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    <title>VIS-SIG Blog</title>
    <link>https://graphicsprinciples.github.io</link>
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    <description>The blog of the PSI Visualisation SIG
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    <item>
      <title>Wonderful Wednesdays December 2024</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2024-12-20-wonderful-wednesdays-december-2024</link>
      <description>Use Visualizations to answer the following questions: Inter-goniometer: do the two goniometers agree? Intra-goniometer: do the goniometers measure consistently the knee joint angle across the three repeated measurements?</description>
      <category>Inter-rater reliability</category>
      <category>Intra-rater reliability</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2024-12-20-wonderful-wednesdays-december-2024</guid>
      <pubDate>Wed, 11 Dec 2024 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2024-12-20-wonderful-wednesdays-december-2024/./images/Fig1_raw - Thomas Weissensteiner.png" medium="image" type="image/png" width="937" height="770"/>
    </item>
    <item>
      <title>Wonderful Wednesdays November 2024</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-november-2024</link>
      <description>The challenge was about improving a bad chart.</description>
      <category>Bad chart</category>
      <category>Improve</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-november-2024</guid>
      <pubDate>Wed, 13 Nov 2024 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-november-2024/./images/WW55_fig1 - Thomas Weissensteiner.png" medium="image" type="image/png" width="1085" height="842"/>
    </item>
    <item>
      <title>Wonderful Wednesdays October 2024</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-october-2024</link>
      <description>Why personalized dosing? „Again and again, it has been shown that certain patients benefit more from individual drugs, while others suffer more heavily from side effects… …and still they are usually treated with a standard dose." Free translation of a quote from Prof. Dr. Dr. Gerd Geisslinger, Executive Director, Fraunhofer Institute for Translational Medicine and Pharmacology, Frankfurt</description>
      <category>Personalised dosing</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-october-2024</guid>
      <pubDate>Wed, 09 Oct 2024 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-october-2024/./images/doris - Bodo Kirsch.png" medium="image" type="image/png" width="1774" height="760"/>
    </item>
    <item>
      <title>Wonderful Wednesdays September 2024</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-september-2024</link>
      <description>Visualise the pattern of events (disease progression, death, etc) on a summary data level or on a patient level data. Highlight differences between treatments or subgroups.</description>
      <category>Progression free survival</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-september-2024</guid>
      <pubDate>Wed, 11 Sep 2024 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-september-2024/./images/augmented_survival_1.png" medium="image" type="image/png" width="1200" height="675"/>
    </item>
    <item>
      <title>Wonderful Wednesdays August 2024</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-august-2024</link>
      <description>When a new patient-reported outcome (PRO) is developed, there is interest in deriving the following, to support interpretation of data collected using the new tool: Minimal Clinically Important Difference (MCID) / Responder Definition (RD), Meaningful Between-Group Difference, Meaningful Score Categories. These are usually derived by using an existing, validated measure as the ‘gold standard’ to quantify e.g. disease status.</description>
      <category>Responder definition</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-august-2024</guid>
      <pubDate>Wed, 14 Aug 2024 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-august-2024/./images/improvement.png" medium="image" type="image/png" width="959" height="742"/>
    </item>
    <item>
      <title>Wonderful Wednesdays July 2024</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-july-2024</link>
      <description>The data set is based on a platform trial design. How can we investigate the effectiveness of early stopping for futility? What is the relationship between the quality of the surrogate endpoint and the average number of patients enrolled? Is there an interaction with futility stopping and treatment effect scenario? How do FWER and FWER_BA differ from each other with respect to treatment effect scenario and maximum number of cohorts? Can you find other interesting patterns in the data?</description>
      <category>Platform Trial Design</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-july-2024</guid>
      <pubDate>Wed, 10 Jul 2024 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-july-2024/./images/WW_Jul24_03 - Steve Mallett.png" medium="image" type="image/png" width="1250" height="1000"/>
    </item>
    <item>
      <title>Wonderful Wednesdays June 2024</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-june-2024</link>
      <description>The data set is based on a platform trial design. How can we best show the effect of sample size on the power/type 1 error? Does sample size affect PTP and Disj_Power in the same way? How about in interaction with the type of data sharing? What is the relationship between number of cohorts, cohort inclusion rate and power? How can we investigate the effectiveness of early stopping for futility? What is the relationship between the quality of the surrogate endpoint and the average number of patients enrolled? Is there an interaction with futility stopping and treatment effect scenario? How do FWER and FWER_BA differ from each other with respect to treatment effect scenario and maximum number of cohorts? Can you find other interesting patterns in the data?</description>
      <category>Platform Trial Design</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-june-2024</guid>
      <pubDate>Wed, 12 Jun 2024 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2024-12-01-wonderful-wednesdays-june-2024/./images/power_TomMarlow_2024-06-04 - T M.jpg" medium="image" type="image/jpeg"/>
    </item>
    <item>
      <title>Wonderful Wednesdays May 2024</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2024-09-12-wonderful-wednesdays-may-2024</link>
