Through real-world examples, this presentation teaches strategies for choosing appropriate outcome measures, methods for analysis and randomization, and sample sizes as well as tips for collecting the right data to answer your scientific questions.

2. JenniferMarcello,
SeniorBiostatistician
• Jennifer has experience in statistical planning, analysis, and reporting
for Phase 1, 2 and 3 clinical trials. Her research experience includes
over 5 years of experience working on clinical trials in oncology
including brain, skin, lung, breast, head and neck, and colorectal
cancers. In addition to oncology, she has experience in clinical trials for
palliative care, hepatitis, HIV, Alzheimer’s disease, anti-infectives, and
pain. Jennifer is trained in writing detailed statistical analysis plans,
performing sample size computation, preparing statistical analysis
specifications of analysis databases, developing summary displays
including summary tables for integrated safety and efficacy data,
utilizing SAS® software for programming and analysis of clinical data,
and providing ongoing safety evaluations for data monitoring
committees. She also has experience in analyzing quality of life data,
nutrition data and other patient reported outcomes.

3. Agenda
 Clinical Trial Study Flow
 Planning Your Trial
 Sample Size and Power
 Data Capture
 Randomization
 Statistical Analysis Plan
 Interim Analyses
 Database Lock
 Final Analyses
 CSR
When do I
need a
statistician?

4. Clinical Trial Study Flow
CSR
Displays
Analysis Datasets
SAP
CRF
Randomization Scheme
Protocol
Study Planning

5. Planning Your Trial
What is
our goal?
What data
do we
collect?
How do
we test
them?

6. Planning Your Trial - Example
• OA of the kneeIndication:
• Show our product is better
than placeboGoal:
• Pain by VAS on 50 foot walk
test, multiple collection times
Data to
collect:

7. Statistical Review- Example
• OA of the knee unilateral, bilateral, age?Population:
• ContinuousType of data:
• Repeated Measures
Number of time
points:
• LS Means Difference based on Repeated
Measures Population Average ModelTest:
• Unilateral vs. Bilateral, Missing DataSensitivity:

8. Planning Your Trial –
Blinding/Masking
Single
Blinding
• The participant doesn’t know to which intervention
they have been assigned.
Double
Blinding
• The participant and the investigator don’t know to
which intervention the participant has been
assigned.
Triple
Blinding
• The participant, investigator, and monitoring
committee do not know to which intervention the
participant is assigned.

9. Planning Your Trial –
Blinding/Masking
Advantages
• Decrease bias
• Participant response
not influenced by
knowledge of
treatment
• [DB] Investigator
preconception does
not matter
Disadvantages
• Patient consent
• Another layer of
complexity
• [TB]Patient safety
• Can the study really
be blinded?

10. Study Populations
Who do you want in your study?
• Inclusion & Exclusion criteria
Ensure that statistical inference can be made
to targeted market population
• Safety Analysis Set
• Full analysis sets (ITT Population)
• Per Protocol
• Depending on draft guidance, Clinically evaluable

11. Sample Size and Power
• TOO SMALL
• NOT ADEQUATE TO ADDRESS QUESTIONS
• TOO LARGE
• WASTED TIME, RESOURCES, AND MONEY
• POTENTIALLY EXPOSE PTS TO INEFFECTIVE TRT
 2 Basic Approaches
• Power of a hypothesis test (most common)
• Precision

12. Sample Size and Power
• Sample size calculation depends on:
• Planned analysis method/ hypothesis
• Clinically significant difference/ effect size
• α:Type I error
• β:Type II error
• σ: standard deviation
• Other considerations
• Cost
• Expected dropout rate

13. Sample Size and Power
Standard
Deviation
Power Desired
Acceptable Error
Clinically Significant Difference
Power Desired
Cost

14. Hypothesis Testing
Study comparing Drug X to placebo in lowering pain due
to osteoarthritis of the knee.
• The null hypothesis, H0, is the hypothesis to be tested.
H0: μdrug = μplacebo
• The alternative hypothesis, Ha, is the hypothesis which contradicts
the null hypothesis.
Ha: μdrug ≠ μplacebo

