1. Biostatistics in Cancer Clinical Trials
Presented at the “Recent Trends in Bio-Medical Biostatistics”,
Gujarat University, Ahmedabad on 24.02.2007
Dr. Bhaswat S. Chakraborty
VP, R&D, Cadila Pharmaceuticals Ltd.

2. Contents
 Research and Regulations of Cancer Trials
 Pivotal Cancer Trials (Phase III)
 Efficacy end points
 Merits and demerits
 Optimum Study Designs
 Superiority
 Non-Inferiority and other designs
 Sample Size Considerations
 Scientific questions
 Basics of sample size calculation
 Statistical Plan for a Cancer RCT
 Statistical Analysis of Cancer Data
 Tumor Data Analysis – an Example
 Conclusion

3. Worldwide Cancer Statistics (All Types)
From Parkin, D. M. et al.
CA Cancer J Clin 2005;55:74-108.

4. From Parkin, D. M. et al.
CA Cancer J Clin 2005;55:74-108.

5. Population
Based
Cancer
Registries in
India
(PBCR)

7. Cancer Research Today
 Research is conducted mainly on
 New Drugs
 New Combinations
 Radiotherapy
 Surgery
 In the West, research is usually done by large co-operative groups, in
addition to those mentioned for India
 In India
 Large Pharmaceuticals
 Co-operative Groups, e.g., ICON (Indian Co-operative Oncology Network)
 Regional Cancer Centres & Govt. sponsored studies
 Academia

8. 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

9. 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 Radio- and Chemotherapy Protectants

10. 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

11. 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

12. 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

13. 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

14. What to Measure?
 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

15. 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 Potentially affected by
measured crossover therapy
Precisely Does not capture
measured symptom benefit
Includes noncancer
deaths
Disease-Free Surrogate for RCT needed Considered to Not a validated
Survival accelerated Blinding be clinical benefit survival surrogate in most
(DFS) approval or preferred by some settings
regular Needs fewer Subject to assessment
approval* patients and bias
shorter studies Various definitions
than survival exist

16. Cancer Trials – End Points
Endpoint Evidence Assessment Some Advantages Some Disadvantages
Objective Surrogate for Single-arm or Can be assessed Not a direct measure of
Response accelerated randomized in single-arm benefit
Rate (ORR) approval or studies can be studies Usually reflects drug
regular used activity in a minority of
approval* Blinding patients
preferred in Data are moderately
comparative complex compared to
studies survival
Complete Surrogate for Single-arm or Durable CRs Few drugs produce high
Response accelerated randomized represent obvious rates of CR
(CR) approval or studies can be benefit in some Data are moderately
regular used settings (see text) complex compared to
approval* Blinding Can be assessed survival
preferred in in single-arm
comparative studies
studies

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

18. Phase III Cancer Trials
New Drug (or Regimen) is
Compared with a Standard
90
80 New
70 Standard
60
50
40
30
20
10
0
 Superiority Trials Survival DFS QoL

19. Phase III Cancer Trials
40
35 New
30 Standard
25
20
15
10
5
0
Survival DFS QoL
 Non-Inferiority or Equivalence Trials

20. Understanding Basics
 μ0 and μA
 Means under Null & Alternate Hypotheses
 σ02 and σA2
 Variances under Null & Alternate Hypotheses (may be the same)
 N0 and NA
 Sample Sizes in two groups (may be the same)
 H0: Null Hypothesis
 μ0 – μA = 0
 HA: Alternate Hypothesis
 μ0 – μA = δ
 Type I Error (α): False +ve
 Probability of rejecting a true H0
 Type II Error (β): False –ve
 Probability of rejecting a true HA
 Power (1-β): True +ve
 Probability of accepting a true HA

21. Basics of Sample Size Calculation
 Answer the scientific questions for the Trial size
 Understand the distribution and variability of the data
 Construct correct Null and Alternate hypotheses
 From the hypotheses derive formula for sample size
 Also make sure that this size trial has adequate power
to establish a true alternate

22. Five Key Questions
1. What is the main purpose of the trial?
2. What is the principal measure of patient outcome?
3. How will the data be analysed to detect a treatment
difference?
4. What type of results does one anticipate with standard
treatment?
5. How small a treatment difference is it important to detect
and with what degree of certainty?
 Answers to all of the five questions above enable us to
calculate the sample size and analyze the data with most
appropriate test of hypothesis.
Pocock SJ: Clinical Trials: A Practical Approach Chichester: Wiley; 1983

