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Effective Use of Graphs

Effective Use of Graphs. Annie Herbert Medical Statistician Research & Development Support Unit Salford Royal (Hope) Hospitals NHS Foundation Trust annie.herbert@manchester.ac.uk (0161 720) 2227. Timetable. Outline. Graphs for categorical data Graphs for numerical data Comparing groups

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Effective Use of Graphs

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  1. Effective Use of Graphs Annie Herbert Medical Statistician Research & Development Support Unit Salford Royal (Hope) Hospitals NHS Foundation Trust annie.herbert@manchester.ac.uk (0161 720) 2227

  2. Timetable

  3. Outline • Graphs for categorical data • Graphs for numerical data • Comparing groups • Additional graphs (covered in other courses) • Final tips & Computer packages

  4. Categorical Data (1) Examples: • Sex – Male/Female • Blood Group – A/B/AB/O • Employment Status – Unemployed/Part-time/Full-time

  5. Categorical Data (2) • Record: Frequency (discrete number) per category • Summary: Frequency OR percentage/fraction/proportion • Visually: - Bar Chart - Pie Chart

  6. Example – Discharge Destination (1)

  7. Example – Discharge Destination (2)

  8. Example – Psychiatric Illness/ Discharge Destination (1)

  9. Example – Psychiatric Illness/ Discharge Destination (2)

  10. Example – Psychiatric Illness/ Discharge Destination Bar Chart

  11. Stacked Bar Chart

  12. Re-ordering categories can emphasize a certain effect:

  13. The axis should always start from 0:

  14. Bar Charts – Adv & Disadv • Advantages: - Visually strong. - Easy to compare between more than one dataset. • Disadvantages: • Categories can be ‘re-ordered’ to emphasize certain effects. • Misleading if not used for counts. • Misleading if y-axis not from 0.

  15. Bar Charts – Things to consider: • What group differences are you interested in? • Frequencies or percentages? If percentage, it’s down to you to specify the totals. • Is ‘Other’ a large frequency/percentage? • Consider the categories as un-ordered when using a stacked bar chart.

  16. Pie Charts

  17. Pie Charts – Advantages: • Easy to compare categories, are equidistant from each other. • Ordering of categories does not emphasize certain effects as badly as stacked bar charts do.

  18. Pie Charts –Disadvantages: • No choice between frequencies and percentages (down to you to specify totals). • Cannot put more than one data set into a pie chart. • Lose individual values of the data. • Limited space: if using more than 5 or 6 categories, chart can look complicated.

  19. Numerical Data (1) Examples: • Weight • Blood Pressure • Cholesterol Levels

  20. Numerical Data (2) • Record: Number/Value (discrete or continuous) • Summary: - Mean (SD) - Median (IQR) • Visually: - Histogram - Box plot - Spread plot

  21. Data – Ages of Patients inSelenium Study

  22. Histogram – Ages of Patients inSelenium Study

  23. Histograms for the same data can vary:

  24. Compromise:

  25. Beware!Histogram is not Bar Chart

  26. Histograms – Advantages: • Visual display of interval frequencies, easy to compare intervals. • Can give an idea of the distribution of the data, e.g. shape, typical value, spread.

  27. Histograms– Disadvantages: • Choice of interval width can alter appearance. • Individual values lost. • One data set per histogram, difficult to compare data sets.

  28. Box Plot Extreme Outlier Upper Quartile Median Lower Quartile Outlier

  29. Box Plots– Advantages: • Defines many summary statistics in one plot. • Defines ‘outliers’ explicitly. • Can have more than one data set in a plot, so easy to compare data sets:

  30. Box Plots– Disadvantages: • More complicated visually than some other types of data plots. • Individual values lost.

  31. Spread Plots (1)

  32. Spread Plots (2) • Advantages: • Can give an idea of the distribution of the data, e.g. shape, typical value, spread. • Shows individual values of the data. • Can show more than one dataset in a plot. • Disadvantages: • Not very widely used in journal publications. • Doesn’t explicitly summarise statistics or outliers as box plot does.

  33. Relationships in Numerical Data

  34. Serial Measurements

  35. What information does this give? Mean ± SE, n ≈ 30 per group

  36. Better to look at individual data…

  37. …or give a sensible summary.

  38. Kaplan-Meier Curve (step graph) Time-to-Event data.

  39. Bland-Altman Plots (scatter plots) How well do two methods of measurement agree?

  40. Forest Plots (Hi-Lo-Close charts) Meta-Analysis.

  41. Final Pointers: • Before plotting think about the type of data and what you would like to compare. • Show all data rather than summaries where possible. • Label axes clearly. Graph should ‘stand alone’. • Make sure when comparing groups that outcome on the same scale. • Make sure any colours used are sufficiently different from each other, and not red/green.

  42. Using a Computer Package:

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