15. Possible Outcomes
of Hypothesis Tests
Correct
decision
Type II error
(β)
Type I error (α)
Correct
decision
True State of Nature
H0 true H0 false
Decision
RejectH0FailtorejectH0
False
negative
False
positive (H0 = null hypothesis)

16. SMOKE ALARM SYSTEM
Correct decision Type II error (β)
Type I error (α) Correct decision
No Fire Fire
AlarmNoAlarm
False
negative
False
positive

17. Sample Size and Power
The lower the
allowable error,
the bigger the
sample size
REMEMBER:

18. Sample Size and Power
The higher
the power,
the bigger the
sample size
REMEMBER:

19. Sample Size and Power
The bigger the
standard
deviation, the
bigger the
sample size
REMEMBER:

20. Sample Size and Power
The bigger the
clinically significant
difference, the
smaller the sample
size
REMEMBER:

21. Sample Size and Power
All differences can be
“statistically
significant” if you have
enough subjects,
power only for your
clinically significant
difference!
REMEMBER:

22. Statistical Significance
• Informally, a p-value is the probability under a
specified statistical model that a statistical summary
of the data (for example, the sample mean difference
between two compared groups) would be equal to or
more extreme than its observed value.
Ronald L. Wasserstein & Nicole A. Lazar (2016): The ASA's statement on p-values: context,
process, and purpose, The American Statistician, DOI: 10.1080/00031305.2016.1154108

23. Statistical Significance
• If p-value > α, we fail to reject the null
hypothesis, and the result is considered
statistically insignificant
• If p-value ≤ α, we reject the null hypothesis,
and the result is considered statistically
significant

24. Type I Error Rate Control
• Multiple looks (Unmasked/Unblinded
interim analyses)
• Multiple comparisons (More than one
primary hypothesis/endpoint)

25. Data Capture
CRF design is integral to capturing the data you
need for a successful analysis.
Statisticians need to participate in CRF design
to make sure assessments align with analyses!
It’s VERY difficult to go back and obtain data
after the fact!
Will this study be part of a submission?
• CDASH
(CRF=Case Report Form)

26. Data Capture – Missing Data
• Potential source of bias
• Minimize through protocol design
• Consult guidance, literature, and your
statistician for candidate methods for analysis
• Define and justify the proposed method
• Communicate with client and the internal team
See:
O’Neill, R and Temple, R. “The Prevention and Treatment
of Missing Data in Clinical Trials: An FDA Perspective on
the Importance of Dealing With It.” Clin Pharmacol Ther.
2012 Mar, 91(3); 550-4.

27. Clinical Data Standards
• Clinical Data Interchange Standards
Consortium (CDISC)
– Clinical Data Acquisitions Standard Harmonization
(CDASH) -> data collection
– Study Data Tabulation Model (SDTM) -> ‘raw’ data
– Analysis Dataset Model (ADaM) -> analysis ready
data
www.cdisc.org

28. Randomization
Reasons
• Reduction of bias
• Sound statistical basis for evaluation
• Produces treatment groups in which
the distributions of prognostic factors,
known and unknown, are similar

29. Randomization - Types
Simple Randomization
Like flipping a coin
Pro: Easy!
Con: You could randomize everyone to the same
group

30. Randomization - Types
Permuted Block Randomization
Randomized by block
Pro: Balance across intervention arms
Con: If you know the block size (and it’s small),
you may be able to guess the next treatment
Block 1 2 3 4 5 6 7 8 9
Treatments ABC CBA CAB BCA ACB ACB ABC CAB BCA

31. Randomization - Types
Stratified Randomization
If a key factor may affect how an intervention works in
a particular group, stratify by that factor.
Can combine this method with permuted block for
balance:
Permuted block stratified by baseline pain:
Moderate pain: AABB ABAB BBAA
Severe pain: ABAB BBAA BABA
3 blocks
of size 4
3 blocks
of size 4