      <description>In almost every study there is a flow chart to present the patient's path through the trial. What could the next level of study flow charts look like? Sandard study flow charts are basically organised lookup tables. A true visualisation can do much more. For example colour coding or indenting boxes can help organising. Also additional information can be embedded. This can easily done in R. For patient disposition Sankey diagrams can be extremely useful. This is even more powerful if used interactively. People charts even go without explanation.</description>
      <category>Study flow charts</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2024-09-12-wonderful-wednesdays-may-2024</guid>
      <pubDate>Wed, 08 May 2024 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2024-09-12-wonderful-wednesdays-may-2024/./images/Sankey - SNZ Skrivanek.png" medium="image" type="image/png" width="800" height="334"/>
    </item>
    <item>
      <title>Wonderful Wednesdays April 2024</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2024-09-10-wonderful-wednesdays-april-2024</link>
      <description>As statisticians, we often have to deal with power or sample size calculations. It can be very beneficial to visualise the impact that different factors have on the sample size or power. Produce data visualisation(s) or share tools that help your audience understand how power and sample size calculations work and what impacts the actual results of these calculations.</description>
      <category>Power Calculations</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2024-09-10-wonderful-wednesdays-april-2024</guid>
      <pubDate>Wed, 10 Apr 2024 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2024-09-10-wonderful-wednesdays-april-2024/./images/power - T M.jpg" medium="image" type="image/jpeg"/>
    </item>
    <item>
      <title>Wonderful Wednesdays March 2024</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2024-09-09-wonderful-wednesdays-march-2024</link>
      <description>Produce data visualisation(s) providing insights into how microbiome profiles change over time and what the differences are in microbiome profiles between Pre-Term Birth and Term Birth.</description>
      <category>Microbiome</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2024-09-09-wonderful-wednesdays-march-2024</guid>
      <pubDate>Wed, 13 Mar 2024 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2024-09-09-wonderful-wednesdays-march-2024/./images/WW_Mar24_01 - Steve Mallett.png" medium="image" type="image/png" width="7200" height="3600"/>
    </item>
    <item>
      <title>Wonderful Wednesdays February 2024</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2024-09-01-wonderful-wednesdays-february-2024</link>
      <description>Find a plot in a healthcare related publication and improve the visual presentation with only the data available from the publication.</description>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2024-09-01-wonderful-wednesdays-february-2024</guid>
      <pubDate>Thu, 08 Feb 2024 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2024-09-01-wonderful-wednesdays-february-2024/./images/dlqi_II_updated_2.png" medium="image" type="image/png" width="675" height="666"/>
    </item>
    <item>
      <title>Wonderful Wednesdays January 2024</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2024-09-01-wonderful-wednesdays-january-2024</link>
      <description>Best of 2023</description>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2024-09-01-wonderful-wednesdays-january-2024</guid>
      <pubDate>Wed, 10 Jan 2024 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2024-09-01-wonderful-wednesdays-january-2024/./images/meas_inc - Bodo Kirsch - Steve Mallett.png" medium="image" type="image/png" width="1000" height="687"/>
    </item>
    <item>
      <title>Wonderful Wednesdays December 2023</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-december-2023</link>
      <description>Demographic data display: The challenge is based on CDISC data. It is a standard ADSL data set for subject level data (demographics and baseline).</description>
      <category>Demographic data display</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-december-2023</guid>
      <pubDate>Wed, 13 Dec 2023 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-december-2023/./images/WW_Dec23_SM01 - Steve Mallett.gif" medium="image" type="image/gif"/>
    </item>
    <item>
      <title>Wonderful Wednesdays November 2023</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-november-2023</link>
      <description>Gatekeeping Procedure: Results are provided from a simulation of a phase III study in schizophrenia patients, investigating efficacy at 3 dose levels (Low, Medium, High) versus placebo. The study includes a primary endpoint and two key secondary endpoints. The study includes a total of 9 hypotheses, and multiplicity adjustments are required to control for type I error.</description>
      <category>Gatekeeping Procedure</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-november-2023</guid>
      <pubDate>Wed, 08 Nov 2023 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-november-2023/./images/example_2.png" medium="image" type="image/png" width="938" height="825"/>
    </item>
    <item>
      <title>Wonderful Wednesdays October 2023</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-october-2023</link>
      <description>Leverage visual analytics to identify data issues.</description>
      <category>data cleaning</category>
      <category>visual analytics</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-october-2023</guid>
      <pubDate>Wed, 11 Oct 2023 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-october-2023/./images/slopes.png" medium="image" type="image/png" width="840" height="506"/>
    </item>
    <item>
      <title>Wonderful Wednesdays September 2023</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-september-2023</link>
      <description>The PRIMUM study aimed to improve medication appropriateness in elderly patients with multimorbidity. A complex intervention involving general practitioners (GPs) and their healthcare assistants (HCA) was developed and compared to usual care. Check for a treatment effect on the MAI score after the intervention. Visualize the effect of adjusting the treatment effect for the MAI score at baseline.</description>
      <category>Treatment effect</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-september-2023</guid>
      <pubDate>Wed, 13 Sep 2023 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-september-2023/./images/example_1.png" medium="image" type="image/png" width="1792" height="983"/>
    </item>
    <item>
      <title>Wonderful Wednesdays August 2023</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-august-2023</link>
