Method glossary
Method glossary
The terms that appear in our appraisals and verdicts, explained without jargon. Each entry says where the concept comes from, where in the work, and on what date we checked it at the source.
- 95% CI
95% confidence interval — the range where the true value probably lies.
- absolute risk
The real difference in risk between the groups (e.g. 2 in 100 vs 1 in 100).
- AMSTAR-2
Tool for appraising the quality of systematic reviews (16 items, 7 critical).
- Bradford Hill
A set of viewpoints (temporality, dose-response, consistency…) for weighing cause and effect in observational data.
- CKD
Chronic kidney disease.
- clinical outcome
Concrete event experienced by the patient (e.g. heart attack, death), not a laboratory marker.
- cohort
Study that follows a group over time to see who develops the outcome.
- comparator
The group or condition the intervention is compared against (e.g. placebo, another diet).
- CONSORT
Checklist for how to REPORT a trial — transparency of reporting, not quality.
- cross-sectional
Study that measures everything at a single moment — it shows association, not cause.
- effectiveness
Effect in the real world, with imperfect adherence and varied patients.
- efficacy
Effect measured under ideal, controlled conditions (inside the trial).
- eGFR
Estimated glomerular filtration rate — how much the kidneys filter, which is ONE of the kidney's functions.
- FFQ
Food frequency questionnaire: it estimates habitual diet from memory, and is prone to error.
- fixed effects
Meta-analysis model that assumes a single true effect common to the studies.
- GFR
Glomerular filtration rate — how much the kidneys filter the blood.
- GRADE
System that grades the certainty of the body of evidence, outcome by outcome.
- hard outcome
Event that matters to the patient (heart attack, death, fracture) — not an intermediate marker.
- healthy-user
Someone who takes up a habit seen as healthy tends to have other healthy habits alongside it — it confounds the analysis.
- HR
Hazard ratio — risk over time (>1 higher, <1 lower).
- intention-to-treat
Analyses each person in the group they were randomized to, even if they dropped out — it avoids inflating the effect.
- I²
Measures how much of the difference between studies is real (not chance): 0% = none; high = a lot.
- Mendelian randomization
Uses genetic variants as a 'natural lottery' to test cause and effect.
- meta-analysis
Statistically combines the results of several studies into a single estimate.
- narrative review
Opinion or context piece, without a systematic search or a reproducible appraisal of the studies.
- Newcastle-Ottawa
Scale for appraising the quality of cohort and case-control studies.
- NNT
Number of people who need to be treated to prevent one event — it turns the effect into something practical.
- normative leap
Jumping from weak evidence straight to 'change practice or guidelines' — a common overreach.
- OR
Odds ratio — how many times more (>1) or less (<1) likely the outcome is.
- p-value
How compatible the data would be if there were no real effect; it measures neither size nor importance.
- PICO
The outline of the question: Population, Intervention (or exposure), Comparison and Outcome.
- placebo
Inert treatment used as a comparison to isolate the real effect of the intervention.
- power
The study's ability to detect an effect that is there; a small sample has little power.
- prevalence ratio
Measure of association typical of cross-sectional studies (it compares the proportion who have the outcome).
- PRISMA
Checklist for how to report a systematic review.
- publication bias
Studies with a 'positive' result get published more, and faster, which distorts the whole.
- QUADAS-2
Tool for appraising diagnostic accuracy studies.
- random effects
Meta-analysis model that allows the effect to vary between studies.
- regression to the mean
Natural tendency of extreme values to come back closer to the average at the next measurement.
- relative risk
How many times the risk changes between the groups (e.g. 'it doubled') — it can look large while being little in absolute numbers.
- reverse causation
When it is the disease that changes the measured factor — and not the other way around.
- RoB 2
Standard tool for assessing risk of bias in randomized trials (5 domains).
- ROBINS-E
Tool for risk of bias in exposure studies (e.g. diet → outcome).
- ROBINS-I
Tool for risk of bias in intervention studies without randomization.
- scoping review
Maps what exists on a topic, without judging the quality of the studies.
- SMD
Standardized mean difference — effect size in standard deviations (0 = no difference).
- STROBE
Checklist for how to report observational studies.
- surrogate outcome
Intermediate marker (e.g. GFR, cholesterol), not the event that matters to the patient.
- systematic review
Searches and appraises, in a structured way, every study on one question.
- vote-counting
Counting how many studies fell on each side, without combining the effect sizes — a weak synthesis.