Graphics of Clinical Data: A Picture is Worth a Thousand Tables Tutorial 03 PhUSE 2009, Basel Switzerland Oct 19-21, 2009 Andreas Krause Lead scientist

Graphics of Clinical Data:A Picture is Worth a Thousand TablesTutorial 03 PhUSE 2009, Basel SwitzerlandOct 19-21, 2009Andreas KrauseLead scientist modeling and simulationActelion Clinical Pharmacology

A picture is worth a thousand tables. Slide 2

A Picture Is Worth A Thousand Tables: Table

event active placeborelative

differencep-value (etc.)

headache 45 23 +96%

diarrhea 23 15 +53%

pain 11 17 -35%

nausea 7 0 -

vomiting 5 2 +150%

fever 11 7 +57%

rash 8 7 +14%


A Picture Is Worth A Thousand Tables: Picture

• Safety Data sorted by difference in rate of AE

• Easy reading

– Highest incidence rate– Lowest incidence rate– Differences btw active and

placebo– Comparison between AEs


What are Graphics?

• Statistical graphics … are information graphics in the field of statistics used to visualize quantitative data

• Good statistical graphics can provide a convincing means of communicating to others the underlying message that is present in the data

• Graphical statistical methods have four objectives (Cleveland, 1994):– The exploration of the content of a data set– The use to find structure in data– Checking assumptions in statistical models– Communicate the results of an analysis

• Not using graphics increases the risk of missing a signal in the dataBased on the Wikipedia entry for “statistical graphics”


The Goal

• Communicate the information in the data– The real story, no fudging

• Marketing Graph - A pictorial representation which uses three dimensions, four colours and five cartoons to show one fact which probably isn't true.

• From: The Devil's Drug Development Dictionaries. http://www.senns.demon.co.uk/wdict.html

• Channel– The perception of the reader

• Benefits– Increase likelihood of detection of efficacy and safety signals

• Key Elements– Accuracy of interpretation– Ease of understanding– Speed of reading


Who Benefits?

• Study Teams (trial design and evaluation)• Data managers

– Quality control, detection of outliers and errors• Programmers

– Quality control– Creating a graph is more fun and creative than creating a table

• Medical writing, commercialization– Publications

• Management– See the evidence

• In the end: patients


Human Perception of Visualization

• Pattern Detection– Detection, Assembly, Estimation of relative orders and magnitudes

• Easier Tasks– Positioning/ comparison along a common scale– Relative lengths

• More Difficult Tasks– Comparison of angles and slopes

• In particular, pie charts are hard to interpret (correctly)– Area and volume (bar charts, 3D)– Color, shading, saturation: discern ordering


Principle: The Focus is on the Data

• “Maximize the data to ink ratio” (Tufte)• Avoid distortion• Use coherent displays• Encourage and facilitate visual comparison of groups of data

– Use few different plot types– Possibly add (faint) horizontal and/ or vertical lines– Choose appropriate order of categories for categorical data

• Clear purpose• Color and gray scale use only if it supports the analytical process


Principles: Some Consequences

• Aggregation– If feasible, show all data

• aggregation can be misleading (e.g., choice of categories)• Aggregate to an appropriate level

– If not, don’t: A scatterplot with 10,000 dots does not show anything

• Graph elements– Keep the axes out of the graphs (tickmarks, labels, everything)– No legend inside graphs– Annotation only if necessary– Color only if it helps visualization– Make reading of the graph intuitive

• Principle: Think before you graph!


Principle: Choice of Appropriate Aggregation Level

• Two treatments, A and B• Observed range cut into four intervals depending on a covariate• Poor data to ink ratio

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Treatment Comparison by Quartiles of Covariate

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spo

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[0.26, 2.93) [2.93, 4.39) [4.39, 6.49) [6.49,11.19]Trt A Trt B Trt A Trt B Trt A Trt B Trt A Trt B

The information of the bar plot is in the horizontal line on top of the bars.



• Better data to ink ratio• Allows comparison within and between groups (green and blue)• Lines suggest connection and order



• Show all the data• Added nonparametric smoother• Allows judgment about patterns/ relationships, outliers, and more

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Response by Treatment and Covariate


Principle: Encourage Comparison

• If the data can or shall be compared– Show comparable graphs– Make it easy to compare

• Stick with one or few types of graph– Do not change elements (axes, colors, groups)

• No visual distortion– Shading– Irrelevant use of colors or symbols


Encourage Comparison

• Compare– Across Individuals– Within individuals– Across groups (dose groups, body weights, genders, …)


Encourage Comparison

• AUC pre and post an event for four doses• Left: compare exposure/ dose by pre/ post• Right: compare pre and post by dose• Same eight box plots, different arrangement!