      <description>Benefit-Risk Assessment: For a drug to be approved for marketing, regulatory authorities must determine that the drug is effective and that its expected benefits outweigh its potential risks to patients. An Alzheimer’s Disease drug might have a positive benefit-risk profile for lowering blood pressure. To generate a hypothesis, an exploratory benefit-risk assessment will be conducted.</description>
      <category>Benefit-Risk Assessment</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-august-2023</guid>
      <pubDate>Wed, 09 Aug 2023 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-august-2023/./images/app.png" medium="image" type="image/png" width="1536" height="768"/>
    </item>
    <item>
      <title>Wonderful Wednesdays July 2023</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-july-2023</link>
      <description>US Disease Incidence</description>
      <category>US Disease Incidence</category>
      <category>Epidemiology data</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-july-2023</guid>
      <pubDate>Wed, 12 Jul 2023 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2023-12-17-wonderful-wednesdays-july-2023/./images/meas_inc - Bodo Kirsch.png" medium="image" type="image/png" width="1000" height="687"/>
    </item>
    <item>
      <title>Visualisation SIG Showcase</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2023-05-14-visualisation-sig-showcase</link>
      <description>This is an entry accompanying the poster at the PSI conference 2023.</description>
      <category>PSI conference</category>
      <category>Visualisation SIG Showcase</category>
      <guid>https://graphicsprinciples.github.io/posts/2023-05-14-visualisation-sig-showcase</guid>
      <pubDate>Sun, 11 Jun 2023 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2023-05-14-visualisation-sig-showcase/./images/VIS_SIG_poster.png" medium="image" type="image/png" width="1526" height="1080"/>
    </item>
    <item>
      <title>Wonderful Wednesdays May 2023</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2023-05-29-wonderful-wednesdays-may-2023</link>
      <description>Data visualisations to present Clinical Global Impression Data and to provide comparisons between the different groups (e.g. based on response differences or odds ratios for the different response categories).</description>
      <category>Clinical Global Impression (CGI)</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2023-05-29-wonderful-wednesdays-may-2023</guid>
      <pubDate>Wed, 10 May 2023 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2023-05-29-wonderful-wednesdays-may-2023/./images/lollipop_forest - Paolo Eusebi.png" medium="image" type="image/png" width="1200" height="800"/>
    </item>
    <item>
      <title>Wonderful Wednesdays April 2023</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2023-04-16-wonderful-wednesdays-april-2023</link>
      <description>This month’s challenge will be to visualise diary data, whereby patients complete a diary (grading a number of symptoms), each day. For this challenge, we consider a hypothetical diary, consisting of 6 symptoms (Exhaustion, Aching, Tenderness, Depression, Loneliness, Anxiety). 300 subjects (200 active, 100 placebo), grade each of these symptoms on a 4-point scale (0 = None, 1 = Minor, 2 = Moderate, 3 = Extreme) each day, for 100 days.</description>
      <category>Diary Data</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2023-04-16-wonderful-wednesdays-april-2023</guid>
      <pubDate>Wed, 12 Apr 2023 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2023-04-16-wonderful-wednesdays-april-2023/./images/WWWDiary_Aching - Agustin Calatroni.png" medium="image" type="image/png" width="2400" height="1500"/>
    </item>
    <item>
      <title>Wonderful Wednesdays March 2023</title>
      <dc:creator>Rhys Warham</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2023-04-16-wonderful-wednesdays-march-2023</link>
      <description>This month’s challenge was to improve a published visualisation. The visualisation displayed the mean (and SD) Dermatology Life Quality Index (DLQI) score for 12 studies on alopecia areata. The publication had visualised these using ‘dynamite plots’, a type of visualisation which has received criticism in the past.</description>
      <category>DLQI</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2023-04-16-wonderful-wednesdays-march-2023</guid>
      <pubDate>Wed, 08 Mar 2023 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2023-04-16-wonderful-wednesdays-march-2023/./images/DLQI_individual - Benjamin Lang.png" medium="image" type="image/png" width="4200" height="3230"/>
    </item>
    <item>
      <title>Wonderful Wednesdays December 2022</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2023-04-16-wonderful-wednesdays-december-2022</link>
      <description>The data is an excerpt of the official VAERS data created by US Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) that may be associated with vaccines.</description>
      <category>VAERS</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2023-04-16-wonderful-wednesdays-december-2022</guid>
      <pubDate>Wed, 14 Dec 2022 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2023-04-16-wonderful-wednesdays-december-2022/./images/double_dot_plot.png" medium="image" type="image/png" width="1321" height="713"/>
    </item>
    <item>
      <title>Wonderful Wednesdays November 2022</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2023-04-09-wonderful-wednesdays-november-2022</link>
      <description>The EORTC QLQ-C30 is a 30-item questionnaire that has been designed for use in a wide range of cancer patient populations and is a reliable and valid measure of the quality of life in cancer patients. It includes a number of different scales, but this challenge is focussed on the global health and quality of life scale (QL).</description>
      <category>Quality of life</category>
      <category>Missing data</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2023-04-09-wonderful-wednesdays-november-2022</guid>
      <pubDate>Wed, 09 Nov 2022 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2023-04-09-wonderful-wednesdays-november-2022/./images/line_plot - Paolo Eusebi.png" medium="image" type="image/png" width="3600" height="1800"/>
    </item>
    <item>
      <title>Wonderful Wednesdays October 2022</title>
      <dc:creator>Steve Mallett</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2023-04-09-wonderful-wednesdays-october-2022</link>
      <description>The EORTC QLQ-C30 is a 30-item questionnaire that has been designed for use in a wide range of cancer patient populations and is a reliable and valid measure of the quality of life in cancer patients (Aaronson et al., 1993).</description>
      <category>Quality of life</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2023-04-09-wonderful-wednesdays-october-2022</guid>
