Jama Statistics

---
name: jama-statistics
description: Use when preparing or auditing the statistical analysis and reporting of a JAMA manuscript so it survives JAMA's dedicated statistical review. Enforces effect sizes with 95% CIs, multiplicity control, and pre-specification; it does NOT choose the study design or write prose.
---

# Statistics & Statistical Review (jama-statistics)

## When to trigger

- Results report p-values without effect sizes or confidence intervals
- Many comparisons but no multiplicity plan
- The analysis was not pre-specified, or outcomes drifted from the protocol
- Preparing for JAMA's **dedicated statistical review** of accepted-pending manuscripts

## Core reporting rules at JAMA

1. **Effect sizes with 95% CIs, not p-values alone.** Report the estimate (mean difference, risk/hazard/odds ratio, absolute risk difference) **with its 95% CI**. P-values supplement; they do not substitute. Treat "significance" as a statement about the interval, not a 0.05 threshold ritual.
2. **Pre-specified outcomes.** State the single primary outcome and the secondary outcomes exactly as registered/protocoled. Flag any post hoc analysis as exploratory.
3. **Intention-to-treat for RCTs.** Primary analysis is ITT; per-protocol and as-treated are secondary/sensitivity.
4. **Multiplicity.** With multiple outcomes, subgroups, or time points, pre-specify the testing hierarchy and the correction (e.g., hierarchical testing, Bonferroni/Holm, gatekeeping). Subgroups without a multiplicity plan are hypothesis-generating only.
5. **Missing data.** State the mechanism assumed and the method (e.g., multiple imputation); report a sensitivity analysis. Complete-case-only needs justification.
6. **Model assumptions.** Justify the model (proportional hazards, linearity, clustering, repeated measures) and report how assumptions were checked.
7. **Absolute and relative effects.** Pair relative measures (RR, OR, HR) with absolute measures (risk difference, NNT) so clinicians grasp magnitude.

## Reporting conventions to follow

- Report exact p-values (e.g., P = .03), not "P < .05"; very small as "P < .001"
- Round sensibly; do not imply false precision
- Give denominators with every percentage; report n/N
- Pre-register and report the analysis population for each estimate
- Describe software and key procedures so the analysis is reproducible
- For meta-analysis: report heterogeneity (e.g., I²), the model (fixed/random), and sensitivity analyses

## Audit table

| Symptom                                          | Fix                                                  |
|--------------------------------------------------|------------------------------------------------------|
| "P < 0.05" with no estimate                      | Add point estimate + 95% CI                          |
| Five "key" secondary outcomes, all "significant" | Pre-specify hierarchy; correct for multiplicity      |
| Subgroup result drives the conclusion            | Label exploratory; report interaction test           |
| RCT analyzed per-protocol as primary             | Re-run ITT as primary                                |
| 20% dropout, complete-case only                  | Multiple imputation + sensitivity analysis           |
| Only relative risk reported                      | Add absolute risk difference / NNT                   |

## Checklist

- [ ] Every primary/secondary estimate has a 95% CI
- [ ] Single pre-specified primary outcome; secondaries labeled
- [ ] Multiplicity handled with a pre-specified plan
- [ ] ITT is the primary analysis for the RCT
- [ ] Missing-data method stated + sensitivity analysis
- [ ] Absolute and relative effects both reported
- [ ] Model assumptions justified and checked
- [ ] Analysis is reproducible (software, code/procedures described)

## Anti-patterns

- Reporting p-values without effect sizes or CIs
- Switching or under-reporting pre-specified outcomes
- Mining subgroups and reporting only the "significant" one
- Claiming "no difference" from an underpowered study (absence of evidence ≠ evidence of absence)
- Per-protocol-as-primary to make an RCT look better
- Conclusions built on a secondary outcome while the primary was null

## Output format

```
【Primary outcome estimate + 95% CI】...
【All estimates have CIs】yes / no
【Multiplicity plan】... 
【ITT primary (RCT)】yes / no / n.a.
【Missing-data handling】...
【Absolute + relative effects reported】yes / no
【Stat-review risks remaining】...
【Next skill】jama-figures-tables
```