– To answer different questions

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Principle: A graph is a model!

• A statistical model aims at detecting the pattern in the data• So does the graph

• A graph represents the reality – do not distort it• Beauty is in the eye of the beholder – and so is interpretation of

the graph!

• What gets displayed, how it gets displayed, influences the perception

• Keep the “reader” in mind: a statistician, a medical doctor, a marketing expert


Elements of Graphics

• Axes• Lines• Symbols• Colors• Legends


Choosing the Axes

• Intuition: the y-variable is dependent on the x-variable.– y is modeled or graphed as a function of x

• Examples: – Heart rate is a function of the body weight– Body weight is a function of the gender– Fitted versus observed values

• The data are given• The model can be varied• Therefore, fitted=f(observed) • Do the fitted values depend on the observed values?


Axis Ranges

• Choosing the range can help or destroy pattern recognition• The graphs below just differ by the choice of the y-axis range• Consider inclusion of the origin

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our compound competitorcompound

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21.18


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Point Symbols

• Open circles are easier to discern than filled bullets• Single pixels are too small

• Circles, triangles, and other symbols– Require a legend– Force the reader to switch between graph and legend– Are difficult to discern, in particular with larger data sets

• If point symbols are used, consider using a monotone representation– 2, 3, 4, 5 edges (line, triangle, square, pentagon)– To represent increasing doses, ages, body weights

• Consider symbols that are meaningful– “A” for active, “L” for low dose, “H” for high dose– “0”, “2”, “5”, “8” for 0, 20, 50, 80 mg


Lines

• A line suggests an order– A time line– A trend line (in time)– A regression line (from low x-values to high x-values)

• A line is useless if not irritating if there is no order– Connecting races or genders

• Consider using monotonicity to represent ordered categories– Line widths: thin, medium, thick– Colors light gray, dark gray, black – Dotted and dashed lines– For ascending doses, ages, body weights


Legends

• “If it needs a legend, you might want to think again”

• Introduce an order that maps an order in the data– Green/ yellow/ red– Dark green/ light green/ yellow/ orange/ light red/ dark red– From dark to light– From thick to thin lines

• Introduce a logic such that looking at the legend once suffices– “L”, “M”, “H” for low, medium and high dose– “P” for placebo and “A” for active


Colors

• If colors can help understanding patterns:

• Use intuitive colors– Connotation

• Green: “good”• Red: “bad” (red alert)• Yellow: “watch out” (in between green and red)

• Example: patient response to treatment– Disease improvement: green– Disease worsening: red– Unchanged: yellow


Colors (2)

• Scientists have analyzed people’s pattern recognition abilities• Black and white patterns are easier to detect for humans• Thus, color distracts unless the color contains information• Shading and other effects are only there for their own sake

• Black and white– If colors do not help seeing the patterns, leave them out– Do not use colors to “spice up” the graph


Particular Applications


Comparing Like with Like

• Compare two data sets, x and y, to assess if they are “similar”• Simple: plot y against x• Can be very misleading: “suggestive”• Example: plot predicted values (y) versus observed values (x)

– The line is the identity line y=x

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observed

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• Two comparisons: Which set of (x, y) values is more similar?• The two data sets are the same!

– Just that x and y are swapped• So why is the visual perception so different?

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• Same axis ranges• One unit corresponds to the same number of pixels on both axes

– The graphs are square– Consequence: the identity line has a 45 degree slope

• Avoids visual bias


Comparison by Ordering

• To facilitate reading of graphs, consider the ordering of categories.• Left: categories ordered by percentage occurrence in active treatment• Right: categories ordered by absolute difference btw active and placebo


Comparison by Ordering and Grouping

• Group AEs into categories (nervous system, respiratory, skin, etc.)


Change From Baseline

• Change from baseline = change from 100% or 1• To avoid misleading visual perception, consider

– a graph symmetric around “no change”– Addition of a supportive line of no change


Outliers and Trends

• A few outliers can distort visual perception

• Here: a single outlier suggests a trend down

• 99 percent of the data lie in the top left corner of the graph

• Techniques to overcome outliers– ‘cut off’: if x > T then x=T (but

indicate cutting!)– Use logarithmic scale

• Can be irritating to misleading though


Higher Dimensions

• A standard 3D graph (the default)• Exercise: Read off the values.

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A B C D

**


Higher Dimensions

• A 3D data set on a tilted surface• What are the values? What bar is the highest?• Other aspects: angles and order change perception• Alternative: four lines with four points each: Might not look as “fancy”. • Might be more useful though

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Higher Dimensions: Add a Table?

• Optionally, you can show a table• Well…

– Why show a table if the graph is useful?