32. Statistical Analysis Plan
• What data will we use?
• Which participants will be included?
• Exactly how will we analyze?
• Factors affecting analysis?
Gets down to the nuts and
bolts of the statistical
analyses

33. Statistical Analysis Plans
• Descriptive Statistics
• T – test and Non-
parametric Test (Wilcoxon
Test)
• ANOVA and ANCOVA
• Linear Regression
• Linear Mixed Models
Continuous
Outcomes
• Descriptive Statistics
• χ2 / Fisher’s Exact Test
• CMH test, Odds Ratio,
Relative Risk
• McNemar’s, Agreement
(Kappa)
• Logistic Regression
• Poisson Models
Categorical
Outcomes

34. Statistical Analysis Plans
• Kaplan Meier
• Log Rank Test
• Survival Rates
• Poisson Models
• Cox Proportional
Hazard Models
Survival
Analysis
• Pattern Mixture
Models
• Missing Data
Imputation Methods
• LOCF
• BOCF
• Multiple Imputation
Sensitivity
Analyses

35. Interim Analyses
• Is an interim analysis planned?
• What is the purpose of the interim analysis?
• Interim analysis timing and frequency
• Is an unblinded interim team needed?
• What is the data cleaning process for the
interim analysis?
• How does this affect α?

36. Interim Analyses - IDMC/DSMB
• SafetyPurpose
• Based on outcome and safety concerns
Timing and
Frequency
• Possible, not always necessaryUnmasked Team
• Interim database locks, snapshotsData Cleaning
• No efficacy dataAffected α?

37. Interim Analyses – Sample Size
Recalculation
• Ensure necessary sample size based on
SD assumptionsPurpose
• Usually just once
Timing and
Frequency
• Not necessaryUnmasked Team
• Interim database locksData Cleaning
• Not if performed in a pooled SD
adjustmentAffected α?

38. Interim Analyses – Stopping Rules
• EfficacyPurpose
• Based on primary outcomeTiming and
Frequency
• RequiredUnmasked Team
• Interim database locksData Cleaning
• Yes, if study continuesAffected α?

39. Interim Analyses – Adaptive
Designs
• EfficacyPurpose
• Based on primary outcome
Timing and
Frequency
• Usually, based on arms involvedUnmasked Team
• Interim database locksData Cleaning
• Yes, but not usually well controlled studies
(Phase I or II)Affected α?
Example: Pruning

40. Database Lock and Unmasking
• All analysis plans
should be complete
• Per-Protocol
population selection
• Statistician sign off
• Data quality
• Missing data?
• Unmasking

41. Final Analyses
• Hypothesis Testing
• Primary/Secondary Outcomes
• Safety Reporting
• Missing Data?

42. Clinical Study Report
• Statistical Reporting
• Primary Endpoint Discussion
• What does it all mean??

43. Summary
• Determining trial objectives and corresponding
endpoints, primary and secondary, is important initial
step.
• The type of trial should be aligned with sponsor’s
clinical plan.
• Determining the sample size early is very important to
the projected cost for running the trial.
• Statistical parts of the protocol serve as starting point
for all remaining activities.
• Emphasis on design and statistical principles protects
the study from bias by specifying the analysis methods
a priori.

44. References
• ICH-E3: Structure and Content of CSRs
• ICH-E6:Good Clinical Practice: Consolidated Guidance
• ICH-E9: Statistical Principles for Clinical Trials
• ICH-E10: Choice of Control Group and Related Issues in Clinical
Trials
• FDA Guidance for Industry: Various Indications
• National Academy of Science Missing Data Guidance
• “Statistical Reasoning in Medicine: The Intuitive P-Value Primer” –
Lemuel A. Moyé
• “Fundamentals of Clinical Trials” - Lawrence M. Friedman and Curt
D. Furberg

45. Contact Us
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info@rhoworld.com
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