      <pubDate>Wed, 12 Oct 2022 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2023-04-09-wonderful-wednesdays-october-2022/./images/qlq-c30_fig_comb.png" medium="image" type="image/png" width="5520" height="2760"/>
    </item>
    <item>
      <title>Wonderful Wednesdays September 2022</title>
      <dc:creator>Rhys Warham</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2023-01-29-wonderful-wednesdays-september-2022</link>
      <description>The data is based on a study to assess the health status in patients with coronary heart diseases (see Ose et al., 2012). The research question was: what factors are associated with health status in usual primary care? The original data included 2,656 patients with 14 (patient and practice) characteristics (with clustered data structure). The outcome was EQ-5D VAS scale (Health related quality of life; range: [0; 1]). The data set for this challenge is a sub-sample of patients and variables and it is simulated data.</description>
      <category>EQ-5D</category>
      <category>Predictors</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2023-01-29-wonderful-wednesdays-september-2022</guid>
      <pubDate>Wed, 14 Sep 2022 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2023-01-29-wonderful-wednesdays-september-2022/./images/ed5d_relaimpo - Agustin Calatroni.png" medium="image" type="image/png" width="6375" height="5625"/>
    </item>
    <item>
      <title>Wonderful Wednesdays August 2022</title>
      <dc:creator>Rhys Warham</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2022-08-14-wonderful-wednesdays-august-2022</link>
      <description>Visualise the relative performance of the treatments based on ranking data. You may want to consider other metrics which can be derived from the ranking data and used to compare treatments.</description>
      <category>Ranking Data</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2022-08-14-wonderful-wednesdays-august-2022</guid>
      <pubDate>Wed, 10 Aug 2022 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2022-08-14-wonderful-wednesdays-august-2022/./images/NMA_ranks - Steve Mallett.PNG" medium="image" type="image/png" width="1900" height="966"/>
    </item>
    <item>
      <title>Wonderful Wednesdays July 2022</title>
      <dc:creator>Bodo Kirsch</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2022-08-14-wonderful-wednesdays-july-2022</link>
      <description>Detailed patient level data is meant to be an informed representation of the patient and what happened to the patient during the course of the study. These visualisations can help to get a quick overview on demographic information as well as exposure, concomitant medications, adverse events and laboratory data.</description>
      <category>Patient profiles</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2022-08-14-wonderful-wednesdays-july-2022</guid>
      <pubDate>Wed, 13 Jul 2022 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2022-08-14-wonderful-wednesdays-july-2022/./images/David.png" medium="image" type="image/png" width="1600" height="720"/>
    </item>
    <item>
      <title>Wonderful Wednesdays June 2022</title>
      <dc:creator>Irene de la Torre Arenas</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2022-06-12-wonderful-wednesdays-june-2022</link>
      <description>The purpose of this data visualization is to assess if there is an adverse impact on the growth of these children due to taking the drug (exploratory). Explanatory examples are welcome too (once you know the answer).</description>
      <category>Pediatric Growth</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2022-06-12-wonderful-wednesdays-june-2022</guid>
      <pubDate>Wed, 08 Jun 2022 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2022-06-12-wonderful-wednesdays-june-2022/./images/growth - Steve Mallett.png" medium="image" type="image/png" width="3600" height="3600"/>
    </item>
    <item>
      <title>Wonderful Wednesdays May 2022</title>
      <dc:creator>Lorenz Uhlmann</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2022-06-06-wonderful-wednesdays-may-2022</link>
      <description>The challenge is to visualize the impact of changing the definition of HiSCR response on the results. The idea is to change, for example, the first condition and assume that a decrease of 25% or 75% is needed to reach HiSCR response. The second and third condition can be changed separately, or a mix of changes to two or all conditions can be applied. How does this impact the results?</description>
      <category>HiSCR</category>
      <category>Combined endpoint</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2022-06-06-wonderful-wednesdays-may-2022</guid>
      <pubDate>Wed, 11 May 2022 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2022-06-06-wonderful-wednesdays-may-2022/./images/example_1a.png" medium="image" type="image/png" width="1298" height="665"/>
    </item>
    <item>
      <title>Wonderful Wednesdays April 2022</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2022-04-30-wonderful-wednesdays-april-2022</link>
      <description>The data reflects a phase III study - active vs placebo and the endpoint was Ordinal Scale for Improvement (OSCI). All patients started at OSCI=4 and daily measures over 35 days were performed. The challenge was to describe the treatment effect specifically taking into account the number and timing of patients who died, left the hospital or fully recovered.</description>
      <category>OSCI</category>
      <category>Data over time</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2022-04-30-wonderful-wednesdays-april-2022</guid>
      <pubDate>Wed, 13 Apr 2022 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2022-04-30-wonderful-wednesdays-april-2022/./images/covid_combined - Agustin Calatroni.png" medium="image" type="image/png" width="1204" height="926"/>
    </item>
    <item>
      <title>Wonderful Wednesdays February 2022</title>
      <dc:creator>Irene de la Torre Arenas</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2022-02-09-wonderful-wednesdays-february-2022</link>
      <description>The current example is focusing on uncertainty in study planning. In this Wonderful Wednesday Webinar, we assume that a phase III cardiovascular superiority study is planned and we will discuss how uncertainty can be displayed with regard to total study duration if the assumptions are not exactly met.</description>
      <category>Uncertainty</category>
      <category>Hazard rate</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2022-02-09-wonderful-wednesdays-february-2022</guid>
