Effective Use of Graphs

Effective Use of Graphs

Annie HerbertMedical Statistician

Research & Development Support UnitSalford Royal (Hope) Hospitals NHS Foundation Trust

annie.herbert@manchester.ac.uk(0161 720) 2227

Timetable

Time Task

60 mins Presentation

20 mins Coffee Break

90 minsPractical Tasks in

IT Room

Outline• Graphs for categorical data

• Graphs for numerical data

• Comparing groups

• Additional graphs (covered in other courses)

• Final tips & Computer packages

Categorical Data (1)

Examples: • Sex

– Male/Female

• Blood Group

– A/B/AB/O

• Employment Status

– Unemployed/Part-time/Full-time

Categorical Data (2)

• Record: Frequency (discrete number) per category

• Summary: Frequency OR

percentage/fraction/proportion

• Visually:

- Bar Chart - Pie Chart

Official Employment Status of Population of Camberwick Green

0

500

1000

1500

2000

2500

Unemployed Part-time Full-time

Employment Type

Fre

qu

en

cy

Official Employment Status of Population of Camberwick Green

Unemployed

Part-time

Full-time

Example – Discharge Destination (1)

Where Patient Lives n = 731

Alone 339 (46.3%)

Family 210 (28.7%)

Home 180 (24.6%)

Other 2 (0.3%)

Example – Discharge Destination (2)

Discharge Destinations of Patients

050

100150200250300350400

Alone Family Home Other

Discharge Destination

Fre

qu

ency

Example – Psychiatric Illness/ Discharge Destination (1)

Psychiatric Illness?

WherePatientLives

Non=208

Yesn=523

Alone 117 (56%) 222 (42%)

Family 81 (39%) 129 (25%)

Home 9 (4%) 171 (33%)

Other 1 (0%) 1 (0%)

Example – Psychiatric Illness/ Discharge Destination (2)

Where Patient Lives

Psychiatric Illness? Alone (n=339) Family (n=210) Home (n=180) Other (n=2)

No 117 (35%) 81 (39%) 9 (5%) 1 (50%)

Yes 222 (65%) 129 (61%) 171 (95%) 1 (50%)

Example – Psychiatric Illness/ Discharge Destination Bar Chart

Discharge Destination of Patients with and without Psychiatric Illness

0

50

100

150

200

250

Alone Family Home Other

Discharge Destination

Fre

qu

ency

No

Yes

Stacked Bar ChartDischarge Destination of Patients with and without

Psychiatric Illness

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Alone Family Home Other

Discharge Destination

Per

cen

tag

e

Yes

No

Re-ordering categories can emphasize a certain effect:

Discharge Destination of Patients with and without Psychiatric Illness

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

No Yes

Psychiatric Illness?

Per

cen

tag

e Other

Home

Family

Alone

Discharge Destination of Patients with and without Psychiatric Illness

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

No Yes

Psychiatric Illness?

Per

cent

age

Family

Other

Home

Alone

The axis should always start from 0:

Discharge Destination (Alone, Family) for Patients with and without Psychiatric Illness

0

50

100

150

200

250

No Yes

Psychiatric Illness?

Fre

qu

ency

Alone

Family

Discharge Destination (Alone, Family) for Patients with and without Psychiatric Illness

70

90

110

130

150

170

190

210

230

No Yes

Psychiatric Illness?

Fre

qu

ency

Alone

Family

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.

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.

Pie Charts

Psychiatric Illness? No

Home

Alone

Family

Other

Psychiatric Illness? Yes

Home

Alone

Family

Other

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.

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.

Numerical Data (1)

Examples:

• Weight

• Blood Pressure

• Cholesterol Levels

Numerical Data (2)• Record: Number/Value

(discrete or continuous)

• Summary: - Mean (SD) - Median (IQR)

• Visually:- Histogram - Box plot - Spread plot

Data – Ages of Patients inSelenium Study

Age

48

36

56

66

65

19

36

59

48

52

67

39

28

58

48

49

39

57

62

74

59

66

45

69

55

63

42

68

54

24

19

70

73

29

34

50

Histogram – Ages of Patients inSelenium Study

Histograms for the same data can vary:

Compromise:

Beware!Histogram is not Bar Chart

Length of stay (days)

1901701501301109070503010

400

300

200

100

0

Length of stay (days)

>120

61to120

31to60

15to30

7to14

5to7

<5

Cou

nt

400

300

200

100

0

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.

Histograms – Disadvantages:

• Choice of interval width can alter appearance.

• Individual values lost.

• One data set per histogram, difficult to compare data sets.

Box Plot

Upper Quartile

Lower QuartileMedian

Extreme Outlier

Outlier

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:

Box Plots – Disadvantages:

• More complicated visually than some other types of data plots.

• Individual values lost.

Spread Plots (1)

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.

Relationships in Numerical Data

Serial Measurements

Mean TG (±standard error) at each time point

-100 150 400 6500

1

2

3

Mea

n T

G (

mM

)

Time (minutes)

Change of TG over time

-100 150 400 6500

1

2

3

TG

(m

M)

Time (minutes)

E 1.1

E 2.2

E 3.2

E 4.1

E 5.1

E 6.1

What information does this give?

Mean ± SE, n ≈ 30 per group

Better to look at individual data…

…or give a sensible summary.

Kaplan-Meier Curve (step graph)

Time-to-Event data.

Survival Plot (PL estimates)

0 100 200 3000.00

0.25

0.50

0.75

1.00

Surv

ivor

Times

1

0

Bland-Altman Plots (scatter plots)

How well do two methods of measurement agree?

Agreement Plot (95% limits of agreement)

200 250 300 350 400 450-100

-50

0

50

100

mean

diff

ere

nce

Forest Plots (Hi-Lo-Close charts)

Meta-Analysis.

Forest (meta-analysis) plot

0.2 0.5 1 2

Pooled 0.75 (0.50, 1.14)

KW 0.80 (0.47, 1.36)

MT 0.80 (0.60, 1.07)

SW 0.68 (0.45, 1.03)

AH 0.72 (0.48, 1.08)

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.

Using a Computer Package:

Package Advantages Disadvantages

SPSS Produces journal quality graphs

• Difficult to start with• Expensive

StatsDirect When copied and pasted, these graphs may be edited in Word

Difficult to draw bar/pie charts

Excel Easy to use for bar/pie charts

Not a statistics package