes
Yes
Sometimes
III
Yes
Use of
Historical
Controls
Yes
Use of
Historical
Controls
Carrying out a multi-center randomized clinical trial is the most difficult
way to generate scientific information.
Why Clinical Trials?
1. Most definitive method to determine
whether a treatment is effective.
– Other designs have more potential
biases
– One cannot determine in an
uncontrolled setting whether an
intervention has made a difference in
the outcome.
Observational Studies
• Correlation vs. Causation
Examples of False Positives
1. High cholesterol diet and rectal cancer
2. Smoking and breast cancer
3. Vasectomy and prostate cancer
4. Red meat and colon cancer
5. Red meat and breast cancer
6. Drinking water frequently and bladder cancer
7. Not consuming olive oil and breast cancer
– Replication of observational studies may not
overcome confounding and bias
Why Clinical Trials?
2. Help determine incidence of side
effects and complications.
Example: Coronary Drug Project
A. Detection of side effect (Cardiac Arrhythmias)
Clofibrate 33.3%
Niacin
32.7%
p>.05
Placebo
38.2%
B. Natural occurring side effect (nausea)
Clofibrate 7.6%
Placebo
6.2%
Typical Side Effect Report - Lyrica ®
Why Clinical Trials?
3.Theory not always best path
• Intermittent positive pressure breathing
(IPPB)  reduced use, no benefit
• High [O2] in premature infants 
Retrolental Fibroplasia, Harmful
• Tonsillectomy  Reduced use
• Bypass Surgery  Restricted use
Phase I Design Strategy
• Designs based largely on tradition
• Typically do some sort of dose escalation
to reach maximum tolerated dose (MTD)
• Has been shown to be safe and
reasonably effective
• Dose escalation often based on Fibonacci
series
– 1 2 3 5 8 13 . . . .
Dose-response curve
(animal study)
Typical Scheme
1. Enter 3 patients at a given dose
2. If no toxicity, go to next dosage and repeat
step 1
3. a. If 1 patient has serious toxicity, add 3 more
patients at that dose (go to 4)
b. If 2/3 have serious toxicity, consider MTD
4. a. If 2 or more of 6 patients have toxicity,
MTD reached
b. If 1 of 6 has toxicity, increase dose and go
back to step 1
Summary of Schemes
(Storer, Biometrics 45:925-37, 1989)
A. “Standard”
– Observe group of 3 patients
– No toxicity increase dose
– Any toxicity  observe 3 or more
• One toxicity out of 6  increase dose
• Two or more toxicity  stop
B. “1 Up, 1 Down”
– Observe single patients
– No toxicity  increase dose
– Toxicity  decrease dose
Summary of Schemes
(Storer, Biometrics 45:925-37, 1989)
C. “2 Up, 1 Down”
– Observe single patients
– No toxicity in two consecutive  increase
dose
– Toxicity  decrease dose
D. “Extended Standard”
– Observe groups of 3 patients
– No toxicity  increase dose
– One toxicity  dose unchanged
– Two or three toxicity  decrease dose
Summary of Schemes
(Storer, Biometrics 45:925-37, 1989)
E. “2 Up, 2 Down”
– Observe groups of 2 patients
– No toxicity  increase dose
– One toxicity  dose unchanged
– Both toxicity  decrease dose
* B, C, D, E - fixed sample sizes ranging from 12 to
32 patients
* Can speed up process to get to target dose range
F. Bayesian sequential/adaptive designs
Phase II Designs
References:
Gehan (1961) Journal of Chronic Disorders
Fleming (1982) Biometrics
Storer (1989) Statistics in Medicine
• Goal
–
–
–
–
Screen for therapeutic activity
Further evaluate toxicity
Test using MTD from Phase I
If drug passes screen, test further
Phase II Design
• Design of Gehan
– No control (is this wise?)
– Two-stage (small initial sample, observe at least one benefit
take a second larger sample)
– Goal is to reject ineffective drugs ASAP
Decision I:
Drug is unlikely to be effective
in  x% of patients
Decision II:
Drug could be effective
in  x% of patients
Phase II Design
• Example: Gehan Design
– Let x% = 20% : want to check if drug likely to
work in at least 20% of patients
1. Enter 14 patients
2. If 0/14 responds, stop and
declare true drug response 20%
3. If 1+/14 respond, add 15-40
more patients
4. Estimate response rate & C.I.
Gehan Design
• Why 14 patients initially?