23. Start
Planning Reliable or historical
data available? No
Yes Use conventional
methods for analysis
Use bootstrap simulation for
sample size
Normally distributed
continuous data? Summary
Yes
measure: mean & mean
μT – μC difference
∆normal = Use parametric methods of
σ analysis, two sample ‘t’ or
ANOVA
Effect Size
No
2 [Z1-α/2 + Z1-β/2]2
Use non-parametric methods
Nnormal =
∆2normal of analysis, Mann-Whitney U
or Proportional Odds Model
Sample Size

24. Understanding Sample Size Determination
H0: μ0 – μA = 0 HA: μ0 – μA = δ
Critical Value
S.Error =σ(√2/N) S.Error =σ(√2/N)
Power = 1-β
β
α/2 α/2
0 δ
X0–XA
0+Z1-α/2σ√(2/N) δ–Z1-βσ√(2/N)

25. From the Previous Graph, We have
0+Z1-α/2σ√(2/N) = δ–Z1-βσ√(2/N)
Upon simplification,
2 [Z1-α/2 + Z1-β/2]2
Nnormal =
∆2normal

26. Sample Size: 2-Sample, Parallel
Superiority/Non-Inferiority Trial
(z+zβ)2 (p1 (1– p1) + p2(1 – p2))
N in each arm =
(Є – )2

27. Power: 2-Sample, Parallel Superiority/Non-
Inferiority Trial

28. Sample and Power for Simulated Tumor
Data
Expected Relative Risk
1
0.8
0.6 86
0.4 64
50
0.2
110
0
0.3 0.4 0.5 0.6 0.7 0.8
Relative risk

29. Statistical Plan
 Primary outcome considerations
 Study Design
 Sample size calculation
 Randomization
 Statistical consideration in Inclusion/Exclusion criteria
(Homogeneity within centre and strata)
 Accrual of patients
 Cleaning of data
 Interim Analysis
 Go/No go criteria
 α Considerations
 Final analysis
 Final conclusions

30. Accrual of Patients
 Study of the statistical trends in accrual patterns
 Seasonal
 Planned approaches
 Reasons for drop outs and loss to follow up
 Motivational factors
 Monitoring of recruitment progress and strategies
 Frequency
 Parameters
 Duration
 Understanding natural history and non-cancer, non-intervention deaths
 Changes in accrual after Interim Analysis

31. Randomisation
 Generation of randomisation scheme according to
 Centre
 Block
 Strata
 Patient
 Investigational Product to be given
 Measures of ensuring non-bias
 Allocations
 What should go on the labels
 Primary, secondary, tertiary packaging

32. Blinding
 Often difficult in oncology trials
 Test and control are of different characteristics
 Different routes of administration
 Different schedules
 New low toxicity oral treatments are relatively easy to
blind
 In other cases the end-point evaluating investigator
must be different from the one administering the drugs

33. Data Capture
 Manual or Electronic
 CRF is the main source of raw data capture
 Data must be quality assured
 Integrity, accountability, traceability
 Data must be validated
 All production and/or quality system software, purchased or
developed in-house
 Should document
 Intended use, and information against which testing results and other
evidence can be compared
 To show that the software is validated for its intended use

34. Data Cleaning & Locking
 Data are cleaned based on a good plan for interim or final analysis
 E.g.,
 Hundred percent data are made quality checked and assured
 Eligibility criteria for data selection
 Correction and editing
 Double data entry or other methods of data integrity
 Data will be locked after cleaning the data and resolving all the
queries
 SOP for data locking
 No change after locking
 Only locked data are used as input into data analysis program

35. Interim Analysis of Data
0.05
0.045
0.04
0.035
0.03
0.025
Nominal p
0.02
0.015
0.01
0.005
0
1 2 3 4
Looks

36. Interim Analysis of Data
2 Look
3s Lo
How many times
NominalPvue 0.3 0.5
NominalPvue 0.3 0.5
can you look into
the data?
o p o c o c k
o o b + fle
o fle + h a r + o b
0. 0.1
0. 0.1
1 2 1 2 3
L o o k L o o k
4 Look
5s Lo
NominalPvue 0.3 0.5
NominalPvue 0.3 0.5
Type 1 error at kth
test is NOT the
same as the
nominal p value
0. 0.1
for the kth test 1 2 3
L o o k
0. 0.1
4 1 2 3 4
L o o k
5

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?

