Organized and Effective Interpretation of Clinical Laboratory Data: Graphs Make a Difference

Robert GordonBiostatistics and Medical SafetyJohnson & Johnson

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Overview

• Conflicts of Interest– J&J has financed the trip and accommodations

• Industry Drivers • Labs and Liver Methodology, Categories and

Questions• Some Examples• Q & A• Pitfalls and Suggestions

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Industry Drivers

Industry Documents

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Hepatology 48(5):1680-9, 2008

CIOMS: Council for International Organizations of Medical Sciences

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‘…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

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From This!

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To This!

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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

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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!

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Labs and Liver Categories

• Baseline & Trending

• Association between Lab Variables

• Liver Function Tests

• General

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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?

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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?

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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?

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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?

• Are there graphics which can aid in determining emerging safety signals?

• 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?

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Some Examples

From This!

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Subject ID Peak ALT ULN Peak Bili ULN Treatment

Subject 10013 5.65 0.74 ActiveSubject 10014 3.56 0.80 ActiveSubject 10015 2.73 1.13 ActiveSubject 10016 5.66 4.57 ActiveSubject 10017 2.47 0.88 ActiveSubject 10018 7.73 1.32 ControlSubject 10019 33.06 1.46 ActiveSubject 10020 11.60 3.23 ActiveSubject 10021 6.04 0.69 ControlSubject 10022 13.60 0.83 ActiveSubject 10023 1.85 0.58 ControlSubject 10024 0.21 0.42 ActiveSubject 10025 53.06 1.29 ActiveSubject 10026 8.36 1.63 ActiveSubject 10027 25.67 1.93 ActiveSubject 10028 31.23 1.27 Active

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%)

Total 1456 (96%) 59 (4 %) 1517 (100%)CONTROL GROUP

  Peak Bilirubin < 2 Peak Bilirubin ≥ 2 TotalPeak ALT < 3 579 (80%) 6 (<1%) 585 (81%)

Peak ALT ≥ 3 128 (18%) 5 (<1%) 133 (19%)Total 707 (98%) 11 (2%) 718 (100%^)

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To This!

Let’s Remove Coloring

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Simple and Powerful

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SubjectID LabTest StudyDay xULNSubject 123456US ALT (SGPT) 0 0.8Subject 123456US ALT (SGPT) 8 0.6Subject 123456US ALT (SGPT) 14 6Subject 123456US ALT (SGPT) 18 3.3Subject 123456US ALT (SGPT) 29 3Subject 123456US ALT (SGPT) 33 5.2Subject 123456US ALT (SGPT) 36 2.5Subject 123456US ALT (SGPT) 40 1.9Subject 123456US ALT (SGPT) 44 1.8Subject 123456US ALT (SGPT) 48 2.8Subject 123456US ALT (SGPT) 52 1.2Subject 123456US ALT (SGPT) 54 1Subject 123456US ALT (SGPT) 56 1.2Subject 123456US ALT (SGPT) 59 0.8Subject 123456US ALT (SGPT) 64 1.5Subject 123456US ALT (SGPT) 70 1.4Subject 123456US AST (SGOT) 0 1Subject 123456US AST (SGOT) 8 0.9Subject 123456US AST (SGOT) 14 3.5

Study Day Treatment ALT (xULN) AST (xULN) Bilirubin (xULN)

0   0.8 1 0.88 X 0.6 0.9 0.6

14   6 3.5 218   3.3 2.8 0.829 X 3 1.4 0.733   5.2 2.5 1.136   2.5 1.8 0.640   1.9 1.6 0.744 X 1.8 1.6 0.848   2.8 1.8 0.952   1.2 1.2 0.854   1 1.3 0.956   1.2 1 0.859 X 0.8 1 0.864   1.5 1.3 0.670   1.4 1.2 0.4

  STUDY DAY  0 8 14 18 29 33 36 40 44 48 52 54 56 59 64 70Treatment   X     X       X         X    ALT (xULN) 0.8 0.6 6 3.3 3 5.2 2.5 1.9 1.8 2.8 1.2 1 1.2 0.8 1.5 1.4AST (xULN) 1 0.9 3.5 2.8 1.4 2.5 1.8 1.6 1.6 1.8 1.2 1.3 1 1 1.3 1.2Bilirubin (xULN) 0.8 0.6 2 0.8 0.7 1.1 0.6 0.7 0.8 0.9 0.8 0.9 0.8 0.8 0.6 0.4

Trending over Time

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[Courtesy - Andreas Brueckner – Bayer]

Matrix Plots

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

Subjects of Interest

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[Courtesy – Alain Smits– J&J]

Age Profile by Treatment Group

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[Courtesy – Qi Jiang– Amgen]

Interpretation – ‘Graphics Reveal Data’ (Tufte)

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ANSCOMBE’s QUARTET

Graph 1 Graph 2 Graph 3 Graph 4

X Y X Y X Y X Y

10 8.04 10 9.14 10 7.46 8 6.58

8 6.95 8 8.14 8 6.77 8 5.76

13 7.58 13 8.74 13 12.74 8 7.71

9 8.81 9 8.77 9 7.11 8 8.84

11 8.33 11 9.26 11 7.81 8 8.47

14 9.96 14 8.1 14 8.84 8 7.04

6 7.24 6 6.13 6 6.08 8 5.25

4 4.26 4 3.1 4 5.39 19 12.5

12 10.84 12 9.13 12 8.15 8 5.56

7 4.82 7 7.26 7 6.42 8 7.91

5 5.68 5 4.47 5 5.73 8 6.89

Mean of Y's 7.5

Mean of X's 9

Regression line Y = 0.5X + 3

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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.

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• 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

The members of the FDA/Industry/Academia Working Group

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Thank You!

Q & A

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Pitfalls and Suggestions

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Our Hepatotoxicity Graph

Let’s Remove Coloring

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Reference Lines

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Major and Minor Tickmarks

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Logarithmic Scale ?

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Our Hepatotoxicity Graph

Know Your Data

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Proper Labeling

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