Evidence Based Medicine
Statistics
An FY2 in ED is collecting retrospective data for an audit on headache. He records the number of patients with headaches seen per day and for each patient who presented to ED with a headache he collects data on the length of time waiting to see the triage nurse, the pain score at triage (Faces Pain Rating Scale) and pain relief given in the ED (e.g. simple non-opioid analgesia, opioid analgesia and nil).
The pain-scoring system given is an example of which of the following types of data:
Answer:
A pain scoring system is an example of ordinal categorical data.Types of Data
Evidence Based Medicine / Statistics
Last Updated: 21st April 2019
The purpose of most studies is to collect data to obtain information about a particular area of research. Data comprises observations on one or more variables e.g. gender, height. The most appropriate statistical method used to analyse data is dependent on the type of data collected.
Data may be categorical or quantitative (numerical). In general it is easier to summarise categorical variables, and so quantitative variables are often converted to categorical ones for descriptive purposes. However, categorising a continuous variable reduces the amount of information available, and statistical tests will in general be more sensitive (have more power) for a quantitative variable than the corresponding categorical one. Categorising data is therefore useful for summarising results, but not for statistical analysis.
Categorical Data
Categorical data occurs when each individual can only belong to one of a number of distinct categories of the variable.
Categorical data may be nominal or ordinal.
- Ordinal data is data that is ordered in some way e.g. disease staging system, pain scoring system.
- Nominal data is data where the categories have names but are not ordered in any way e.g. blood group, marital status, gender.
A categorical variable is binary or dichotomous when there are only two categories e.g. yes/no, male/female.
Quantitative Data
Quantitative data occurs when the variable takes some numerical value.
Quantitative data may be discrete or continuous.
- Discrete data is counted data that can only take whole numerical values e.g. number of children, number of days missed from work per year.
- Continuous data is measured data where there is no limitation on the value that the variable can take e.g. weight, height.
Age is often treated as discrete data (taken as age at last birthday) but is technically continuous.
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- Biochemistry
- Blood Gases
- Haematology
| Biochemistry | Normal Value |
|---|---|
| Sodium | 135 – 145 mmol/l |
| Potassium | 3.0 – 4.5 mmol/l |
| Urea | 2.5 – 7.5 mmol/l |
| Glucose | 3.5 – 5.0 mmol/l |
| Creatinine | 35 – 135 μmol/l |
| Alanine Aminotransferase (ALT) | 5 – 35 U/l |
| Gamma-glutamyl Transferase (GGT) | < 65 U/l |
| Alkaline Phosphatase (ALP) | 30 – 135 U/l |
| Aspartate Aminotransferase (AST) | < 40 U/l |
| Total Protein | 60 – 80 g/l |
| Albumin | 35 – 50 g/l |
| Globulin | 2.4 – 3.5 g/dl |
| Amylase | < 70 U/l |
| Total Bilirubin | 3 – 17 μmol/l |
| Calcium | 2.1 – 2.5 mmol/l |
| Chloride | 95 – 105 mmol/l |
| Phosphate | 0.8 – 1.4 mmol/l |
| Haematology | Normal Value |
|---|---|
| Haemoglobin | 11.5 – 16.6 g/dl |
| White Blood Cells | 4.0 – 11.0 x 109/l |
| Platelets | 150 – 450 x 109/l |
| MCV | 80 – 96 fl |
| MCHC | 32 – 36 g/dl |
| Neutrophils | 2.0 – 7.5 x 109/l |
| Lymphocytes | 1.5 – 4.0 x 109/l |
| Monocytes | 0.3 – 1.0 x 109/l |
| Eosinophils | 0.1 – 0.5 x 109/l |
| Basophils | < 0.2 x 109/l |
| Reticulocytes | < 2% |
| Haematocrit | 0.35 – 0.49 |
| Red Cell Distribution Width | 11 – 15% |
| Blood Gases | Normal Value |
|---|---|
| pH | 7.35 – 7.45 |
| pO2 | 11 – 14 kPa |
| pCO2 | 4.5 – 6.0 kPa |
| Base Excess | -2 – +2 mmol/l |
| Bicarbonate | 24 – 30 mmol/l |
| Lactate | < 2 mmol/l |