38. Final Analysis and Conclusion
 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
 E.g., time to event data

39. 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”

40. 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

41. 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

42. 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

43. 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

44. time death group futime number reduction in size
0 0 1 0 1 1
1 0 1 1 1 3
4 0 1 4 2 1
7 0 1 7 1 1
Simulated Tumor Data: An Example
10
6
14
0
1
0
1
1
1
10
10
14
5
4
1
1
1
1
18 0 1 18 1 1
5 1 1 18 1 3
12 1 1 18 1 1
23 0 1 23 3 3
10 1 1 23 1 3
3 1 1 23 1 1
3 1 1 23 3 1
7 1 1 24 2 3
3 1 1 25 1 1
26 0 1 26 1 2
1 1 1 26 8 1
2 1 1 26 1 4
25 1 1 28 1 2
29 0 1 29 1 4
29 0 1 29 1 2
29 0 1 29 4 1
28 1 1 30 1 6
2 1 1 30 1 5
3 1 1 30 2 1
12 1 1 31 1 3
32 0 1 32 1 2
34 0 1 34 2 1
36 0 1 36 2 1
29 1 1 36 3 1
37 0 1 37 1 2
9 1 1 40 4 1
16 1 1 40 5 1
41 0 1 41 1 2
3 1 1 43 1 1
6 1 1 43 2 6
3 1 1 44 2 1
9 1 1 45 1 1
18 1 1 48 1 1
49 0 1 49 1 3

45. time death group futime number reduction in size
1 0 2 1 1 3
210 0 2 210 1 10
180 1 2 180 8 8
Simulated Tumor Data: An Example
180
10
13
0
0
0
2
2
2
180
10
13
1
1
1
6
1
1
221 1 2 365 2 7
1 1 2 17 5 3
18 0 2 18 5 1
142 1 2 365 1 5
2 1 2 19 5 1
76 1 2 21 1 4
22 0 2 22 1 1
25 0 2 25 1 10
25 0 2 25 1 5
25 0 2 25 1 1
6 1 2 26 1 1
6 1 2 27 1 6
2 1 2 29 2 6
2 1 2 36 8 8
38 0 2 38 1 1
22 1 2 39 1 11
4 1 2 39 6 5
24 1 2 40 3 1
41 0 2 41 3 2
41 0 2 41 1 1
1 1 2 43 1 1
44 0 2 44 1 1
2 1 2 44 6 1
45 0 2 45 1 2
2 1 2 46 1 4
46 0 2 46 1 4
49 0 2 49 3 3
50 0 2 50 1 1
87 1 2 100 4 6
54 0 2 54 3 4
38 1 2 54 2 1
59 0 2 59 1 3

46. Control Group
Simulated Tumor Data: An Example
70
Survival Time (Days)
60
50
40
30
20
10
0
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46
Patient No
Experimental Group
250
Survival Time (days)
200
150
100
50
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37
Patient No

47. Descriptive Statistics
± Standard deviation
120
100
80
time
60 Variable: time
grouped by: group
40 95%
N Mean Conf. (±) Std.Error Std.Dev.
20
1 48 15.77083333 4.241619672 2.108375644 14.60725495
0 2 38 47.73684211 19.66266124 9.704135603 59.8203094
1 2
Entire sample 86 29.89534884 9.420677178 4.738064537 43.93900293
group

48. Log-rank Test (Cox-Mantel)
Events Events
Kaplan Meier observed expected
1 29 21.09256306
2 18 25.90743694
Degrees of
1.2 Chi-square Freedom P
1 6.369814034 1 0.011607777
0.8
Probability
Censored
0.6 1
2
0.4
0.2
0
0 50 100 150 200 250
tim e

49. Cox Regression
at Mean
1.2
1
0.8
Probability 0.6
0.4
0.2
0
0 50 100 150 200 250
tim e
95% Hazard =
Coefficient Conf. (±) Std.Error P Exp(Coef.)
-0.823394288 0.667410889 0.340517244 0.015603315 0.438939237

50. Conclusion of Tumor Data
 Kaplan Meier
 Two survival patterns are different with a median of 12 and 70 days
for the Control and Experimental Groups
 Log-Rank Test
 The p-value of 0.0116 indicates significantly higher survival
experience of the experimental group
 Cox Regression
 Hazard of death for the Experimental group is estimated to be about
44% that of the Control group
 The log hazard coefficient is – 0.8234 (hence, e-0.8234=0.4389,
which gives us the estimated unadjusted Experimental hazard
ratio). It means that the expected log hazard for the Experimental
group is .8234 lower than it is for the Control group
 Difference in survival time in Experimental & Control groups is
highly significant (p=0.0156)

51. Conclusions
 Clinical testing of new Oncology products is very sophisticated
and complex
 A Statistician‟s role in Cancer trials is invaluable
 Statistical considerations must be thoroughly given attention and
built in while planning the study design and calculating the
sample size
 Cancer clinical data is very complex (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. Conclusions
 Survival as primary end point is preferred by regulatory agencies
 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
 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
 There are many oustanding statistical issues in Cancer trials that need no be
discussed and solved

53. Acknowledgements
 Dr. Nikunj Patel
 Dr. Sumit Goyal
 Dr. Manish Harsh
 Dr. Nilesh Patel
 Ms. Darshini Shah
Thank You Very Much