Data: Presentation and Description Peter T. Donnan Professor of Epidemiology and Biostatistics Statistics for Health Research

Overview What is Data? What is Data? Summarising data Summarising data Displaying data Displaying data SPSS SPSS

Why have you collected data? Most important question! Most important question! Related to testing hypotheses Related to testing hypotheses If you have not got any hypotheses – Get some! If you have not got any hypotheses – Get some! Return to later Return to later

DATA – Where from? All data is a Sample – a subset of population All data is a Sample – a subset of population How was it collected? How was it collected? Potential for bias? Potential for bias?

Extrapolating from the sample to population Illustrations Ian Christie, Orthopaedic & Trauma Surgery, Copyright 2002 University of Dundee

Quantitative Data? Observation or measurement of one or more variables Observation or measurement of one or more variables Variable is any quantity measured on a scale Variable is any quantity measured on a scale Unit of analysis can be person, group (e.g. practice), specimen, time Unit of analysis can be person, group (e.g. practice), specimen, time Multilevel – patient and practice Multilevel – patient and practice

Cross-classified 3 level multilevel model Practice level j Patient level i Hospital k

Statistics Statistics encompasses - 1. Design of study; 2. Methods of collecting, and summarising data; 3. Analysing and drawing appropriate conclusions from data

Variable types Categorical (qualitative) Categorical (qualitative) –E.g. type of drug, eye colour, smoker Numerical (quantitative) Numerical (quantitative) –E.g. age, birth weight, BP

Categorical Nominal Nominal Categories are mutually exclusive and unordered Eg Blood group type (A/B/AB/O) Ordinal Ordinal Categories are mutually exclusive and ordered Eg Disease stage (mild/moderate/ severe) Binary - two categories (yes, no)

Numerical Discrete Integer values, often counts Eg number of cigarettes smoked Continuous Continuous Takes any value in a range of values Eg Height in cm, cholesterol

Organisation of data Generally each variable in separate columns and one row per subject SubjectAgeGenderScore

1 st step in analysis? Look at the data!

Display and summarise data To get a feel for the data To get a feel for the data To spot errors and missing data To spot errors and missing data Assess the range of values Assess the range of values Also.. Also..

Caregorical data 1. Campylobactor21. Giardia 2. Campylobactor22. Crytosporidium 3. Escherichia coli Crytosporidium 4. Shigella sonnei24. Campylobactor 5. Crytosporidium25. Shigella sonnei 6. Giardia26. SRSV 7. Crytosporidium27. Crytosporidium 8. Campylobactor28. Campylobactor 9. Campylobactor29. Giardia 10. Crytosporidium30. Giardia 11. Giardia31. Escherichia coli Shigella sonnei32. Shigella sonnei 13. SRSV33. Crytosporidium 14. Giardia34. SRSV 15. Escherichia coli Campylobactor 16. Campylobactor36. Campylobactor 17. Giardia37. Campylobactor 18. SRSV38. Giardia 19. Campylobactor39. Escherichia coli Crytosporidium40. Campylobactor

Infection N (%) Campylobactor 12 (30.0) Cryptosporidium 9 (22.5) Giardia 8 (20.0) SRSV 5 (12.5) Escherichia coli (7.5) Shigella Total Total 40 (100) Summarised by frequencies or percentage

Numerical data Frequency distributions for continuous variable unfeasibly large Frequency distributions for continuous variable unfeasibly large Grouping may be necessary for presentation Grouping may be necessary for presentation

Age group (years)Frequency Relative Frequency (%) Cumulative relative frequency (%) Frequency distribution for continuous variable

Baseline measureN (%) (3.1) (3.0) (2.9) (3.9) (3.6) (4.8) (5.2) (5.9) (5.6) (5.0) (4.1) (3.9) (4.7) (4.4) (4.5) (4.5) (4.2) (3.6)

Baseline groupN (%) 4.0 to (16.5) 4.5 to (26.5) 5.0 to (21.6) 5.5 to (20.3) 6.0 to (15.1) Total Total 1677

Guide for grouping data Obtain min and max values Obtain min and max values Choose between 5 and 15 intervals Choose between 5 and 15 intervals Summarise but not obscure data especially continuous data Summarise but not obscure data especially continuous data Intervals of equal width Intervals of equal width – Good but not essential – Remember to label tables!

