1. 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

2. Disclaimer
The views and opinions expressed in the following PowerPoint slides are those of the individual presenter.
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.

3. 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

4. Industry Drivers

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

6. 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

7. 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

8. From This!

9. 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

10. Labs and Liver Methodology, Categories and Questions

11. 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!

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

13. 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?

14. 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?

15. 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?

16. 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?

17. Some Examples

18. 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%^)

19. To This!

20. Let’s Remove Coloring

21. 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

22. Trending over Time
[Courtesy - Andreas Brueckner – Bayer]

23. Matrix Plots
[SAS Institute - ]

24. Subjects of Interest
[Courtesy – J&J]

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

26. 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

28. 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.

29. 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

30. Thank You! Q & A

31. Pitfalls and Suggestions

32. Our Hepatotoxicity Graph

33. Let’s Remove Coloring

34. Reference Lines

35. Major and Minor Tickmarks

36. Logarithmic Scale ?

37. Our Hepatotoxicity Graph

38. Know Your Data

39. Proper Labeling