The document discusses key aspects of phase III clinical trials (RCTs) including: 1) Phase III trials require large sample sizes to further evaluate safety and effectiveness. 2) Study designs consider controls like placebo, active controls, and non-inferiority comparisons. Assay sensitivity and dealing with multiple analyses are also important. 3) Interim analyses allow evaluating safety and stopping early for efficacy but can increase chances of false positives. Intent-to-treat and final analyses include all randomized patients.

1. Integrated Aspects of Phase III
RCTs
Presented at the NIPER Symposium on Clinical Research and Training,
February 21-22, 2009
Dr. Bhaswat S. Chakraborty
Senior VP, Cadila Pharmaceuticals Ltd.

2. Contents
• Clinical Trials
– Regulatory phases
• Phase 3 Trials
• Data Requirements
• Study Design Considerations
• Controls
– Placebo, Active, NI
• Assay Sensitivity, Multiplicity of Analyses
• Interim Analysis
• Intent to Treat & Final Analyses
• Missing Data, Multiple Analyses & Assay Sensitivity
• Prediction of Success of a Trial
• Evidence Based Medicine
• Conclusions
2

3. Global Ethical Medicine

4. 4

5. Source: MDS, New Zealand 5

6. Investigational New Drug
• The pharmaceutical industry begins talking to FDA prior to
submission of an IND
• Show FDA the results of preclinical testing in animals and any
prior human experience
• What is being proposed to do for human testing
• At this stage, the FDA decides whether it is reasonably safe
for the company to move forward with testing the drug in
humans
6

7. Phase 1
• Phase 1 studies are usually conducted in
healthy volunteers
• Goal:
– determine what the drug's most frequent side
effects are
– how the drug is metabolized and excreted
• The number of subjects typically ranges from
20 to 100
7

8. Early Phase Clinical Trials
8

9. Phase 2
• Phase 2 begins if Phase 1 studies don't show unacceptable
toxicity
• Phase 1 → Safety; the emphasis in Phase 2 → Effectiveness
– Obtaining data on whether the drug works in people with a certain
disease or condition
• RCTs
– Gold standard for evidence of efficacy
– Patients receiving the drug vs patients receiving a placebo or a
different drug
– Randomized, well designed
• Safety continues to be evaluated
• N = a few dozen to about 300
9

10. Phase 3
• Phase 3 studies begin if evidence of effectiveness
is shown in Phase 2
• Phase 3: large-scale efficacy & safety studies
• Jointly decided by FDA & sponsor
• A is pre-NDA meeting with FDA is common, right
before a new drug application is submitted
• Gather more information about safety and
effectiveness
– different populations and different dosages
– using the drug in combination with other drugs
• N = several hundred to about ≥4,000 patients
10

11. 11

12. Phase 4
• Postmarketing study commitments are called Phase 4
commitments
– Studies conducted after the FDA has approved a product
for marketing
• The FDA uses postmarketing study commitments to
gather additional information about a product's
– Safety (mainly)
– also efficacy or optimal use
12

13. 13

14. Regulatory Approvals: USFDA
2008: 22 NMEs; 3 BLAs
14

15. Clinical Trials: Testing Medical Products in
Humans
• Clinical studies, test potential treatments in human volunteers
to see whether they should be approved for wider use in the
general population
– A treatment could be a drug, medical device, or biologic,
such as a vaccine, blood product, or gene therapy
– A new treatment may or may not be “better”
– Complete and accurate research
– Protection and well being of participants
• Ethics, consent, audit
– Documentation
15

16. Clinical Trials..
• Drug studies in humans can begin only after an
IND is reviewed by the FDA and a local
institutional review board (IRB)
• The board is a panel of scientists and non-
scientists in hospitals and research institutions
that oversees clinical research
16

17. Institutional (Ethical) Review
• IRBs approve the clinical trial protocols
– the type of people who may participate in the clinical
trial
– the schedule of tests and procedures
– the medications and dosages to be studied
– the length of the study
– the study's objectives
– other details
• IRBs make sure the study is
– acceptable
– participants have given consent
– Participants are fully informed of their risks
– researchers take appropriate steps to protect patients from
harm 17

18. Data
• There are legal and ethical reasons for reporting all
relevant data collected during the drug development
process
• Some reporting strategies already exist in the 1988
Guidelines, ICH E3 and E9
• Electronic Submissions and desktop review capabilities
will help all of us make better use of clinical data in
NDA’s
• There may be better strategies and these should be
considered
18

19. What does FDA Look for?
• FDA approves a drug application based on
– Substantial evidence of efficacy & safety from
“adequate and well-controlled investigations”
– A valid comparison to a control
– Quantitative assessment of the drug’s effect
• (21 CFR 314.126.)
• The design of cancer trials intended to support
drug approval is very important
19