      <pubDate>Wed, 09 Feb 2022 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2022-02-09-wonderful-wednesdays-february-2022/./images/heatmaps_rlc - Agustin Calatroni.png" medium="image" type="image/png" width="1100" height="475"/>
    </item>
    <item>
      <title>Wonderful Wednesdays December 2021</title>
      <dc:creator>Abi Williams</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2022-01-16-wonderful-wednesdays-december-2021</link>
      <description>The challenge was to give a comprehensive visual overview of demographic data and baseline charactaristics.</description>
      <category>Demographic data</category>
      <category>Baseline characteristics</category>
      <category>Alzheimer data example</category>
      <guid>https://graphicsprinciples.github.io/posts/2022-01-16-wonderful-wednesdays-december-2021</guid>
      <pubDate>Wed, 08 Dec 2021 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2022-01-16-wonderful-wednesdays-december-2021/./images/baseline_SM - Steve Mallett.png" medium="image" type="image/png" width="3600" height="2400"/>
    </item>
    <item>
      <title>Wonderful Wednesdays November 2021</title>
      <dc:creator>Steve Mallett</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2021-12-05-wonderful-wednesdays-november-2021</link>
      <description>Understanding clinically meaningful differences is important to get a feeling for how relevant changes are on e.g. a patient reported outcome. These are often done by understanding how changes from baseline relate to the overall impression of the physician measured by the CGI-I.</description>
      <category>CGI-I data</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2021-12-05-wonderful-wednesdays-november-2021</guid>
      <pubDate>Wed, 10 Nov 2021 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2021-12-05-wonderful-wednesdays-november-2021/./images/plot_par_nonpar - Paolo Eusebi.png" medium="image" type="image/png" width="1920" height="1080"/>
    </item>
    <item>
      <title>Wonderful Wednesdays October 2021</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2021-11-06-wonderful-wednesdays-october-2021</link>
      <description>The goal was to find a way to visualize the impact of different risk factors (comorbidities and symptoms at admission to hospital) on the risk of death considering the competing risk of recovery.</description>
      <category>Competing risk</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2021-11-06-wonderful-wednesdays-october-2021</guid>
      <pubDate>Wed, 13 Oct 2021 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2021-11-06-wonderful-wednesdays-october-2021/./images/nijman_tables_flx - Agustin Calatroni.png" medium="image" type="image/png" width="1920" height="1080"/>
    </item>
    <item>
      <title>PSI Scientific Meeting</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2021-09-03-scientific-meeting</link>
      <description>PSI Scientific Meeting: Generating Insights through Modern Applications of Data Visualisation.
Find more information and sign up here: https://psiweb.org/events/event-item/2021/09/17/default-calendar/psi-scientific-meeting-generating-insights-through-modern-applications-of-data-visualisation</description>
      <guid>https://graphicsprinciples.github.io/posts/2021-09-03-scientific-meeting</guid>
      <pubDate>Fri, 17 Sep 2021 00:00:00 +0000</pubDate>
    </item>
    <item>
      <title>Wonderful Wednesdays September 2021</title>
      <dc:creator>Lorenz Uhlmann</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2021-09-06-wonderful-wednesdays-september-2021</link>
      <description>The goal of the challenge is to produce explanatory graphics - Visually demonstrate that there is a dose response in glycemic control for Rx (the higher the dose the lower the variability) and that the Rx med and Rx high demonstrate better glycemic control that SOC and these attributes are sustained.</description>
      <category>CGM</category>
      <category>Continuous variable</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2021-09-06-wonderful-wednesdays-september-2021</guid>
      <pubDate>Wed, 08 Sep 2021 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2021-09-06-wonderful-wednesdays-september-2021/./images/CGM-trelliscope.png" medium="image" type="image/png" width="1680" height="939"/>
    </item>
    <item>
      <title>Wonderful Wednesdays August 2021</title>
      <dc:creator>Mark Baillie</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2021-09-06-wonderful-wednesdays-august-2021</link>
      <description>For this edition of the Wonderful Wednesdays, the audience was asked to send examples of visualizations representing coronavirus data. The webinar was a showcase og the different types of visualizations, such as area graphs, forest plots, streamgraphs, or network analysis.</description>
      <category>Data journalism</category>
      <category>Wonderful wednesdays</category>
      <category>COVID-19</category>
      <guid>https://graphicsprinciples.github.io/posts/2021-09-06-wonderful-wednesdays-august-2021</guid>
      <pubDate>Mon, 06 Sep 2021 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2021-09-06-wonderful-wednesdays-august-2021/./images/Picture1.png" medium="image" type="image/png" width="745" height="408"/>
    </item>
    <item>
      <title>Wonderful Wednesdays July 2021</title>
      <dc:creator>Rhys Warham</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2021-07-18-wonderful-wednesdays-july-2021</link>
      <description>Patient Reported Outcomes (PROs) are often used within clinical trials. Prior to use as an endpoint, it is important to establish measurement properties of the instrument in the population of interest. This is conducted on blinded data, with no knowledge of the treatment allocation of each patient. Instead, anchor measures are used and compared to the score that is being evaluated. These anchor measures are usually simple questions that are designed to link conceptually to the overall target of evaluation or can be previously validated measures that assess similar concepts.</description>
      <category>Patient Reported Outcomes</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2021-07-18-wonderful-wednesdays-july-2021</guid>
      <pubDate>Wed, 14 Jul 2021 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2021-07-18-wonderful-wednesdays-july-2021/./images/Response Distributions - Philip Griffiths.png" medium="image" type="image/png" width="920" height="540"/>
    </item>
    <item>
      <title>Wonderful Wednesdays June 2021</title>
      <dc:creator>Irene de la Torre Arenas</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2021-07-18-wonderful-wednesdays-june-2021</link>
      <description>Vasculitis is an inflammation of the blood vessels, in many cases caused by
