ROBINS-I V2 (2025): Explicit Cut-off Lines for Risk-of-Bias Judgement

Link to ROBIN-I V2 Website A Domain-by-Domain Operational Guide

Core Principle of ROBINS-I V2

ROBINS-I V2 is anchored on the identification of material bias:

Material bias = bias that is large enough to meaningfully distort the estimated effect or invalidate causal interpretation.

All cut-offs below are therefore defined by impact on causal interpretability, not by the mere presence of bias.

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Domain 1 — Bias Due to Confounding

Low Risk (Low except for residual confounding)

Cut-off line

• All important confounders are identified, measured with acceptable validity, and appropriately controlled using suitable methods (e.g., regression, matching, weighting).
• Any remaining confounding is plausibly too small to materially alter the effect estimate.

Note: In non-randomized studies, “pure” Low is rarely achievable; hence Low except for residual confounding.

Moderate Risk

Cut-off line

• Most important confounders are controlled.
• Any uncontrolled confounding is unlikely to be substantial or to change the direction or magnitude of the effect in a meaningful way.
• Supporting evidence (e.g., sensitivity analyses, negative controls) suggests robustness.

Serious Risk

Cut-off line

• ≥1 important confounder that should have been controlled was not controlled, and
• There is strong a priori or empirical rationale that this failure has a material impact on the estimated effect.

Key discriminator: Bias likely changes the size of the effect, but the effect is still interpretable with caution.

Critical Risk

Cut-off line

• Confounding is so severe that the observed comparison cannot be interpreted as a causal contrast, or
• The study effectively compares groups defined by prognostic severity or indication that directly determines the outcome, without adequate control, or
• Preliminary ROBINS-I V2 screening question B2 = “Yes” (confounding too severe to proceed).

At this level, results should not be synthesized as evidence of effect.

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Domain 2 — Bias in Classification of Interventions

Low Risk

Cut-off line

• Intervention groups are clearly distinguishable at the start of follow-up.
• Classification is independent of outcome information.

Moderate Risk

Cut-off line

• Some misclassification exists but is non-differential and unlikely to materially bias the effect estimate.

Serious Risk

Cut-off line

• Differential misclassification related to outcome occurrence, timing, or prognosis.
• Examples include exposure defined using post-baseline information (e.g., immortal time not addressed).

Critical Risk

Cut-off line

• Misclassification is so severe that the intended intervention contrast no longer exists in practice.
• Groups do not represent the interventions as defined.

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Domain 3 — Bias in Selection of Participants

Low Risk

Cut-off line

• Participants are included from the start of intervention or exposure.
• No selection based on post-intervention variables.

Moderate Risk

Cut-off line

• Some post-intervention selection occurs, but sensitivity analyses indicate minimal impact.

Serious Risk

Cut-off line

• Selection into analysis depends on post-intervention variables associated with both intervention and outcome.
• Typical example: inappropriate per-protocol or responder-only analyses.

Critical Risk

Cut-off line

• Selection produces irreversible structural bias (e.g., survivor-only cohorts, missing entire exposure periods).
• Bias cannot be corrected analytically.

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Domain 4 — Bias Due to Missing Data

Low Risk

Cut-off line

• Outcome data are nearly complete (>90–95%), or
• Missingness is plausibly MCAR/MAR and handled with appropriate methods (e.g., multiple imputation).

Moderate Risk

Cut-off line

• Missing data exist, but sensitivity analyses show no meaningful change in effect estimates.

Serious Risk

Cut-off line

• Missingness is plausibly MNAR and related to the true outcome value.
• Analysis relies primarily on complete-case methods without correction.

Critical Risk

Cut-off line

• Large amounts of outcome data are missing and strongly outcome-dependent, such that the direction or existence of the effect is uncertain.

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Domain 5 — Bias in Measurement of Outcomes

Low Risk

Cut-off line

• Outcomes are objective or
• Outcome assessors are blinded and measurement methods are identical across groups.

Moderate Risk

Cut-off line

• Assessors are unblinded, but outcomes are minimally influenced by judgment.

Serious Risk

Cut-off line

• Outcomes are subjective and
• Assessment is unblinded, with plausible influence of knowledge of intervention on measurement.

Critical Risk

Cut-off line

• Outcome measurement methods differ fundamentally between groups or
• Measurement is so flawed that outcomes are not comparable.

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Domain 6 — Bias in Selection of the Reported Result

Low Risk

Cut-off line

• Results are reported according to a pre-specified protocol or SAP.

Moderate Risk

Cut-off line

• No pre-registration, but there is little or no analytical flexibility and no indication of selective reporting.

Serious Risk

Cut-off line

• Evidence of selective reporting from multiple analyses, scales, time points, or models without prespecification.

Critical Risk

Cut-off line

• Selective reporting is so extreme that conclusions would likely reverse if all analyses were reported.

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Overall Risk of Bias (Outcome-Specific)

Cut-off rules

• Low: All domains Low
• Moderate: ≥1 Moderate, none Serious/Critical
• Serious: ≥1 Serious, none Critical
• Critical: ≥1 Critical or multiple Serious domains rendering the effect non-credible

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Final Calibration Rule (Across All Domains)

Serious = biased but still interpretable with cautionCritical = causal interpretation no longer defensible

This distinction—not terminology—is the decisive cut-off line in ROBINS-I V2.