20. Adequate and Well-Controlled
Studies
• Because the course of most diseases is variable, you need a control group,
a group treated just like the test group, except that they don’t get the drug,
to distinguish the effect of the drug from spontaneous change, placebo
effect, observer expectations
• 21 CFR 314.126 describes the following controls
– Placebo
– No treatment
– Dose response
– Active control
• Superiority of non-inferiority
– Historical
• Placebo, dose response or superiority are usually convincing studies
20

21. Adequate and Well-Controlled
Studies..
• Minimization of Bias: a unidirectional tilt favoring one group,
a non-random difference in how test and control group are
selected, treated, observed, and analyzed
– These are the 4 main places bias can enter
• Remedies:
– Blinding (patient and observer bias)
– Randomization (treatment and control start out equal)
– Careful specification of procedures and analyzes in a
protocol to avoid
• Choosing the most favorable analysis out of many (bias)
• Having so many analyses that one is favorable by chance
(multiplicity)
Source: RJ Temple, US FDA, Unapproved Drugs Workshop January 21
2007

22. Design Concepts
Non-Inferiority
Difference in Clinical Efficacy (Є)
Superiority
+δ
0
Equivalence
-δ
Inferiority
Non-Superiority
Equality δ = Meaningful Difference
22

23. Purposes of Active Trials
• The purpose of an active control trial
could be to demonstrate that a new
experimental treatment is either
• superior to the control
• equivalent to the control, or
• non-inferior to the control
• superior to a virtual placebo
23

24. Study Design: Approaches
• Randomised Controlled Trials (RCT) most preferred approach
– Demonstrating superiority of the new therapy
• Other approaches
– Single arm studies (e.g., Phase II)
• e.g., when many complete responses were observed or when
toxicity was minimal or modest
– Equivalence Trials
– No Treatment or Placebo Control Studies
– Isolating Drug Effect in Combinations
– Studies for Radiotherapy Protectants and Chemotherapy
Protectants
24

25. Randomized Clinical Trials
• Gold standard in Phase III
• Single centre CT
– Primary and secondary indications
– Safety profile in patients
– Pharmacological / toxicological characteristics
• Multi-centre CT
– Confirmation of the above
– Effect size
– Site, care and demographic differences
– Epidemiological determination
– Complexity
– Far superior to meta-analyzed determination of effect
25

26. Placebo Control Equality Trials
• No anticancer drug treatment in the control arm is unethical
• Sometimes acceptable
– E.g., in early stage cancer when standard practice is to give
no treatment
– Add-on design (also for adjuvants)
• all patients receive standard treatment plus either no
additional treatment or the experimental drug
– Placebos preferred to no-treatment controls because they
permit blinding
– Unless very low toxicity, blinding may not be feasible
because of a relatively high rate of recognizable toxicities
26

27. Reasons for Active Control
1. Ethics – For trials involving mortality or serious morbidity
outcome, it is unethical to use placebo when there are
available active drugs on the market
2. Assay sensitivity – In trials involving psychotropic drugs,
placebo often has large effect. An active control is sometime
used to demonstrate that the trial has assay sensitivity
3. Comparative purpose – To show how the experimental drug
compares to another known active drug or a competitor
27

28. Non-Inferiority Trials
• New drug not less effective by a predefined
amount, the noninferiority (NI) margin
– NI margin cannot be larger than the effect of the
control drug in the new study
– If the new drug is inferior by more than the NI
margin, it would have no effect at all
– NI margin is some fraction of (e.g., 50 percent) of
the control drug effect
28

29. Drug or Therapy Combinations
• Use the add-on design
– Standard + Placebo
– Standard + Drug X
• Effects seen in early phases of development
– Establish the contribution of a drug to a standard
regimen
– Particularly if the combination is more effective
than any of the individual components
29

30. What to Measure?
• Primary outcome measure: The health parameter measured
in all study participants to detect a response to treatment
• Secondary outcomes measure: Other parameters that are
measured in all study participants to help describe the effect of
treatment
• Baseline variables: The characteristics of each participant
measured at the time of random allocation.
– This information is documented to allow the trial results to
be generalised to the appropriate population/s
– Specific characteristics associated with the patient’s
response to treatment (such as age and sex) are known as
prognostic factors
30

31. What to Measure? E.g., Cancer
Trials
• Time to event end points
– Survival
– Disease free survival
– Progress (of disease) free survival
• Objective response rates
– Complete
– Partial
– Stable disease
– Progressive disease
• Symptom end points
• Palliation
• QoL
31

