nejm-statistics

name	nejm-statistics
description	Use to enforce NEJM's clinical statistical reporting — confidence intervals over bare P values, the pre-specified intention-to-treat primary analysis, multiplicity control for secondary endpoints, pre-specified subgroups with interaction tests, missing-data handling, and absolute risk with NNT alongside relative measures.

Clinical Statistics & Reporting (nejm-statistics)

When to trigger

• Results report P values without confidence intervals.
• The primary analysis population (ITT vs per-protocol) is unclear.
• Many secondary endpoints are tested with no multiplicity plan.
• Subgroup findings are highlighted without pre-specification or interaction tests.
• Only relative risk is reported, with no absolute risk or NNT.

Confidence intervals over bare P values

NEJM's statistical reporting guidance favors effect estimates with 95% confidence intervals over bare significance tests.

• Every primary and key secondary outcome: effect estimate + 95% CI (HR/RR/OR or mean difference).
• Report exact P values (e.g., P=0.03), not "P<0.05", except for very small values.
• A CI that includes the null is not "no difference" — report the estimate and its width; interpret precision.
• For observational data, use cautious, non-causal language (associated with, not causes).

The pre-specified primary analysis

• The primary outcome is single and pre-specified; its analysis follows the SAP.
• Intention-to-treat is the primary analysis population for superiority trials; per-protocol is a sensitivity analysis. (For non-inferiority, both ITT and per-protocol matter — report both.)
• State the analysis model (e.g., Cox, logistic, mixed model) and pre-specified covariates/stratification.

Multiplicity (the secondary-endpoint trap)

• Pre-specify a testing hierarchy or a multiplicity adjustment (e.g., gatekeeping, Hochberg, Bonferroni) for multiple primary/secondary endpoints.
• Endpoints not in the controlled testing scheme are exploratory — say so, and report estimates with CIs but no inferential P claims.
• Do not declare a secondary endpoint "significant" if it sits outside the multiplicity plan.

Subgroups (warn against over-interpretation)

• Subgroup analyses must be pre-specified; label post-hoc subgroups as exploratory.
• Test the interaction (treatment × subgroup), not just within-subgroup P values — a within-subgroup "significant" effect without a significant interaction is weak evidence.
• Present subgroups in a forest plot with interaction P values (see nejm-figures-tables).
• Do not let a subgroup result become the headline of an otherwise null trial.

Absolute risk and NNT

• Report absolute risk difference alongside relative measures — a large relative reduction on a rare outcome can be a tiny absolute one.
• Where clinically meaningful, give the number needed to treat (NNT) (or number needed to harm).
• For safety, report absolute event rates by arm, not relative comparisons alone.

Missing data, survival, and other essentials

• State the amount and pattern of missing data and the handling (e.g., multiple imputation); avoid naive last-observation-carried-forward as the primary approach.
• Time-to-event: Kaplan–Meier with numbers-at-risk; Cox model with the proportional-hazards assumption checked; report median follow-up.
• Pre-specify interim analyses / stopping boundaries; report them if a DSMB acted.
• For non-inferiority: state the pre-specified margin and its justification.

What the statistical reviewer probes

NEJM routinely assigns trials a dedicated statistical reviewer (see nejm-rebuttal). Pre-empt the line items:

• SAP concordance — every analysis traces to the pre-specified SAP; deviations listed and dated.
• Event counts behind every ratio — per-arm numerators and denominators for every reported estimate.
• CI width, not just position — an interval spanning trivial and decisive effects is inconclusive.
• Assumptions checked — proportional hazards verified; how stratification variables entered the model.
• Cross-location consistency — the same estimate, to the same decimal, in abstract, text, tables, and figures.

Worked micro-example — a subgroup claim (before → after)

• Before: "The benefit was significant in patients older than 65 years (P=0.04) but not younger (P=0.31), so older patients should receive the drug."
• After: "The hazard ratio was 0.68 (95% CI, 0.47 to 0.98) among patients older than 65 years and 0.89 (95% CI, 0.64 to 1.24) among younger patients (P=0.29 for interaction)."

The estimates, CIs, and interaction test replace significance chatter; the nonsignificant interaction disciplines the conclusion. (Numbers invented.)

Output format

【Per-outcome reporting】 effect + 95% CI present for primary + key secondary? gaps: [...]
【Statistical-reviewer preflight】 SAP concordance / event counts / assumptions / cross-location consistency? yes/no
【Primary analysis population】 ITT primary? per-protocol as sensitivity? yes/no
【Multiplicity】 hierarchy/adjustment pre-specified? exploratory endpoints labeled? yes/no
【Subgroups】 pre-specified? interaction tests reported? over-interpretation flags: [...]
【Absolute risk + NNT】 reported alongside relative measures? yes/no
【Missing data / survival / non-inferiority margin】 handled & stated? yes/no
【Causal language (observational)】 appropriately cautious? yes/no
【Next】 nejm-figures-tables

Anti-patterns

• Do not report relative risk without absolute risk for clinical outcomes.
• Do not declare secondary or subgroup endpoints significant outside the multiplicity plan.
• Do not infer "no effect" from a wide CI crossing the null on an underpowered comparison.
• Do not use causal verbs ("reduced", "caused") for observational associations.
• Do not make per-protocol the primary analysis in a superiority trial.