1. Visualizations of Safety Data
Mat Soukup, Ph.D.
FDA/CDER/OTS/OB/Division of Biometrics 7

2. Disclaimer
The views expressed in this presentation are those of the presenter and must not be taken to represent policy or guidance on behalf of the Food and Drug Administration.

3. Outline Some General Graphic Principles
Graphics for Exploring & Understanding Safety
Application of Graphics in the Quantitative Evaluation of Safety

4. Duke, S. , Bancken, F. , Crowe, B. , Soukup, M. , Botsis, T
Duke, S., Bancken, F., Crowe, B., Soukup, M., Botsis, T., and Forshee, R. (2015) Seeing is believing:
Good graphic design principles for medical research. Statist. Med., doi: /sim.6549.

5. Nine Graphic Principles
Communication: Tailor each graph to its primary communication purpose
Type of Plot: Use the simplest plot that is appropriate for the information to be displayed
Technique: Use established techniques to clarify the message
Content: Every graph should stand on its own
Information: Maximize the data-to-ink ratio
Annotation: Provide legible text and information to aid in interpretation
Axes: Design axes that adequately aid in interpretation
Styles: Choose symbols and lines that are distinct and readable
Color: Make use of color if appropriate for the communication purpose
Best practices recommendations from the Clinical Trials Safety Graphics Working Group.
Available at: ctspedia.org/CTSpedia/BestPractices

6. Quantitative Data Visualization
Courtesy of Fabrice Bancken, Ph.D.

7. Qualitative Data Visualization
Courtesy of Fabrice Bancken, Ph.D.

8. Outline Some General Graphic Principles
Graphics for Exploring & Understanding Safety
Application of Graphics in the Quantitative Evaluation of Safety

9. Exploring & Understanding Safety
Graphical approaches are ideal to explore and understand safety data
Outlier detection
Utilize graphs that show extreme values
Effective to identify a small set of subjects with extreme values
Assessing temporal relationships
Use graphs that examine effects over time (cumulative dose)
Effective for looking at population trends
Assessing effects by subject characteristics
Use graphs the distinguish between values of baseline characteristic
Assessing relationships between variables
Scatterplots are helpful to look at effects of continuous variables that each measure some related functional aspect of safety (E.g. Hy’s Law for liver injury)
Dotplots are effective when looking at adverse events coded in the MedDRA dictionary

10. An Example Scenario Randomized clinical trial Questions
Two treatment groups: Active and Control
Safety outcome of interest: systolic blood pressure
Collected at baseline and two post-baseline visits (Visit 2 and Visit 3)
Population
Age ≥ 18 years old (categories: 18 – 39, 40 – 59, and 60+)
Treatment with an anti-hypertensive was allowed at baseline
Questions
Question 1: Are there any extreme changes in systolic blood pressure (baseline vs. Visit 3)?
Question 2: What is the trend in systolic blood pressure over time?
Question 3: Are findings consistent across age groups?
Data is simulated for this example scenario

11. Boxplot of Changes in SBP
Q1: Outlier Detection
Boxplot of Changes in SBP

12. Scatterplot of Changes in SBP
Q1: Outlier Detection
Scatterplot of Changes in SBP
Set of 12 outliers from previous figure

13. Q2: Trend Over Time

14. Q3: Findings by Age Group

15. Q3: Findings by Age Group
Set of 12 outliers

16. Outline Some General Graphic Principles
Graphics for Exploring & Understanding Safety
Application of Graphics in the Quantitative Evaluation of Safety

17. Assessing Mortality: Spiriva Respimat
Two sources of information to evaluate mortality
Vital status database (VSD): Four trials
Control: Placebo
Duration: 24 – 48 weeks
TIOSPIR: Safety outcome trial (1⁰ EP: mortality)
Control: Spiriva HandiHaler
Duration: > 2 years
TIOSPIR
VSD
SHH
(N = 5694) SR
(N = 5711)
Placebo
(N = 3047)
(N = 3049)
Deaths (IR)
439 (3.4)
423 (3.2)
51 (2.0)
68 (2.6)
HR (95% CI) -
0.96 (.84, 1.09)
1.33 (0.93, 1.92)
Source: August 14, 2014 Pulmonary-Allergy Drugs Advisory Committee Meetings