the body’s immune system attacking healthy blood vessels, causing them to
become swollen and narrow. The symptoms associated with vasculitis are varied,
and include asthma, allergic rhinitis (cold-like symptoms), fever, joint pain,
tiredness, loss of appetite and weight loss. In serious cases, if left
untreated, severe vasculitis can lead to organ failure and death. Vasculitis
is treated primarily with oral corticosteroid (OCS) treatment. However
long-term usage of OCS therapy at high doses is associated with a number of
side-effects which worsen over time, and steroid-dependency is often a
concern, so steroid doses need to be reduced if possible. However, some
patients have a propensity to relapse, in which the patient experiences an
acute flare-up of symptoms, usually requiring immediate treatment with high
dose OCS treatment. Patients are considered to be in remission if the symptom
score is zero concurrently with the OCS dose being ≤ 7.5 mg/day.</description>
      <category>Vasculitis</category>
      <category>Rare disease</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2021-07-18-wonderful-wednesdays-june-2021</guid>
      <pubDate>Wed, 09 Jun 2021 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2021-07-18-wonderful-wednesdays-june-2021/./images/remission-tte - Mark Baillie.png" medium="image" type="image/png" width="4798" height="2337"/>
    </item>
    <item>
      <title>Wonderful Wednesdays May 2021</title>
      <dc:creator>Lorenz Uhlmann</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2021-05-09-wonderful-wednesdays-may-2021</link>
      <description>Psoriasis is a chronic, inflammatory skin disease. For such diseases, it is of interest to not only compare treatments in terms of patients achieving a response, but also in terms of patients sustaining this response once it is achieved. This is the focus of this month’s challenge: produce a visualization to compare treatments with regards to patients sustaining a response. The synthetic data contains PASI (Psoriasis Area and Severity Index) scores for three treatment groups: two receive an active treatment in different doses; the third receives a comparator. The three arms are balanced, with 300 patients in each. The data contains PASI scores at Baseline and eight follow-up visits, spread over the course of a year. PASI scores can range from 0 to 72, with higher scores indicating a worse condition state. The criteriafor response can be chosen, but typical thresholds include a 75%, 90% or 100% reduction in PASI scores relative to Baseline (PASI75/PASI90/PASI100). More than one threshold could also be considered in the same visualization.</description>
      <category>Psoriasis</category>
      <category>Sustained response</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2021-05-09-wonderful-wednesdays-may-2021</guid>
      <pubDate>Wed, 12 May 2021 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2021-05-09-wonderful-wednesdays-may-2021/./images/WWW_May_RhysWarham - Rhys Warham.png" medium="image" type="image/png" width="787" height="979"/>
    </item>
    <item>
      <title>Wonderful Wednesdays April 2021</title>
      <dc:creator>Lovemore Gakava</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2021-04-13-wonderful-wednesdays-april-2021</link>
      <description>COPD is a disease that effects adults typically aged 40 years or older. It is a chronic condition that effects the lungs of a patient. The intervention in this study was an self-management app that aimed to improve inhaler use and exercise capacity in individuals with COPD. Users of the intervention could access different videos to educate themselves around different topics to better understand COPD and what steps help improve their condition, such as exercise or smoking cessation. Users could also report other detail such as their medication use, their CAT score and even a daily symptom score on how they are feeling that day. All this information could be shared with their doctor who can then monitor and manage patients to ensure they get the right treatment for them.</description>
      <category>Mobile app data</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2021-04-13-wonderful-wednesdays-april-2021</guid>
      <pubDate>Wed, 14 Apr 2021 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2021-04-13-wonderful-wednesdays-april-2021/./images/ITA_time_scores - Irene De la Torre Arenas.png" medium="image" type="image/png" width="2081" height="1165"/>
    </item>
    <item>
      <title>Wonderful Wednesdays March 2021</title>
      <dc:creator>Steve Mallett</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2021-03-08-wonderful-wednesdays-march-2021</link>
      <description>The current example data set is focusing on missing data. Missing data are present in almost any (clinical) data set. Ways to visualize different patterns of missing values is the topic of this webinar / blog entry.</description>
      <category>Missing values</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2021-03-08-wonderful-wednesdays-march-2021</guid>
      <pubDate>Wed, 10 Mar 2021 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2021-03-08-wonderful-wednesdays-march-2021/./images/ch_missing_data - Agustin Calatroni.png" medium="image" type="image/png" width="1206" height="978"/>
    </item>
    <item>
      <title>Wonderful Wednesdays February 2021</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2021-02-07-wonderful-wednesdays-february-2021</link>
      <description>The DLQI is the most frequently used method for evaluating quality of life for patients with different skin conditions. There are 10 questions, covering the following topics: symptoms, embarrassment, shopping and home care, clothes, social and leisure, sport, work or study, close relationships, sex, treatment. Each question refers to the impact of the skin disease over the previous week and is scored from 0 to 3, giving a possible score range from 0 (meaning no impact of skin disease on quality of life) to 30 (meaning maximum impact on quality of life). This month's challenge was to use an effective visualisation to show the effect of treatment on DLQI scores across time points, possibly also reflecting the DLQI's multidimensional nature.</description>
      <category>DLQI</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2021-02-07-wonderful-wednesdays-february-2021</guid>
      <pubDate>Wed, 10 Feb 2021 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2021-02-07-wonderful-wednesdays-february-2021/./images/hysto2c.png" medium="image" type="image/png" width="688" height="449"/>
    </item>
    <item>
      <title>Wonderful Wednesdays January 2021</title>
      <dc:creator>PSI VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2021-01-11-wonderful-wednesdays-january-2021</link>