A B CD

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Higher Dimensions: Pie Charts

• The yellow area has many more pixels due to the shaded yellow side of the pie in front

– Values: yellow: 20, green: 30• One of the first options: add the numbers• But what’s the point of the graph then?

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Higher Dimensions: Real Life Examples

Left: the desired increase is artificially increased by an x-axis that goes up!

Top: what can be read off here – if anything?


Higher Dimensions

• Illustrate relationships between variables

• FEV1 as a function of age and body weight

– FEV1 increases with body weight

– FEV1 decreases with age

• Reading off values is difficult (y-axis and/ or color legend here)


Higher Dimensions: Slicing the surface

• Slices through the 3D surface: – Helps reading off values– Loses the 3D structure

body weight (kg)

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age (years)

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body weightcategories

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Tables

• And sometimes, a table is better than a graph• Colors, legends, labels, shading all distract from the fact that there are

only four numbers to show• What is the point of adding the values?

– Shows that the graph is redundant

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Inspiration: Example Applications combining the principles


A Six-Dimensional Scatterplot:Patient, biomarker, response, dose history, time, normal range

Subject (PID)

normal range: high/low limits

Dose levels (axis)

Dose history

Dotted line indicates missing end date of dosing

Clinical response:Green: (partial) responseYellow: stable diseaseRed: Progressive diseaseBlue: other

Time

Biomarker concentration


Six-Dimensional Scatterplot: 16 Subjects


Creative Ideas: Concentration-Time profiles

• Concentration-Time Profile• Trough levels labeled “T”• Steady state labeled “S”• Dose history as vertical bars

– Drug intake– Amount

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ObservationsDosing event

Population predictionIndividual prediction


Creative Ideas: Kaplan-Meier plot for safety data

• Percentage of subjects without the adverse event (AE)

• Each panel shows one AE over time

Time

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rviv

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P(X

>x)

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Burning Desquamation

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Edema Erosion/ulcer

Erythema Induration Itching

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Rash (vesicles)

Low DoseHigh DosePlacebo


Creative Ideas: Violin Plots

• Extension of the box-plot idea• Each step denotes a quantile (10%, 20%, etc.)

Grand Rapids

Duluth

University Farm

Morris

Crookston

Waseca

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yield

Grand Rapids

Duluth

University Farm

Morris

Crookston

Waseca

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1932

20 30 40 50 60

1931

yield


Summary

• Graphical presentation of PK, AE, labs, vital signs– Can provide clear messages on safety and efficacy aspects– Anchor the clinical study report– Rapidly and accurately identify subjects with potential safety issues

• Four key roles for statistical graphics– Exploratory: Understand/explore the data, cleaning, outliers– Review: Medical review of pop and patient level data

treatment, labs, AEs, vitals, concom meds, medical history

– Submission: Clinical study reports and registration documents– Presentation: Scientific and marketing applications


Ways Forward

• Statistical Graphics plug in to clinical data seamlessly– Standardized clinical data sources (e.g., CDISC)

• Statistical Graphics are suggested based on data types– Using the clinical graphics taxonomy

• A Statistical Graphics Palette is standardized across industry and authorities

– Simple interactions between industry and authorities– Safety and the Critical Path Initiative are making this reality


Principles of Good Graphics: Reading

• Edward Tufte (2006, 2001):– Beautiful evidence– The Visual Display of Quantitative Information– The cognitive style of PowerPoint

• W.S. Cleveland (1993): Visualizing Data

• Becker, R.A., Cleveland W.S., Shyu, M.J. (1996): – The visual design and control of Trellis displays

• D.A. Norman: The design of everyday’s things

• J.W. Tukey (1977): Exploratory Data Analysis


Backup Slides


Overview

• Motivational Examples: The Good, The Bad, and the Ugly• Principles of Graphics• Principles Illustrated

– Aggregation– Encouraging comparisons

• Graphics Elements– Axes, symbols, lines, legends, colors

• Particular applications– Comparison– Categorical variables– Change from baseline– Outliers– Higher Dimensions

• Creative Examples– A six-dimensional scatterplot, concentration-time profiles, categorical

variables, … • Discussion


Graphics: The Good, The Bad, and The Ugly

• Napoleon’s March on Russia by Minard, 1869.– The army’s location and direction, splits and rejoints– The size of the army– The temperatures during the retreat (low)



• ROOSEVELT PRE-WWII NEW DEAL– 1932 Unemployment Rate: 23.6%– 1940 Unemployment Rate: 14.6%– Rate Change: -9.0

• Clinton years– 1993: 6.9%– 2000: 4.0%

• One cannot reduce the unemployment rate by 14.6 percent if it currently is at 6.9 percent!

• Do not even think about percentages of a percentage.


Graphics: The Good, The Bad, The Worse, and The Ugly

• Do not even think about percentages of a percentage!