18. Comparing PopulationsF M
White
Non-White
USA
Non-USA
Smoker
Non-Smoker
Yes No
Courtesy of Dr. Bo Li (Primary Statistical Reviewer)

19. Cardiovascular Safety: IDeg/IDegAsp
Meta-analysis to evaluate cardiovascular safety
Database consists of 17 randomized trials
Active: Insulin Degludec (IDeg) and Insulin Degludec Aspart (IDegAsp)
Multiple comparators
Duration: 26 – 104 weeks
Indication: T2DM (13 trials), T1DM (4 trials)
Primary Endpoint: MACE+ (CV Death, nonfatal MI, nonfatal stroke, and unstable angina pectoris)
Primary Analysis Result for MACE+
IDeg/IDegAsp
(N = 5794)
Comparator
(N = 2461)
Events 95 37
HR (95% CI)
1.30 (0.88, 1.93)
Source: November 8, 2012 Endocrine and Metabolism Drug Advisory Committee Meetings

20. Forest Plot (MACE+) Decreasing Duration T2 IDegAsp T1
Courtesy of Dr. Bo Li (Primary Statistical Reviewer)

21. Randomized Trials for Safety Outcomes
Randomized, controlled, event-driven trials
Test H0: HR ≥ Δ0 versus Ha: HR < Δ0
Primary endpoint is based on a safety outcome
E.g. MACE (CV Death, nonfatal MI, nonfatal stroke)
Low event rates imply large trials to achieve sufficient power
Attribution of drug exposure to safety outcome is important in the evaluation
Considerations are given to the timing of the event relative to treatment exposure in the analysis
“On-study” Analysis: Includes all events from the trial
Includes events that occur while exposed to treatment or off treatment
“On-treatment” Analysis: Includes only events that occur while a subject is exposed to treatment
Typically includes a window of time after treatment exposure (w)
How large should w be?

22. Drug Exposure and Event Timing
On treatment
Off treatment
Event
time

23. What Events are Attributable to Treatment?
On Treatment Events
On treatment
Off treatment
Event
time

24. What Events are Attributable to Treatment?
On Treatment Events + small w
On treatment
Off treatment
Event
time

25. What Events are Attributable to Treatment?
On Treatment Events + medium w
On treatment
Off treatment
Event
time

26. What Events are Attributable to Treatment?
All Events (i.e. “on-study” analysis)
On treatment
Off treatment
Event
time

27. Visualizing Analysis Results
Objective of the Graph
Show both the on-study analysis and the on-treatment analyses
Show full picture of analyses
Features
The ascertainment window length (w), measured in days, is plotted along the x-axis
w = 0 includes only events that occur while exposed to treatment
As w increases it converges to the “on-study” analysis
HR estimate and confidence intervals for each analysis
All conducted with various censoring window (w)
Application1
Assessment of mortality from the SAVOR cardiovascular outcome trial of saxagliptin
1 Source: April 14, 2015 Endocrine and Metabolism Drug Advisory Committee Meetings

28. Mortality Analyses from SAVOR
“On Study” Analysis
Ascertainment analysis window length (w)
Courtesy of Dr. Shanti Gomatam (Primary Statistical Reviewer)

29. References/Links
Duke, S., Bancken, F., Crowe, B., Soukup, M., Botsis, T., and Forshee, R. (2015) Seeing is believing: Good graphic design principles for medical research. Statist. Med., doi: /sim.6549.
August 14, 2014 Pulmonary and Allergy Drug AC Meeting
November 8, 2012 Endocrine and Metabolism AC Meeting
April 14, 2015 Endocrine and Metabolism AC Meeting

30. Thank you!