32. Cancer Trials – End Points
Endpoint Evidence Assessment Some Advantages Some Disadvantages
Survival Clinical benefit • RCT needed • Direct measure of • Requires larger and
• Blinding not benefit longer studies
essential • Easily measured • Potentially affected by
• Precisely crossover therapy
measured • Does not capture
symptom benefit
• Includes noncancer
deaths
Disease-Free Surrogate for • RCT needed • Considered to be • Not a validated survival
Survival (DFS) accelerated • Blinding clinical benefit by surrogate in most settings
approval or preferred some • Subject to assessment
regular • Needs fewer bias
approval* patients and shorter • Various definitions exist
studies than survival
32

33. Interim analysis
after each new response or group of responses
an interim analysis is performed
⇓
enough evidence to stop the trial
or
continue the trial
→ continuous sequential or group sequential analysis
33

34. Why Interim Analyses?
• Ethics: superiority of a treatment
• Safety: inferiority of a treatment /
toxicity of a treatment
• Economy: costly therapy
no clinically relevant difference in
effect between treatments
34

35. False Positives in Interim Analyses
Interim analysis for a trial in non-Hodgkins lymphoma; n=130,
IA after enrolment of each 25 patients
Response Rate Response Rate
CP CVP
Analysis 1 3/14 5/11 1.63
Analysis 2 11/27 13/24 0.92
Analysis 3 18/40 17/36 0.04
Analysis 4 18/54 24/48 3.25 0.05<P<0.1
Analysis 5 23/67 31/59 4.25 0.016<P<0.05
CP=Cytoxan-prednisone
CVP=Cytoxan-vincristine-prednisone
Source: Stuart J. Pocock, Clinical Trials
35

36. Interim Analysis of Data
2 Looks 3 Looks
How many times can
0.05
0.05
you look into the
data?
Nominal Pvalue
Nominal Pvalue
0.03
0.03
o pocock
o ob+fle
0.01
0.01
o fle+har+ob
0.0
0.0
1 2 1 2 3
Look Look
4 Looks 5 Looks
Type 1 error at kth
0.05
0.05
test is NOT the same
Nominal Pvalue
Nominal Pvalue
as the nominal p
0.03
0.03
value for the kth test
0.01
0.01
0.0
0.0
1 2 3 4 1 2 3 4 5
Look Look
36

37. Considerations for IA
– Stopping rules for significant efficacy
– Stopping rules for futility
– Measures taken to minimize bias
– A procedure/method for preparation of data for analysis
– Data has to be centrally pooled, cleaned and locked
– Data analysis - blinded or unblinded?
– To whom the interim results will be submitted?
• DSMB
• Expert Steering Group
– What is the scope of recommendations from IA results?
– Safety? Efficacy? Both? Futility? Sample size
readjustment for borderline results?
37

38. Equality Designs
(e.g., 2-Sample, Parallel)
H0 : Є = 0 HA : Є ≠ 0
Reject H0 when
p1— p2
ˆ ˆ
> z/2
√p1(1—p1)/n1 + p2(1— p2)/n2
ˆ ˆ ˆ ˆ
Where p1— p2 are true mean response rates
ˆ ˆ
from Test & Control
38

39. Superiority/Non-Inferiority Designs
(e.g., 2-Sample, Parallel)
H0 : Є ≤ δ HA : Є > δ … Superiority
H0 : Є ≥ δ HA : Є < δ … Non-Inferiority
Reject H0 when
p1— p2 – δ
ˆ ˆ
> z
√p1(1—p1)/n1 + p2(1— p2)/n2
ˆ ˆ ˆ ˆ
39

40. Survival Data – The Kaplan-Meier Estimator
1.0
0.75
Survival
0.50
0.25
~35% Patient will
survive beyond 0.8
years
0.0
Time (Year)
0.0 0.2 0.4 0.6 0.8 1.0 40

41. Include all Patients: ITT
• It can be justified to look at data and drop the
“outliers”, poor compliers, inappropriately
entered patients
• It is even plausible and acceptable as an
academic principle
• But if not rigorously planned, such exclusion
can lead to bias
• Even when planned, it can lead to imbalances
that also introduce bias
41

42. Intent-to-Treat Principle
• All randomized patients
• Exclusions on prespecified baseline criteria permissible
– also known as Modified Intent-to-Treat
• Confusion regarding intent-to-treat population: define and agree upon in
advance based upon desired indication
• Advantages:
– Comparison protected by randomization
• Guards against bias when dropping out is realted to outcome
– Can be interpreted as comparison of two strategies
– Failure to take drug is informative
– Refects the way treatments will perform in population
• Concerns:
– “Difference detecting ability”
42

43. Per Protocol Analyses
• Focuses on the outcome data
• Addresses what happens to patients who remain on therapy
• Typically excludes patients with missing or problematic data
• Statistical concerns:
– Selection bias
– Bias difficult to assess
43

