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

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. www.fda.gov

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

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: 10.1002/sim.6549.

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: https://www. ctspedia.org/CTSpedia/BestPractices

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

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

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

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

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

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

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

Q2: Trend Over Time

Q3: Findings by Age Group

Q3: Findings by Age Group Set of 12 outliers

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

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

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

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

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

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?

Drug Exposure and Event Timing On treatment Off treatment Event time

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

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

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

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

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

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

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: 10.1002/sim.6549. www.ctspedia.org/StatGraphHome August 14, 2014 Pulmonary and Allergy Drug AC Meeting http://www.fda.gov/AdvisoryCommittees/Calendar/ucm403018.htm November 8, 2012 Endocrine and Metabolism AC Meeting http://www.fda.gov/AdvisoryCommittees/Calendar/ucm323208.htm April 14, 2015 Endocrine and Metabolism AC Meeting http://www.fda.gov/AdvisoryCommittees/Calendar/ucm436451.htm

Thank you!