Take care with missing values SPSS gives % missing in output if missing left blank in data SPSS gives % missing in output if missing left blank in data Careful in reporting % as percentage of observed values or percentage of all subjects Careful in reporting % as percentage of observed values or percentage of all subjects These will differ! These will differ! Can use missing code (often 9) to make missing explicit in output Can use missing code (often 9) to make missing explicit in output

Graphs Simplicity Simplicity Consistency Consistency Not duplicating tables or text Not duplicating tables or text Remember Title Remember Title Remember Label axes Remember Label axes

Graphs – Categorical data Bar charts Bar charts Pie charts Pie charts

Bar charts Used to display categorical (or discrete numerical data) Used to display categorical (or discrete numerical data) One bar per category One bar per category Height of bar equals its frequency Height of bar equals its frequency Each bar same width and equally spaced Each bar same width and equally spaced Space between each bar Space between each bar Vertical axis must start at zero Vertical axis must start at zero

Most common cancer deaths in UK, 2009 Plots and Statistics from CRUK website

Pie charts Displays one variable only Displays one variable only Compare 2 groups using 2 charts Compare 2 groups using 2 charts

But avoid 3-dimensional plots!

Graphs – Numerical data Histograms Histograms Frequency polygon Frequency polygon Cumulative frequency polygon Cumulative frequency polygon Scatter plots Scatter plots Box plots Box plots

Histograms Like bar charts but no spaces Like bar charts but no spaces y axis always begins at zero y axis always begins at zero Area of bar represents the frequency in each group Area of bar represents the frequency in each group

Check data carefully

Florence Nightingale’s ‘Coxcomb’ diagram of Mortality in the Crimea War

Summary measures – Numerical data Central Location (average) Central Location (average) Spread or variability (distance of each data point from the average) Spread or variability (distance of each data point from the average)

Central Location Mean Mean Median Median Mode - most frequent value Mode - most frequent value

Mean _ x = x 1 + x 2 +x 3 + ….. + x n N Often written as ∑x i / N Where Sigma or ∑ is ‘Sum of’

_ x = = 3 litres

Mean Advantages Advantages – Uses all data values – Very amenable to statistical analysis; most models deal with mean Disadvantages (advantages to politicians and estate agents!) Disadvantages (advantages to politicians and estate agents!) – Distorted by outliers – Distorted by skewed data

Median Arrange values in increasing order Median is the middle value [ ] Median = = 2.96 litres 2

Median

Median Advantages Advantages – Not distorted by outliers – Not distorted by skewed data Disadvantages Disadvantages – Ignores most of the information – Less amenable to statistical modelling

Measures of spread [47 52] Mean = 51.3 Median = [51 51] Mean = 51.3 Median = 51

Range [47 52] Range or [51 51] Range or 5

Range from percentiles Data ordered from smallest to largest value Data ordered from smallest to largest value Percentiles Percentiles Deciles –data in equal 10ths Deciles –data in equal 10ths Quartiles = data in equal 4ths Quartiles = data in equal 4ths

Interquartile range (IQR) | | | (lower quartile) 32.5 (upper quartile) Interquartile range (IQR) = = 24.5

Median Range IQR Multiple Box-plots Upper Quartile Lower Quartile Outlier

Distribution of data values around the mean MEAN MEAN

Variance mean=34.16 years _ (x-x) – – – – –

Variance mean=34.16 _ _ (x-x) (x-x)

Variance (s 2 ) _ S 2 =  (x-x) 2 n-1 S 2 = S 2 =182.16

Mean = years Variance = 182.2

Standard deviation (s) Standard deviation (s) _ Std deviation (s) = √  (x-x) 2 n-1 Std deviation = √ = 13.49

Mean = years SD = Coefficient of Variation (CV) = SD / Mean = 0.39 Measure of variability for comparison of different scales

What central measure goes with what measure of spread? Mean (SD) Mean (SD) Median (IQR) Median (IQR)

Summary Summary Do not underestimate value of looking at the data Do not underestimate value of looking at the data Gives a feel for the data before testing or modelling Gives a feel for the data before testing or modelling Check for missing data Check for missing data Check for outliers Check for outliers

From Jan 2010 IBM acquired copyright for SPSS

Implementing Kaplan-Meier in SPSS From Colorectal.sav you need to specify: Survival time – time from surgery (tfsurg) Survival time – time from surgery (tfsurg) Status – Dead = 1, censored = 0 (dead) Status – Dead = 1, censored = 0 (dead) Factor – e.g. hypertension comorbidity (hyperco) Factor – e.g. hypertension comorbidity (hyperco) Select plot of survival Select plot of survival

Implementing Kaplan-Meier plot in SPSS

Select options to obtain plot and median survival

Survival curves for women with glioma by diagnosis. Bland J M, Altman D G BMJ 2004;328:1073

Practical Read LDL.sav or colorectal.sav into SPSS (19) and explore the different types of data using appropriate tables and graphs Data available at MyDundee y _4-t_AueCBgz2 or DEBU website ( and-biostatistics-unit-debu)