      <description>The data set is about a retrospecitve study on finding "Predictors of Residual Tumor in Breast-Conserving Therapy". There are 500 subjects included. The outcome variable is reexcision and it describes, if there has been a reexcision necessary after the (initial) surgery. In a first step, a prediction model for reexcission needed to be set up. The goal of this exercise was to visualize the results. The audience was intended to be a "non-technical" one.</description>
      <category>Prediction</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2021-01-11-wonderful-wednesdays-january-2021</guid>
      <pubDate>Wed, 13 Jan 2021 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2021-01-11-wonderful-wednesdays-january-2021/./images/viz_ms_edited - Agustin Calatroni.png" medium="image" type="image/png" width="1011" height="906"/>
    </item>
    <item>
      <title>Festive Reindeer Plots</title>
      <dc:creator>Steve Mallett</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2020-12-21-festive-reindeer-plots</link>
      <description>In this entry, we do a little festive fun. We hope you will enjoy it!</description>
      <category>Christmas</category>
      <category>Rudolph</category>
      <guid>https://graphicsprinciples.github.io/posts/2020-12-21-festive-reindeer-plots</guid>
      <pubDate>Mon, 21 Dec 2020 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2020-12-21-festive-reindeer-plots/./images/R_example04.png" medium="image" type="image/png" width="3600" height="2400"/>
    </item>
    <item>
      <title>Wonderful Wednesdays December 2020</title>
      <dc:creator>Mark Baillie</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2020-12-03-wonderful-wednesdays-december-2020</link>
      <description>Mark Baillie guides us through the submissions for the meta-analysis data set. The purpose of the challenge was to explore how data visualisation can be deployed to find insights when faced with big data. Big data in the pharma setting typically means wide data i.e. more variables than observations. We went through a number of interesting submissions from a shiny application of a forest plot, an interactive application to explore relationships between variables, an application that provides a workflow for developing a prognostic model, through to exploratory plots using small multiple ridge and scatter plots. A lesson from the challenge is to always visualise your data to avoid surprises.</description>
      <category>Meta-analysis</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2020-12-03-wonderful-wednesdays-december-2020</guid>
      <pubDate>Wed, 09 Dec 2020 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2020-12-03-wonderful-wednesdays-december-2020/./images/forest_plot_with_bands - Waseem Medhat.png" medium="image" type="image/png" width="872" height="545"/>
    </item>
    <item>
      <title>Wonderful Wednesdays November 2020</title>
      <dc:creator>Zachary Skrivanek</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2020-11-10-wonderful-wednesdays-november-2020</link>
      <description>Zachary Skrivanek guides through a number of data visualisations explaining a mediated treatment effect on patient reported quality of life. In addition the problem of missing data should be handled within the graphical representation. A Lollipop plot and a bar chart were presented as well as multiplot solutions using correlation plots or a combination of scatter plots, modelling plots and distribution plots. A point of discussion was the usability of a parallel coordinates plot. Another proposal used a Bayesian model displaying the results in an impressive grable – a combination of graphic and table. The last approach used an innovative way of storytelling with data called scrollytelling.</description>
      <category>Mediation analysis</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2020-11-10-wonderful-wednesdays-november-2020</guid>
      <pubDate>Wed, 11 Nov 2020 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2020-11-10-wonderful-wednesdays-november-2020/./images/DLQI_mediation_mallett2 - Steve Mallett.png" medium="image" type="image/png" width="3600" height="3600"/>
    </item>
    <item>
      <title>Wonderful Wednesdays October 2020</title>
      <dc:creator>Steve Mallett</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2020-10-21-wonderful-wednesdays-october-2020</link>
      <description>Abi Williams presented data visualisations on co-occurrence of adverse events. How to display events that occur at the same time in the same patient and highlight differences of treatment or gender? A variety of very different approaches was discussed. The first visualisation was a heatmap for the frequency of co-occurrences. A Lollipop plot combined the frequency with the overlap time. Then a Shiny App made interactive exploration of up to 4 co-occurring events possible. The presentation of an UpSet plot brought up a discussion on the advantages over the vinn diagram. The shiny app AdEPro was referred to as an useful exploration tool for adverse event in general. Another tool for exploring the co-occurrence in particular was the force directed network graph. The final approach was embedded in a PowerBI Dashboard.</description>
      <category>Adverse events</category>
      <category>Co-occurence</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2020-10-21-wonderful-wednesdays-october-2020</guid>
      <pubDate>Wed, 14 Oct 2020 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2020-10-21-wonderful-wednesdays-october-2020/./images/plot01.png" medium="image" type="image/png" width="4133" height="2362"/>
    </item>
    <item>
      <title>Wonderful Wednesdays September 2020</title>
      <dc:creator>Lorenz Uhlmann</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2020-10-17-wonderful-wednesdays-september-2020</link>
      <description>How to display safety data? This month's challenge has shown there are very different ways to visualize adverse event data. Although the example data set was from a two-arm study and relatively simple, the display of type of AE, frequency, timing, severity and seriousness is not easily combined in one plot.</description>
      <category>Adverse events</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2020-10-17-wonderful-wednesdays-september-2020</guid>
      <pubDate>Thu, 10 Sep 2020 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2020-10-17-wonderful-wednesdays-september-2020/./images/volcano BS Phillips.png" medium="image" type="image/png" width="990" height="720"/>
    </item>
    <item>
      <title>Wonderful Wednesdays August 2020</title>
      <dc:creator>Lorenz Uhlmann</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2020-10-30-wonderful-wednesdays-august-2020</link>