Patient
1
2
3
--8
--14
Prob
0.8
0.64 (0.8 x 0.8)
0.512 (0.8 x 0.8 x 0.8)
--0.16
--0.044
• If drug  20% effective, there would be
~95.6% chance of at least one success
• If 0/14 success observed, reject drug
Phase II Design
• Stage I Sample Size - Gehan
Table I
Rejection
Error
5
5%
59
10%
45
Effectiveness (%)
10 15 20 25 40
29 19 14 11 6
22 15 11
9 5
50
5
4
Stage II Sample Size
• Based on desired precision of effectiveness estimate
r1 = # of successes in Stage 1
n1= # of patients in Stage 1
With pˆ1  r1 / n1 , SE(p̂1 ) 
p̂1 (1  p̂1 )
n1
• Now precision of total sample N=(n1 + n2)
ˆ*  p
ˆ1 then SE (pˆ * ) 
If p
pˆ * (1  pˆ * )
N
Stage II Sample Size
To be conservative, Gehan suggested
pˆ  pˆ1  1.15SE ( pˆ1 )
*
The upper 75% confidence limit from first sample
• Thus, we can generate a table for size of
second stage (n2) based on desired precision
Additional Patients for Stage II
(n2, a1=.05)
Required
Precision
(SE)
Number of
Successes
Stage I
Therapeutic Effectiveness (%)
5
10
15
20
25
30
59
29
19
14
11
9
1
0
4
30
45
60
70
2
0
17
45
63
78
87
3
0
28
58
76
87
91
4
0
38
67
83
89
91
5
0
46
75
86
89
91
1
0
0
0
1
7
11
2
0
0
0
6
12
15
3
0
0
1
9
14
16
4
0
0
3
11
14
16
5
0
0
5
11
14
16
n1
r1
+1 SE
5%
+1 SE
10%
Phase II Trial Designs
• Many cancer Phase II trials follow Gehan design
• Many other diseases could – there seems to be
no standard non-cancer Phase II design
• Might also randomize patients into multiple arms
each with a different dose – can then get a dose
response curve
• Other two-stage designs based on determining
p1-p0 > x% where p0 is the standard care
combination
Phase III Trial Designs
o The foundation for the design of controlled
experiments established for agricultural experiments
o The need for control groups in clinical studies
recognized, but not widely accepted until 1950s
o No comparison groups needed when results dramatic:
o Penicillin for pneumococcal pneumonia
o Rabies vaccine
o Use of proper control group necessary due to:
o Natural history of most diseases
o Variability of a patient's response to intervention
Phase III Design
• Comparative Studies
• Experimental Group vs. Control Group
• Establishing a Control
1.
2.
3.
Historical
Concurrent
Randomized
• Randomized Control Trial (RCT) is the gold
standard
– Eliminates several sources of bias
Purpose of Control Group
• To allow discrimination of patient
outcomes caused by test treatment from
those caused by other factors
– Natural progression of disease
– Observer/patient expectations
– Other treatment
• Fair comparisons
– Necessary to be informative
Goals of Phase III
Clinical Trial
• Superiority Trials
– A controlled trial may demonstrate efficacy
of the test treatment by showing that it is
superior to the control
• No treatment (placebo)
• Best standard of current care
Goals of Phase III
Clinical Trials
• Non-Inferiority Trials
– Controlled trial may demonstrate efficacy by
showing the test treatment is similar in efficacy to a
known effective treatment
• The active control has to be effective under the
conditions of the trials
• New treatment cannot be worse by a pre-specified
amount
• New treatment may not be better than the standard
but may have other advantages
– Cost
– Toxicity and/or side effects
– Invasiveness
Significance of Control Group
•
•
•
•
•
•
•
•
Inference drawn from the trial
Ethical acceptability of the trial
Degree to which bias is minimized
Type of subjects
Kind of endpoints that can be studied
Credibility of the results
Acceptability of the results by regulatory authorities
Other features of the trial, its conduct, and
interpretation
Use of Placebo Control
• The “placebo effect” is well documented
(as high as 33% according to some studies)
• Could be
– No treatment + placebo
– Standard care + placebo
• Matched placebos are necessary so patients and
investigators cannot decode the treatment
assignment
• E.g. Vitamin C trial for common cold
• Placebo was used, but was distinguishable
– Many on placebo dropped out of study – not
blinded
– Those who knew they were on vitamin C
reported fewer cold symptoms and duration than
those on vitamin who didn't know
Unbiased Evaluation
Subject Bias (NIH Cold Study)
(Karlowski, 1975)
Duration of Cold (Days)
Blinded
Unblinded
Subjects
Subjects
Placebo
6.3
8.6
Ascorbic Acid
6.5
4.8
Historical Control Study
• A new treatment used in a series of subjects
• Outcome compared with previous series of
comparable subjects
• Non-randomized
• Rapid, inexpensive, good for initial testing of new
•
treatments
• Vulnerable to biases
Different underlying populations
Criteria for selecting patients
Patient care
Diagnostic or evaluating criteria
Historical Control Study
When might we consider a historical control
study?
• When preliminary data strongly suggest efficacy.
• When course of disease predictable, generally a
consistently poor outcome.
• When endpoints objective, like death or
metastisization.
• When impact of baseline and other variables on
endpoint is well characterized.
Randomized Control
Clinical Trial
• Reference: Byar et al. (1976)
New England Journal of Medicine
• Patients assigned at random to either
treatment(s) or control
• Considered to be “Gold Standard”
Disadvantages of Randomized
Control Clinical Trial
1. Generalizable Results?
– Subjects may not represent general
patient population – volunteer effect
2. Recruitment
– Twice as many new patients
3. Acceptability of Randomization Process
– Some physicians will refuse
– Some patients will refuse
4. Administrative Complexity
Ethics of Randomization
• Statistician/clinical trialist must sell benefits of randomization
• Ethics MD should do what he thinks is best for his patient
– Two MD's might ethically treat same patient quite differently
• Chalmers & Shaw (1970) Annals New York Academy of
Science
1.
