Robert Gordon FDA/QSPI Biostatistics, Statistical Programming and Data Management Summit Washington D.C. March 2012

 The views and opinions expressed in the following PowerPoint slides are those of the individual presenter and should not be attributed to any organization with which the presenter is employed or affiliated.  These PowerPoint slides are the intellectual property of the individual presenter and are protected under the copyright laws of the United States of America and other countries. Used by permission. All rights reserved. 2

 Motivation behind the use of graphics  Methodology across the sub teams  Labs and Liver categories, clinical questions and graphics  Some examples  Q & A 3

4 Mean of Y's7.5 Mean of X's9 Regression lineY = 0.5X + 3

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 Aid in identification of the unexpected  Numerical quantities focus on expected values, graphical summaries on unexpected values. (John Tukey)  Identify patterns within the clinical data  Temporal, dose groups, demographics  Combination of multiple lab parameters  Shift tables → matrix plots  Identification of syndromes, concurrent abnormalities  Much more effective interpretation of tabular data  Very large datasets 8

 Identify the clinical questions that we are trying to answer regarding labs and liver results  Align appropriate graphs with their ‘most’ appropriate category – multiple crossovers  Critique and enhance graphs to best answer the questions  Consolidated questions and graphs to a concise set  Provide background, uses, enhancements, and yes – CODE!  Upload to Wiki environment 9

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 Baseline & Trending  Association between Lab Variables  Liver Function Tests  Graphs that aid in Determining Emerging Safety Signals 11

 What are the changes and percent changes from baseline over time? ie, are abnormal lab values a result of an abnormal baseline or have values changed on study?  Is there a temporal relationship between treatment and lab values?  What are the toxicity grade trends over time?  What is the patient’s profile over time? 12

 What is the association between lab assessments?  Are there multiple lab values that are elevated or abnormal, either concurrently or not? ie, how can we easily identify patients with simultaneous elevations in multiple lab tests over time? 13

 How do we perform a comprehensive assessment of hepato-toxicity so that possible cases of drug induced liver injury are efficiently identified? 14

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Subject IDPeak ALT ULNPeak Bili ULNTreatment Subject Active Subject Active Subject Active Subject Active Subject Active Subject Control Subject Active Subject Active Subject Control Subject Active Subject Control Subject Active Subject Active Subject Active Subject Active Subject Active ACTIVE TREATMENT GROUP Peak Bilirubin < 2Peak Bilirubin ≥ 2Total Peak ALT < (73%)29 (2%)1138 (75%) Peak ALT ≥ 3347 (23%)30 (2%)377 (25%) Total1456 (96%)59 (4 %)1517 (100%) CONTROL GROUP Peak Bilirubin < 2Peak Bilirubin ≥ 2Total Peak ALT < 3579 (80%)6 (<1%)585 (81%) Peak ALT ≥ 3128 (18%)5 (<1%)133 (19%) Total707 (98%)11 (2%)718 (100%^) 17

18 How do we perform a comprehensive assessment of hepato-toxicity so that possible cases of drug induced liver injury are efficiently identified? Subject 2001Subject 2008 Subject 2035

19 SubjectIDLabTestStudyDayxULN Subject USALT (SGPT)00.8 Subject USALT (SGPT)80.6 Subject USALT (SGPT)146 Subject USALT (SGPT)183.3 Subject USALT (SGPT)293 Subject USALT (SGPT)335.2 Subject USALT (SGPT)362.5 Subject USALT (SGPT)401.9 Subject USALT (SGPT)441.8 Subject USALT (SGPT)482.8 Subject USALT (SGPT)521.2 Subject USALT (SGPT)541 Subject USALT (SGPT)561.2 Subject USALT (SGPT)590.8 Subject USALT (SGPT)641.5 Subject USALT (SGPT)701.4 Subject USAST (SGOT)01 Subject USAST (SGOT)80.9 Subject USAST (SGOT)143.5 Study DayTreatmentALT (xULN)AST (xULN)Bilirubin (xULN) X X X X STUDY DAY Treatment X X X X ALT (xULN) AST (xULN) Bilirubin (xULN)

20 SubjectIDLabTestStudyDayxULN Subject USALT (SGPT)00.8 Subject USALT (SGPT)80.6 Subject USALT (SGPT)146 Subject USALT (SGPT)183.3 Subject USALT (SGPT)293 Subject USALT (SGPT)335.2 Subject USALT (SGPT)362.5 Subject USALT (SGPT)401.9 Subject USALT (SGPT)441.8 Subject USALT (SGPT)482.8 Subject USALT (SGPT)521.2 Subject USALT (SGPT)541 Subject USALT (SGPT)561.2 Subject USALT (SGPT)590.8 Subject USALT (SGPT)641.5 Subject USALT (SGPT)701.4 Subject USAST (SGOT)01 Subject USAST (SGOT)80.9 Subject USAST (SGOT)143.5 Study DayTreatmentALT (xULN)AST (xULN)Bilirubin (xULN) X X X X STUDY DAY Treatment X X X X ALT (xULN) AST (xULN) Bilirubin (xULN)

21 What is the patient’s profile over time? ie, is it possible to display values for multiple lab parameters for subject(s) of interest?

22 What are the changes and percent changes from baseline over time? ie, are abnormal lab values a result of an abnormal baseline or have values changed on study?

23 What are the changes and percent changes from baseline over time? ie, are abnormal lab values a result of an abnormal baseline or have values changed on study?

24 [Courtesy - Andreas Brueckner – Bayer] What is the patient’s profile over time? ie, is it possible to display values for multiple lab parameters for subject(s) of interest?

25 Are there multiple lab values that are elevated or abnormal, either concurrently or not? ie, how can we easily identify patients with simultaneous elevations in multiple lab tests over time?

26 What is the patient’s profile over time? What is the patient’s profile over time? How do we perform a comprehensive assessment of hepato-toxicity so that possible cases of drug induced liver injury are efficiently identified?

Labs and Liver Sub Team Members  Robert Gordon (lead) – Johnson & Johnson  Andreas Brueckner - Bayer Healthcare  Susan Duke – GSK  Qi Jiang - Amgen  Mat Soukup – CDER 27 Former Sub Team Members  Ted Guo– CDER  Leslie Kenna – CDER  Yaning Wang - CDER

 Modern data graphics can do much more than simply substitute for small statistical tables. At their best, graphics are instruments for reasoning about quantitative information. Often the most effective way to describe, explore, and summarize a set of numbers – even a large set – is to look at pictures of those numbers. Furthermore, of all methods for analyzing and communicating statistical information, well-designed data graphics are usually the simplest and at the same time the most powerful.  The minimum we should hope for with any display technology is that it should do no harm.  There is no such thing as information overload, just bad design. If something is cluttered and/or confusing, fix your design  Excellence in statistical graphics consists of complex ideas communicated with clarity, precision, and efficiency. 28

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