      <description>In this month’s webinar, we discussed an exacerbation example data set. The data is based on the RISE study for patients with moderate COPD. The primary endpoint is the number of exacerbations during a six month treatment period. Event data – but patients can (and do) have multiple exacerbations. Statistical analysis used a Negative Binomial model. The dataset also included other variables which (may) effect the exacerbation rate: % Predicted Normal of Forced Expiratory Volume in 1 second (FEV1), Exacerbations in previous year, and Region / Gender. The challenge was to produce a data visualisation incorporating the information on the number of exacerbations observed. The discussed visualisations included a straightforward and clear presentation of the number of exacerbations, a Power BI app, longitudinal plots, and a display of patient-level outcomes.</description>
      <category>Event data</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2020-10-30-wonderful-wednesdays-august-2020</guid>
      <pubDate>Wed, 12 Aug 2020 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2020-10-30-wonderful-wednesdays-august-2020/./images/dotplot - Markus Vogler.png" medium="image" type="image/png" width="4133" height="2362"/>
    </item>
    <item>
      <title>Wonderful Wednesdays July 2020</title>
      <dc:creator>Steve Mallett</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2020-09-20-wonderful-wednesdays-july-2020</link>
      <description>This month's challenge was to summarise changes in haemoglobin (Hgb) concentration over time in patients with anaemia associated with Chronic Kidney Disease (CKD). An experimental medicine was compared with a control group, but in addition to demonstrating efficacy, there was a special interest in visualising intra-individual variability, as large changes in Hgb are a potential safety concern. The visualisations presented ranged from grouped line plots over time, Sankey-diagrams and line plots with quantile bands for summary views and a lasagna plot for the display of individual data. Some general hints were given on how to define suitable color palettes for graphs depending on the type of data. Presented by Steve Mallett.</description>
      <category>Longitudinal</category>
      <category>Continuous data</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2020-09-20-wonderful-wednesdays-july-2020</guid>
      <pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2020-09-20-wonderful-wednesdays-july-2020/./images/June_PSI_AW_v1_0.png" medium="image" type="image/png" width="1268" height="507"/>
    </item>
    <item>
      <title>Wonderful Wednesdays June 2020</title>
      <dc:creator>Markus Vogler</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2020-09-11-wonderful-wednesdays-june-2020</link>
      <description>This month the discussion is about visualizations of the satisfaction data using cumulative distribution functions, a tree plot, staggered bar plots, a power BI dashboard and two interactive funnel plots using plotly and RShiny. These examples nicely demonstrate that different displays support different purposes. Some visualizations are telling a story about the data and others help to explore the data. Presented by Bodo Kirsch.</description>
      <category>Wonderful Wednesdays</category>
      <category>Subgroups</category>
      <category>Discrete endpoint</category>
      <guid>https://graphicsprinciples.github.io/posts/2020-09-11-wonderful-wednesdays-june-2020</guid>
      <pubDate>Wed, 10 Jun 2020 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2020-09-11-wonderful-wednesdays-june-2020/./plots/cdf.png" medium="image" type="image/png" width="4133" height="2362"/>
    </item>
    <item>
      <title>Wonderful Wednesday April 2020</title>
      <dc:creator>Rachel Phillips, Steve Mallett</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2020-09-10-wonderful-wednesday-april-2020</link>
      <description>In this webinar we discuss the first submissions looking at some great ways to visualise a categorical response variable over time. We talk about what the SIG members like about each plot and give some pointers on areas we think could be improved. More specifically, we talk about: the value of a title with conclusions, the use of colour, the benefits of animation to display time, ways to declutter your graph and examples of clean design, enclosure to highlight important parts of the data, the benefits of the sankey diagram, how different charts help to answer different questions, and many more aspects of great visualizations.</description>
      <category>survival data</category>
      <category>Kaplan-Meier</category>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2020-09-10-wonderful-wednesday-april-2020</guid>
      <pubDate>Thu, 09 Apr 2020 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2020-09-10-wonderful-wednesday-april-2020/plots/plot01.png" medium="image" type="image/png" width="5790" height="2700"/>
    </item>
    <item>
      <title>Wonderful Wednesdays March 2020</title>
      <dc:creator>Daniel Saure</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2020-10-27-wonderful-wednesdays-march-2020</link>
      <description>The VIS SIG is happy to introduce a new initiative called “Wonderful Wednesdays”. This initiative will provide the participants to not only learn theoretically about visualisation principles but also apply them to relevant examples from the field of healthcare and the development of new therapies. This webinar kicks off the webinar series by giving an introduction into principles of visualization and an overview of the process. The first dataset is also introduced.</description>
      <category>Wonderful Wednesdays</category>
      <guid>https://graphicsprinciples.github.io/posts/2020-10-27-wonderful-wednesdays-march-2020</guid>
      <pubDate>Wed, 11 Mar 2020 00:00:00 +0000</pubDate>
      <media:content url="https://graphicsprinciples.github.io/posts/2020-10-27-wonderful-wednesdays-march-2020/./images/ww1.gif" medium="image" type="image/gif"/>
    </item>
    <item>
      <title>Wonderful Wednesdays Overview</title>
      <dc:creator>VIS SIG</dc:creator>
      <link>https://graphicsprinciples.github.io/posts/2020-09-09-wonderful-wednesdays-overview</link>
      <description>This provides a short overview of our Wonderful Wednesdays Webinars.</description>
      <category>Wonderful Wednesdays</category>
      <category>Overview</category>
      <guid>https://graphicsprinciples.github.io/posts/2020-09-09-wonderful-wednesdays-overview</guid>
      <pubDate>Wed, 04 Mar 2020 00:00:00 +0000</pubDate>
    </item>
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