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Clinical Trials Overview

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Title: Clinical Trials Overview


1
Clinical Trials Overview
2
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

3
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

4
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

5
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.

6
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

7
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

8
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 follow-up
  • Example COX II inhibitors
  • Vioxx Celebrex
  • Immediate pain reduction vs longer term increase
    in cardiovascular risk

9
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

10
The General Flow of Statistical Inference
Sample Protocol to Obtain Participants
Patient Population
Observed Results
Inference about Population
Sample protocol / design key to analysis and
inference and may redefine the population for
future experiments
11
Types of Clinical Trials
  • Randomized
  • Non-Randomized
  • Single-Center
  • Multi-Center
  • Phase I, II, III Trials

12
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

13
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

14
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

15
(No Transcript)
16
Characterization of Trials
Phase Single Center Single Center Multi 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.
17
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.

18
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

19
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 pgt.05
  • Placebo 38.2
  • B. Natural occurring side effect (nausea)
  • Clofibrate 7.6
  • Placebo 6.2

20
Typical Side Effect Report - Lyrica
21
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

22
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 . . . .

23
Dose-response curve (animal study)
 
24
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

25
Summary of Schemes (Storer, Biometrics
45925-37, 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

26
Summary of Schemes(Storer, Biometrics 45925-37,
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

27
Summary of Schemes (Storer, Biometrics
45925-37, 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

28
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

29
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

30
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.

31
Gehan Design
  • Why 14 patients initially?
  • If drug ? 20 effective, there would be 95.6
    chance of at least one success
  • If 0/14 success observed, reject drug

Patient Prob 1 0.8 2 0.64 (0.8 x
0.8) 3 0.512 (0.8 x 0.8 x 0.8) --- --- 8 0.1
6 --- --- 14 0.044
32
Phase II Design
  • Stage I Sample Size - Gehan
  • Table I
  • Rejection Effectiveness ()
  • Error 5 10 15 20 25 40 50
  • 5 59 29 19 14 11 6 5
  • 10 45 22 15 11 9 5 4

33
Stage II Sample Size
  • Based on desired precision of effectiveness
    estimate
  • r1 of successes in Stage 1
  • n1 of patients in Stage 1
  • Now precision of total sample N(n1 n2)

34
Stage II Sample Size
  • To be conservative, Gehan suggested
  • The upper 75 confidence limit from first sample
  • Thus, we can generate a table for size of
  • second stage (n2) based on desired precision

35
Additional Patients for Stage II(n2, a1.05)
36
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 gt x where p0 is the standard care
    combination

37
Phase III Trial Designs
  • The foundation for the design of controlled
    experiments established for agricultural
    experiments
  • The need for control groups in clinical studies
    recognized, but not widely accepted until 1950s
  • No comparison groups needed when results
    dramatic
  • Penicillin for pneumococcal pneumonia
  • Rabies vaccine
  • Use of proper control group necessary due to
  • Natural history of most diseases
  • Variability of a patient's response to
    intervention

38
Phase III Design
  • Comparative Studies
  • Experimental Group vs. Control Group
  • Establishing a Control
  • 1. Historical
  • 2. Concurrent
  • 3. Randomized
  • Randomized Control Trial (RCT) is the gold
    standard
  • Eliminates several sources of bias

39
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

40
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

41
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

42
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

43
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

44
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

45
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

46
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.

47
Randomized ControlClinical 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

48
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

49
Ethics of Randomization
  • Statistician/clinical trialist must sell benefits
    of randomization
  • Ethics Þ 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. If MD "knows" best treatment, should not
    participate in trial
  • 2. 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

50
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
    meansalike on the average
  • Randomization
  • Each patient has the same chance of receiving any
    of thetreatments 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

51
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

52
Commonly Used Phase III Designs
  • Parallel
  • Withdrawal
  • Group/Cluster
  • Randomized Consent
  • Cross Over
  • Factorial
  • Large Simple
  • Equivalence/Non-inferiority
  • Sequential

53
Parallel Design
  • Screen
  • Trt A
  • Randomize -
  • Trt B
  • H0 A vs. B
  • Advantage
  • Simple, General Use
  • Valid Comparison
  • Disadvantage
  • Few Questions/Study

54
Fundamental Design
R A N D O M I Z E
Yes
Yes
A
Eligible
Consent
No
B
No
Dropped
Dropped
Comment Compare A with B
55
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.

56
Run-In Design
R A N D O M I Z E
Screen Consent
Run-In Period
Satisfactory
A
B
Unsatisfactory
Dropped
Note It is assumed that all patient entering the
run-in period are eligible and have given consent
57
Withdrawal Study
  • Treatment A
  • Treament A ?
  • Not Treatment A
    (placebo)
  • Advantage
  • Easy Access to subjects
  • Show if continued treatment is beneficial
  • Disadvantage
  • Selected Population
  • Different Disease Stage

randomize
58
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 districtsin 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

59
Cross Over DesignH0 A vs. B
  • Scheme
  • Period
  • Group I II
  • AB 1 TRT A TRT B
  • BA 2 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)

60
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
  • Is not worse that the standard by more than some
    margin
  • Would have beaten placebo if a placebo arm had
    been included (regulatory)

61
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.

