Diagnostic tests
Sensitivity
Ability to detect disease, true positives (if the patient has the disease, how likely is the test able to detect it)
True positives / true positives + false negatives
i.e., how many people who actually have the disease, did the test pick up
Specificity
Ability to rule out disease or confirm normality, true negatives
True negatives / true negatives + false positives
i.e., how many people without the disease did the test correctly identify as normal
Positive predictive value
If the test is positive, how likely is it true the patient has the disease
True positives / true positives + false positives
i.e., is the test is positive, how likely is it to be true
Negative predictive value
If the test is negative, how likely is it true the patient is disease-free
True negatives / true negatives + false negatives
Risk
Relative risk
Occurrence (risk) in exposed group / occurrence (risk) in comparison group
Absolute risk difference
Occurrence (risk) in exposed group – occurrence (risk) in comparison group
Number needed to treat
- Number of subjects who must be treated with exposure or intervention, to have one subject experience the outcome
- 1 / risk difference ARR (in absolute %)
- i.e., if absolute risk reduction is 2 %, NNT is 1 / 0.02 = 50
- if absolute risk reduction is 20 %, NNT is 1 / 0.2 = 5
Odds ratios
Odds ≠ risk or probability
Odds = probability of something happening / something not happening
Odds of 1:4 imply a 20% risk of something
Odds of 1:1 imply a 50 % risk of something
Odds ratio = ratio of the odds between two groups.
Not the same as relative risk due to above.
Incidence and prevalence
Incidence
Number of individuals who develop a disease or condition within a specified period of time
Incidence only includes new cases developing during the time analysed, and still identifies cases which die early or are cured
Prevalence
Proportion of individuals who have a disease or condition at a specified point in time
Prevalence includes all cases – pre-existing or newly diagnosed, but may miss cases who die rapidly or are cured before specific timeframes analysed
Accuracy and precision
Outcome measures
Intention to treat analysis
Includes all patients in analysis based on initial allocation (regardless of crossover, dropout)
More closely aligns with real world practice
Does not undermine randomisation process
Per protocol analysis
Includes only those patients who underwent the planned intervention correctly
May give over-optimistic results regarding the intervention
T tests
Differences in means / standard error of the differences in means
Standard error calculated from SD and number of subjects
Population must be normally distributed, continuous variable and same populations
P value halves for one sided test cf. two sided (two sided would be preferred)
Non parametric tests
For non ordinal / categorical / non continuous outcomes or non-normal populations
Chi-square test or Fisher’s exact test
Outcomes
Should be clearly defined, measurable, relevant and reliable
Surrogate endpoints should be interpreted with caution
Internal validity
Extent that a measure or test is correct for the group of patients evaluated
External validity
Ability of a measure or test to be applied in routine clinical practice – generalisability
Critical appraisal and bias
Bias – features of study design, conduct or analysis which result in estimates of effect which may be different to the true population effect
Selection bias
- Subjects studied are not representative of the eligible population
- e.g. volunteers likely to be better educated, surgical populations self-selected as fit and healthy, patients unlikely to be compliant are excluded, non-English speakers excluded,
Confounding bias
- A variable associated with the exposure factor influences the outcome
- e.g. surgeon not controlled for in surgical trials, older patients or those with advanced disease appear to do worse etc.
- Can be minimised with randomisation or stratification analysis
Measurement bias
- May result from errors in measurement of patient factors, or disease outcomes
- Examples include measurement of factors for selection criteria, patient compliance or exposure to treatment, lost to follow up, blinding of measurement of subjective outcomes