• But guess what: here it is!

• So from 6.9% to 4.0% unemployment is a drop by 36%!

• Presentation bias– Suggesting one-sided view

http://www.huffingtonpost.com/david-sirota/the-forgotten-math-pre-ww_b_155728.html

Aug 22, 2009



• The New York Times’ Illustration of Economic Cycles

– Expansion, Slowdown, Downturn, and Recovery

– Arbitrary coordinate system

– Years invisible– Suggested

quantification due to the axes – but what is twice the recovery?


Marketing

• It is their job to make it “look good”– That goal can be different from trying to understand the pattern

• Marketing Graph - A pictorial representation which uses three dimensions, four colours and five cartoons to show one fact which probably isn't true.

From:The Devil's Drug Development Dictionaries http://www.senns.demon.co.uk/wdict.html


Beware of Percentages

• Percentages are always tricky!• Consider this:

– “Our drug” shows a survival rate of 80%– The competitor drug shows a survival rate of 75%– So “our drug” has a survival rate that is 5% higher– Or is the competitor (80-75)/80 6% worse?– Or is our compound (80-75)/75 = 7% better?

– Switch to “risk” or death rate:• “our drug”: 20%, the competitor 25%• The death rate of the competitor is thus 25% higher!

• That line of argument is actually used (guess which!)


MY NOTES

• Session chairs: Antoine Brisacier, Dominique Pinet• Section: Tutorials• Paper number: TU03• Assigned time slot: 60 minutes (40 min presentation with 10 min for

questions)• http://www.phuse.eu/

• Deadlines:– Sep 4, 2009: Final papers and copyright grants to session chairs– Oct 2, 2009: Final Powerpoint due to session chairs– Oct 18: Speakers meeting at the conference


Abstract

Graphics of clinical data: A picture is worth a thousand tablesGraphics of clinical data: the good, the bad, and the ugly

Graphics is an essential tool for detecting structure in data, showing results, and communicating about data and results with clinical team members.

When used well, graphics can summarize complex information into a simple and easily interpretable display.

Graphics may allow the scientist to observe clinically relevant relationships which may have gone undiscovered, or to uncover a commonality amongst the outliers that may allow for better individualized pharmacotherapy.

This tutorial introduces basic concepts for good graphics (based on Edward Tufte's works). The principles are then translated for use with clinical data.

The tutorial covers a wide range of graphics with real examples of pharmacokinetic, efficacy, and safety data.


Introduction: Graphics

• SAS Tutorial at this conference: What’s graphically new in SAS 9.2?“This course concentrates of the vast enhancements to the graphical capabilities of SAS in the 9.2 release.”



• DO HERE


• Graphs show the number of samples taken from patients over time, lined up on a typical concentration-time curve.

• Time axis: time intervals that correspond to the “blobs” have to be guessed

• “blobs” overlay• Visual perception depends on the order of drawing of the blobs• Data (number of samples) is proportional to what: diameter,

area, other?• If anything, they should be proportional to the area, since this is

how the human eye interprets it.• Going to a 3-dimensional effect does not improve the clarity of

what is shown• For readability, vertical lines or a histogram underneath the

curve would be better.

Back up slides


Problematic Graphs: The Economist Illustrates the Demise of the Volksparteien

The Economist, Aug 6, 2009


Problematic Graphs: The Economist Illustrates the Demise of the Volksparteien

• The graphs on the left and on the right show the same data – just the order is reversed

• Note the difference in visual perception– All “trends” go down/ up

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Demise?


Graphics: The Good, The Bad, and The UglyExcursion into Bad Numbers

• Analogy of the previous in a clinical context:• (That kind of advertisement is actually not unusual)

• Patients treated with Femara for five years following surgery had a 13% reduced risk of death compared with those treated with tamoxifen (p=0.08).

• Five-year survival rates were 87.9% for women treated with Femara only; 86.2% for those treated with two years of tamoxifen followed by three years of Femara

• Difference in death rate: (87.9% – 86.2%) / (100% - 86.2%) = 13% !!

http://professional.cancerconsultants.com/oncology_main_news.aspx?id=42967, Aug 22, 2009


Final Word

“There are known knowns. There are things we know that we know.There are known unknowns. That is to say, there are things that we now know we don’t know. But there are also unknown unknowns. There are things we do not know we don’t know.”

Donald H. RumsfeldDefense Department BriefingFeb 12, 2002

http://en.wikipedia.org/wiki/Known_unknown, Jan 16, 2008

Documents

Graphics of Clinical Data: A Picture is Worth a Thousand Tables Tutorial 03 PhUSE 2009, Basel Switzerland Oct 19-21, 2009 Andreas Krause Lead scientist