62
Non-Inferiority Challenges
  • Requires high quality trial
  • Poor execution favors non-inferiority
  • Treatment margin somewhat arbitrary

63
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)

64
Classical Sequential Design
  • Continue to randomize subjects until H0 is either
    rejected or accepted
  • Classic

Trt Better
Continue
Net Treatment Effect
20
Accept H0
?
0
Continue
-20
Trt Worse
100
200
300
No. of Paired Observations
65
Sample Size Considerations
66
Comparing Time to Event Distributions
  • Primary endpoint is the time to an event
  • Compare the survival distributions
  • Measure of treatment effect is the ratio of the
    hazard rates
  • Must also consider the length of follow-up

67
Exponential Survival Distributions
  • Surivival function P(T gt t) e-lt
  • George Desu (1974)
  • Assumes all patients followed to an event (no
    censoring)
  • Assumes all patients immediately entered

68
Converting Number of Events (D) to Required
Sample Size (2N)
  • d 2N x P(event) 2N d/P(event)
  • P(event) is a function of the length of total
    follow-up at time of analysis and the average
    hazard rate
  • Let AR accrual rate (patients per year)
  • A period of uniform accrual (2N AR x A)
  • F period of follow-up after accrual complete
  • A/2 F average total follow-up at planned
    analysis
  • average hazard rate
  • Then P(event) 1 P(no event)

69
Time to Failure
  • In many clinical trials
  • 1. Not all patients are followed to an event
  • (i.e. censoring)
  • 2. Patients are recruited over some period of
    time
  • (i.e. staggered entry)
  • More General Model (Lachin, 1981)
  • where .

70
  • 1. Instant Recruitment Study Censored At Time T
  • 2. Continuous Recruiting (O,T) Censored at T
  • 3. Recruitment (O, T0) Study Censored at T (T
    gt T0)

71
  • Example
  • Assume ? .05 (2-sided) 1 - ? .90
  • ?C .3 and ?I .2
  • T 5 years follow-up
  • T0 3
  • 0. No Censoring, Instant Recruiting
  • N 128
  • 1. Censoring at T, Instant Recruiting
  • N 188
  • 2. Censoring at T, Continual Recruitment
  • N 310
  • 3. Censoring at T, Recruitment to T0
  • N 233

72
Sample Size Adjustment for Non-Compliance
  • References
  • 1. Shork Remington (1967) Journal of Chronic
    Disease
  • 2. Halperin et al (1968) Journal of Chronic
    Disease
  • 3. Wu, Fisher DeMets (1988) Controlled
    Clinical Trials
  • Problem
  • Some patients may not adhere to treatment
    protocol
  • Impact
  • Dilute whatever true treatment effect exists

73
Sample Size Adjustment for Non-Compliance
  • Fundamental Principle
  • Analyze All Subjects Randomized
  • Called Intent-to-Treat (ITT) Principle
  • Noncompliance will dilute treatment effect
  • A Solution
  • Adjust sample size to compensate for dilution
    effect (reduced power)
  • Definitions of Noncompliance
  • Dropout Patient in treatment group stops taking
    therapy
  • Dropin Patient in control group starts taking
    experimental therapy

74
  • Comparing Two Proportions
  • Assumes event rates will be altered by
    non-compliance
  • Define
  • PT adjusted treatment group rate
  • PC adjusted control group rate
  • If PT lt PC,

1.0
0
PC
PT
PC
PT
75
Adjusted Sample Size
  • Simple Model -
  • Compute unadjusted N
  • Assume no dropins
  • Assume dropout proportion R
  • Thus PC PC
  • PT (1-R) PT R PC
  • Then adjust N
  • Example
  • R 1/(1-R)2 Increase
  • .1 1.23 23
  • .25 1.78 78

76
Sample Size Adjustment for Non-Compliance
  • Dropouts dropins (R0, RI)
  • Example
  • R0 R1 1/(1- R0- R1)2 Increase
  • .1 .1 1.56 56
  • .25 .25 4.0 4 times

77
Sample Size Adjustments
  • More Complex Model
  • Ref Wu, Fisher, DeMets (1980)
  • Further Assumptions
  • Length of follow-up divided into intervals
  • Hazard rate may vary
  • Dropout rate may vary
  • Dropin rate may vary
  • Lag in time for treatment to be fully effective

78
Sample Size Summary
  • Ethically, the size of the study must be large
    enough to achieve the stated goals with
    reasonable probability (power)
  • Sample size estimates are only approximate due to
    uncertainty in assumptions
  • Need to be conservative but realistic
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