2.
If MD "knows" best treatment, should not participate in trial
If in doubt, randomization gives each patient equal chance to
receive one of therapies (i.e. best)
3. More ethical way of practicing medicine
• Bayesian Adaptive designs  More likely assign “better” treatment
Comparing Treatments
• Fundamental principle
• Groups must be alike in all important aspects and only differ in the
treatment each group receives
• In practical terms, “comparable treatment groups” means
“alike on the average”
• Randomization
• Each patient has the same chance of receiving any of the
treatments under study
• Allocation of treatments to participants is carried out using a
chance mechanism so that neither the patient nor the physician
know in advance which therapy will be assigned
• Blinding
• Avoidance of psychological influence
• Fair evaluation of outcomes
Randomized Phase III
Experimental Designs
Assume:
• Patients enrolled in trial have satisfied eligibility
criteria and have given consent
• Balanced randomization: each treatment group will
be assigned an equal number of patients
Issue
• Different experimental designs can be used to
answer different therapeutic questions
Commonly Used Phase III
Designs
•
•
•
•
•
•
•
•
•
Parallel
Withdrawal
Group/Cluster
Randomized Consent
Cross Over
Factorial
Large Simple
Equivalence/Non-inferiority
Sequential
Parallel Design
Screen
Trt A
Randomize -
Trt B
• H0: A vs. B
• Advantage
– Simple, General Use
– Valid Comparison
• Disadvantage
– Few Questions/Study
Fundamental Design
Eligible
Yes
Consent
No
No
Dropped
Dropped
Yes
R
A
N
D
O
M
I
Z
E
Comment: Compare A with B
A
B
Run-In Design
Problem:
• Non-compliance by patient may seriously impair
efficiency and possibly distort conclusions.
Possible Solution: Drug Trials
• Assign all eligible patients a placebo to be taken
for a “brief” period of time. Patients who are
“judged” compliant are enrolled into the study.
This is often referred to as the “Placebo Run-In”
period.
• Can also use active drug to test for compliance.
Run-In Design
Screen &
Consent
R
A
Run-In
Satisfactory N
Period
D
O
M
I
Unsatisfactory
Z
E
Dropped
Note: It is assumed that all patient entering the run-in
period are eligible and have given consent
A
B
Withdrawal Study
Treatment A
Treament A  randomize
Not Treatment A
(placebo)
• Advantage
–Easy Access to subjects
–Show if continued treatment is beneficial
• Disadvantage
–Selected Population
–Different Disease Stage
Cluster Randomization Designs
• Groups (clinics, communities) are randomized to treatment or control
• Examples:
• Community trials on fluoridization of water
• Breast self-examination programs in different clinic settings in USSR
• Smoking cessation intervention trial in different school districts
in the state of Washington
• Advantages
• Sometimes logistically more feasible
• Avoid contamination
• Allow mass intervention, thus “public health trial”
• Disadvantages
• Effective sample size less than number of subjects
• Many units must participate to overcome unit-to-unit variation,
thus requires larger sample size
• Need cluster sampling methods
Cross Over Design
H0: A vs. B
Scheme
Period
AB
BA
Group
I
II
1
2
TRT A
TRT B
TRT B
TRT A
• Advantage
– Each patient their own control
– Smaller sample size
• Disadvantage
– Not useful for acute disease
– Disease must be stable
– Assumes no period carry over
– If carryover, have a study half sized
(Period I A vs. Period I B)
Superiority vs.
Non-Inferiority Trials
Superiority Design: Show that new treatment is
better than the control or standard (maybe a
placebo)
Non-inferiority: Show that the new treatment
a) Is not worse that the standard by more than some
margin
b) Would have beaten placebo if a placebo arm had
been included (regulatory)
Equivalence/Non-inferiority Trial
• Trial with active (positive) controls.
• The question is whether new (easier or cheaper)
treatment is as good as the current treatment.
• Must specify margin of “equivalence” or non-inferiority
• Can't statistically prove equivalency -- only show that
difference is less than something with specified
probability.
• Historical evidence of sensitivity to treatment
• Sample size issues are crucial.
• Small sample size, leading to low power and
subsequently lack of significant difference, does not
imply “equivalence”.
Non-Inferiority Challenges
• Requires high quality trial
• Poor execution favors non-inferiority
• Treatment margin somewhat arbitrary
Sequential Design
• Continue to randomize subjects until H0 is
either rejected or “accepted”
• A large statistical literature for classical
sequential designs
• Developed for industrial setting
• Modified for clinical trials
(e.g. Armitage 1975, Sequential Medical Trials)
Classical Sequential Design
• Continue to randomize subjects until H0 is either rejected or “accepted”
• Classic
Trt Better
Net
Treatment
Effect
Continue
20

0
Accept H0
-20
Continue
Trt Worse
100
200
300
No. of Paired Observations