The standards your peer reviewers actually read.
Plain-English explainers for the frameworks, risk-of-bias instruments, evidence-rating systems, and statistical diagnostics that make a systematic review defensible. New entries publish weekly.
Evidence rating
How to translate a body of evidence into a single, structured rating of certainty.
- GRADE: rating the certainty of evidence
The five downgrade and three upgrade factors, the four certainty levels, common misuses, and how to apply GRADE per outcome.
- CINeMA: confidence in network meta-analysisComing
How CINeMA adapts GRADE to NMAs, and what to do when direct and indirect evidence disagree.
Quality assessment
Picking the methodologically appropriate risk-of-bias instrument for the study design at hand.
- RoB 2: the five domains explainedComing
The randomisation, deviations, missing-outcome, measurement, and selection-of-result domains, and what counts as some-concerns vs. high.
- ROBINS-I: assessing confounding in non-randomised studiesComing
When ROBINS-I is the right tool, the seven domains, and the role of pre-specified confounders.
- QUADAS-2: the four domains for diagnostic-accuracy reviewsComing
Patient selection, index test, reference standard, and flow/timing — the QUADAS-2 framework end-to-end.
- QUIPS: prognostic-factor study qualityComing
When to use QUIPS over RoB 2, the six domains, and the place of attrition in prognostic-factor reviews.
Frameworks
PICOS and its variants — the structures that turn a research question into an answerable, searchable, and analysable plan.
- PICOS vs. PCC vs. PIRD: choosing the frameworkComing
Intervention reviews use PICOS, scoping reviews use PCC, diagnostic-accuracy reviews use PIRD — and the framework drives every downstream choice.
- PRISMA 2020 and its extensionsComing
Where PRISMA-ScR, PRISMA-DTA, and PRISMA-NMA take over from the base 2020 checklist.
Statistics
The estimands and diagnostics that determine whether a meta-analysis can be trusted.
- Heterogeneity: I², τ², and when high heterogeneity mattersComing
Distinguishing statistical heterogeneity from clinical heterogeneity, and when subgroup analysis is the right answer.
- Effect measures: OR, RR, RD, SMD, and MDComing
Picking the right scale for binary, continuous, and time-to-event outcomes, with worked examples.
- Prediction intervals — the under-reported sibling of CIsComing
Why a 95% CI on a random-effects pooled estimate isn't the whole story, and what a prediction interval adds.