44. Intent to Treat & Per Protocol
Analyses
• Both types of analyses are important for approval
• Results should be logically consistent
• Design protocol and monitor trial to minimize exclusions
• Substantial missing data and poor drug compliance
weaken trial’s ability to demonstrate efficacy
44

45. Missing Data
• Protocol should specify preferred method for
dealing with missing primary endpoint
– ITT
• e.g., treat missing as failures
• e.g., assign outcome based on blinded case-by-case
review
– Per Protocol
• e.g., exclusion of patients with missing endpoint
45

46. Multiple Analyses
• The two main problems introduced by multiple analyses are
– firstly, the increased probability of detecting intervention
effects where none exist (“false positives” owing to
multiple comparisons — type I errors)
– secondly, the limited capability (“power”) of trials to detect
a true treatment effect in secondary outcomes if not enough
participants are enrolled to show a statistically significant
difference in these outcomes (“false negatives” — type II
errors)
46

47. Assay Sensitivity
• The critical question is whether a non-inferiority trial, for
example, could distinguish the control from placebo and shown
an effect of the non-inferiority margin
• If it could
– the trial is said to have said to have “assay sensitivity”
• If a trial a trial has assay sensitivity
– then if C-T < M, T had an effect
• If the trial did not have assay sensitivity
– then even if C-T < M, we have learned nothing
• If you don’t know whether the trial had assay sensitivity, finding no
difference between C and T means
– Both drugs were effective
– Neither drug was effective
Source: RJ Temple, US FDA, Unapproved Drugs Workshop 47

48. Assay Sensitivity: Major Problem in NI
• In a non a non--inferiority trial, the trial itself does not show
the study’s ability to distinguish active from inactive therapy.
Assay ability to distinguish active from inactive therapy
• Assay sensitivity must, therefore, be deduced or assumed,
based on
1. historical experience showing sensitivity to drug effects
2. a close evaluation of study quality and, particularly
important
3. the similarity of the current trial to trials that were able to
distinguish the active control drug from placebo
• In many symptomatic conditions, such as depression, pain,
allergic rhinitis, IBS, angina, the assumption of assay
sensitivity cannot be made
Source: RJ Temple, US FDA, Unapproved Drugs Workshop January 48
2007

49. Data Safety and Monitoring Board
(DSMB)
• All trials may not need a DSMB
• DSMB Membership
– Medical Oncologist, Biostatistician and Ethicist
• Statistical expertise is a key constituent of a DSMB
• Three Critical Issues
– Risk to participants
– Practicality of Periodic Review of a Trial
– Scientific Validity of the Trial
49

50. Bayesian Prediction of Trial Success

51. Conclusion: Effect of Expertise
Trial Success Based on Centre Expertise
100
90
80
70
Success (%)
60
50
40
30
20
10
0
0 0.2 0.4 0.6 0.8 1
Probability of Expertise
Source: Unpublished Results, D. Chakraborty, University of Toronto

52. Conclusions
• Randomized Phase 3 Clinical Trials are very sophisticated and
complex
• Principal Investigators’, Monitoring Teams’, Biostatisticians’,
DSMB’s … all roles are important – Phase 3 Trials are team effort
• Phase 3 RCTs provide for the main evidence of efficacy and safety
• Clinical data is very complex (confounded, censored, skewed, often
fraught with missing data point), therefore, proper hypothesization
and statistical treatment of data are required
• Prospective RCTs are usually the preferred approach for evaluation
of new therapies
52

53. Conclusions
• Clinically meaningful margins must be well defined in Control trials
prospectively
– Superiority and non-inferiority margins must not be confused
• Two or one-sidedness of α should also be prospectively defined
• Power must be adequate
• Variance must be analysed using the right model
• Strategy for dealing with multiple end points must be prespecified
– Too many end points ot tests will increase the false positive (α) error
• Sometimes (e.g., in equality trials) statistically significant results may not be
medically significant
• Data censoring or skewed data must be well defined
– E.g., time to event data
53

54. Conclusions
• Randomisation and blinding offer a robust way to remove bias in end-point estimations
• Data must be accurately captured without any bias and analysed by prospectively
described methods
• Interim analysis should carefully plan ‘ spending’ function and the outcome
measure to be analyzed
• Final analysis should be done carefully, independently and meaningfully (medical as
well as scientific)
• Choose clinically relevant delta
• Design, conduct, and monitor trials to minimize missing data and poor compliance to drug
• Analysis
– Both intent-to-treat and per protocol analyses should be conducted
– Sensitivity analyses
• Outstanding medical and statistical issues must be brought to the fore
• Trial success can be predicted in many cases using Bayesian models
54

55. Thank You Very Much