Robert Gordon Biostatistics and Medical Safety Johnson & Johnson Organized and Effective Interpretation of Clinical Laboratory Data: Graphs Make a Difference Robert Gordon Biostatistics and Medical Safety Johnson & Johnson

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Overview Conflicts of Interest / Disclaimer Industry Drivers J&J has financed the trip and accommodations Industry Drivers Labs and Liver Methodology, Categories and Questions Some Examples Q & A Pitfalls and Suggestions

Industry Drivers

Industry Documents Hepatology 48(5):1680-9, 2008

CIOMS: Council for International Organizations of Medical Sciences ‘…the risk to individual trial subjects is a critical consideration during product development, at a time when the effectiveness of a product is generally uncertain.’ – CIOMS VII

Why Graphics for Lab Data 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

From This!

To This! Amit, hieberger, Lane ‘Graphical approaches to the analysis of safety data from clinical trials’ 2007 Amit, hieberger, Lane ‘Graphical approaches to the analysis of safety data from clinical trials’ 2007

Labs and Liver Methodology, Categories and Questions

Methodology 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 Provide background, uses, enhancements, and yes – CODE!

Labs and Liver Categories Baseline & Trending Association between Lab Variables Liver Function Tests General

Baseline and Trending Are abnormal lab values a result of a borderline baseline lab value? What are the changes and percent changes from baseline over time? What are the toxicity grade trends over time? (what specifically would you be looking for in a trend?) What is the patient’s profile over time?

Association between Lab Variables Are there multiple lab values that are elevated or abnormal, either concurrently or not? How can we easily identify patients with simultaneous elevations in multiple lab tests over time? How can we display values for multiple lab parameters for subjects of interest?

Liver Function Tests How do we perform a comprehensive assessment of hepato-toxicity? How can we efficiently identify possible cases of drug induced liver injury? What are the maximum LFT values (or any max lab values) over time during the course of the study?

General Are there graphics which can aid in determining emerging safety signals? Is there a temporal relationship between treatment and lab abnormalities? What is the lab profile of the entire study, either by lab units or upper/lower limits of normal? What is the hazard for developing a low lab count over time while on treatment? Are there effective means of transitioning from whole population level to individual level?

Some Examples

ACTIVE TREATMENT GROUP From This! Subject ID Peak ALT ULN Peak Bili ULN Treatment Subject 10013 5.65 0.74 Active Subject 10014 3.56 0.80 Subject 10015 2.73 1.13 Subject 10016 5.66 4.57 Subject 10017 2.47 0.88 Subject 10018 7.73 1.32 Control Subject 10019 33.06 1.46 Subject 10020 11.60 3.23 Subject 10021 6.04 0.69 Subject 10022 13.60 0.83 Subject 10023 1.85 0.58 Subject 10024 0.21 0.42 Subject 10025 53.06 1.29 Subject 10026 8.36 1.63 Subject 10027 25.67 1.93 Subject 10028 31.23 1.27 ACTIVE TREATMENT GROUP   Peak Bilirubin < 2 Peak Bilirubin ≥ 2 Total Peak ALT < 3 1109 (73%) 29 (2%) 1138 (75%) Peak ALT ≥ 3 347 (23%) 30 (2%) 377 (25%) 1456 (96%) 59 (4 %) 1517 (100%) CONTROL GROUP 579 (80%) 6 (<1%) 585 (81%) 128 (18%) 5 (<1%) 133 (19%) 707 (98%) 11 (2%) 718 (100%^)

To This!

Let’s Remove Coloring

Simple and Powerful SubjectID LabTest StudyDay xULN Subject 123456US ALT (SGPT) 0.8 8 0.6 14 6 18 3.3 29 3 33 5.2 36 2.5 40 1.9 44 1.8 48 2.8 52 1.2 54 1 56 59 64 1.5 70 1.4 AST (SGOT) 0.9 3.5 Study Day Treatment ALT (xULN) AST (xULN) Bilirubin (xULN)   0.8 1 8 X 0.6 0.9 14 6 3.5 2 18 3.3 2.8 29 3 1.4 0.7 33 5.2 2.5 1.1 36 1.8 40 1.9 1.6 44 48 52 1.2 54 1.3 56 59 64 1.5 70 0.4   STUDY DAY 8 14 18 29 33 36 40 44 48 52 54 56 59 64 70 Treatment X ALT (xULN) 0.8 0.6 6 3.3 3 5.2 2.5 1.9 1.8 2.8 1.2 1 1.5 1.4 AST (xULN) 0.9 3.5 1.6 1.3 Bilirubin (xULN) 2 0.7 1.1 0.4

Trending over Time [Courtesy - Andreas Brueckner – Bayer]

Matrix Plots [SAS Institute - http://support.sas.com/sassamples/graphgallery/Health_and_Life_Sciences_Industry.html ]

Subjects of Interest [Courtesy – J&J]

Age Profile by Treatment Group [Courtesy – Qi Jiang– Amgen]

Interpretation – ‘Graphics Reveal Data’ (Tufte) ANSCOMBE’s QUARTET Graph 1 Graph 2 Graph 3 Graph 4 X Y 10 8.04 9.14 7.46 8 6.58 6.95 8.14 6.77 5.76 13 7.58 8.74 12.74 7.71 9 8.81 8.77 7.11 8.84 11 8.33 9.26 7.81 8.47 14 9.96 8.1 7.04 6 7.24 6.13 6.08 5.25 4 4.26 3.1 5.39 19 12.5 12 10.84 9.13 8.15 5.56 7 4.82 7.26 6.42 7.91 5 5.68 4.47 5.73 6.89 Mean of Y's 7.5 Mean of X's 9 Regression line Y = 0.5X + 3

Tufte Quotes ‘The Visual Display of Quantitative Information’ 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.

FDA/Industry/Academia Working Group Members Regulatory: George Rochester, Matt Soukup, Bruce Weaver, Janelle Charles, Chuck Cooper, Suzanne Demko, Robert Fiorentino, Richard Forshee, Eric Frimpong, Ted Guo, Pravin Jadjav, Stephine Keeton, Leslie Kenna, Joyce Korvick, Catherine Njue, Antonio Paredes, Je Summers, Mark Walderhaug, Yaning Wang, Markus Yap, Hao Zhu Industry: Ken Koury, Brenda Crowe, Rich Anziano, Navdeep Boparai, Andreas Brueckner, Susan Duke, Sylvia Engelen, Mac Gordon, Larry Gould, Matthew Gribbin, Liping Huang, Qi Jiang, Andreas Krause Academia: Mary Banach , Frank Harrell

Thank You! Q & A

Pitfalls and Suggestions

Our Hepatotoxicity Graph

Let’s Remove Coloring

Reference Lines

Major and Minor Tickmarks

Logarithmic Scale ?

Our Hepatotoxicity Graph

Know Your Data

Proper Labeling http://richworks.in/2010/04/50-most-stunning-examples-of-data-visualization-and-infographics/