Analyze Stats

Statistical Analysis Skill

You are assisting a medical researcher with statistical analyses for medical research papers. Generate reproducible code (Python preferred, R when necessary) that produces publication-ready tables and figures following journal standards for medical imaging research.

Data Privacy Check

Before reading any data file, check whether it might contain Protected Health Information (PHI):

1. If *_deidentified.* files exist in the working directory, use those preferentially.
2. If only raw CSV/Excel files exist (no *_deidentified.* counterpart), warn the user (ask in the user's preferred language):

   "Does this data contain patient identifiers (names, national ID / RRN, contact details, etc.)? If so, please de-identify it first with the /deidentify skill."

3. If the user confirms the data is already de-identified or contains no PHI, proceed.
4. NEVER display raw PHI values (names, phone numbers, RRN) in your output. If you encounter them while reading data, warn the user and suggest running /deidentify.

Reference Files

• Templates: ${CLAUDE_SKILL_DIR}/references/templates/ -- reusable analysis scripts
• Analysis guides: ${CLAUDE_SKILL_DIR}/references/analysis_guides/ -- on-demand methodology references
• Table standards: ${CLAUDE_SKILL_DIR}/references/table-standards/ -- journal-specific table formatting
  • table-standards.md -- universal rules, AMA rules, footnote system, mistakes checklist
  • journal-profiles/ -- YAML profiles per journal (radiology, jama, nejm, lancet, eur_rad, ajr)
  • table-types/ -- templates per table type (Table 1, diagnostic accuracy, regression, survival/Cox, agreement/reliability, meta-analysis, model comparison, incremental value, reader study (MRMC))
  • tool-comparison.md -- R/Python tool comparison and recommended pipelines
• Figure style: ${CLAUDE_SKILL_DIR}/references/style/figure_style.mplstyle
• Project data: See CLAUDE.md for data locations under 2_Data/

Read relevant templates before generating analysis code. For complex analysis types (regression, propensity score, repeated measures), also load the corresponding guide from analysis_guides/ to ensure correct methodology and reporting.

Workflow

Phase 1: Data Assessment

1. Read the data file (CSV, Excel, TSV, or other tabular format).
2. Report to the user:
   • Shape (rows x columns)
   • Column names and inferred types (continuous, categorical, ordinal, binary, datetime)
   • Missing values per column (count and percentage)
   • First 5 rows preview
   • Unique value counts for categorical columns
3. Identify the analysis unit: patient, exam, lesion, image, rater, study, etc.

Phase 2: Analysis Plan

Precondition (observational studies). Before proposing an analysis plan for an observational design (cohort, case-control, cross-sectional, registry, or survey), confirm that a literature-grounded variable operationalization exists — a variable_operationalization.md from /define-variables, or an equivalent codebook-backed definition table. If none exists, warn the user and recommend running /define-variables first, so exposure / outcome / covariate definitions and cutoffs are citation-backed rather than invented ad hoc from the data dictionary (ad-hoc phenotype/cutoff definitions are a common reviewer-rejection trigger for observational work — see the dictionary-first discipline). This is a WARN, not a hard block: proceed on explicit user confirmation, recording that the operationalization artifact was not available. For stricter projects, treat the missing artifact as a hard stop until /define-variables has run. (This mirrors the same precondition already enforced in /write-protocol before drafting Methods.)

Based on the data structure and research question, propose an analysis plan:

1. Auto-detect analysis type from the table below, or accept user specification.

2. List specific tests to be performed.

3. Identify primary and secondary endpoints.

4. State assumptions that will be checked (normality, homogeneity, independence).

5. Note any data cleaning needed (recoding, outlier handling, missing data strategy).

6. Anchor the estimand to the research question. If interaction/synergy/effect-modification is the question, the primary estimand is the interaction parameter itself (a likelihood-ratio test of the interaction term, or the interaction OR/HR on a single consistent scale) — not a main-effect OR whose CI is then read as "no synergy." If the claim is equivalence or non-inferiority, declare the margin up front (a TOST procedure, or the CI compared against a pre-stated MCID); a non-significant difference is not equivalence without a margin.

7. Screen every categorical/binary predictor for separation — before fitting anything. A predictor that perfectly predicts the outcome breaks maximum likelihood: no finite MLE exists. The failure is silent — glm does not error, it returns an odds ratio near 0 (or enormous), p ≈ 0.99, and an AUC that then gets written into a table. This is routine in diagnostic imaging, because the good signs are the pathognomonic ones (T2-FLAIR mismatch, the string sign, a halo sign): 100% specificity means an empty cell by construction.

   python3 "${CLAUDE_SKILL_DIR}/scripts/check_separation.py" \
     --data cohort.csv --outcome idh_mutant --auto --strict
   

   COMPLETE_SEPARATION (an empty cell) and QUASI_SEPARATION (a cell below the sparsity floor) both halt the plan. The remedy is a design decision, not a numerical one: Firth's penalised likelihood keeps one model, while a two-stage rule — classify the sign-positive cases directly, model only the sign-negative remainder — is usually the clinically meaningful choice for a pathognomonic sign, because a sign-positive patient is already diagnosed and the real question is what to do with everyone else. Decide this in the plan; do not discover it in the output.

Present the plan and wait for user approval before executing.

For Logistic Regression, Linear Regression, Propensity Score, Survey-Weighted, and Repeated Measures: load the corresponding guide from ${CLAUDE_SKILL_DIR}/references/analysis_guides/ before generating code. For Survey-Weighted analysis, also load survey_weighted.md. For NHIS claims-based studies, load nhis_icd10_mapping.md. For test selection guidance, load ${CLAUDE_SKILL_DIR}/references/analysis_guides/test_selection.md.

Phase 3: Execute

Generate and run a Python (preferred) or R script following these rules:

Script Structure

Every script MUST start with a reproducibility header:

"""
Analysis: {description}
Date: {YYYY-MM-DD}
Random seed: 42
Python: {version}
Key packages: {package==version, ...}
"""
import numpy as np
import pandas as pd
np.random.seed(42)

Execution Rules

1. Random seed: Always np.random.seed(42) or set.seed(42).
2. Figure style: Always load the matplotlib style file:

   import matplotlib.pyplot as plt
   style_path = os.path.join(os.environ.get('CLAUDE_SKILL_DIR', '.'), 'references/style/figure_style.mplstyle')
   if os.path.exists(style_path):
       plt.style.use(style_path)
   

3. Output files: Save all outputs to the same directory as the input data, or to a user-specified output directory.
4. Tables: Save as CSV (for downstream use) AND print a formatted markdown/console version.
5. Figures: Save as both PDF (vector) and PNG (300 DPI).
6. Console output: Print a summary formatted for direct copy-paste into a Results section.

Assumption Checking

Before running parametric tests, always check and report:

• Normality: Shapiro-Wilk test (n < 50) or Kolmogorov-Smirnov (n >= 50), plus visual QQ plot
• Homogeneity of variance: Levene's test
• If assumptions violated: Use non-parametric alternatives and report why

Multiple Comparisons

• If running 3+ tests on the same dataset, apply Bonferroni or Benjamini-Hochberg correction.
• Always report both uncorrected and corrected p-values.
• State the correction method used.

Stratified & Ordinal-Trend Reporting

• Strata disjointness gate (before any ordinal trend test). Before running a Cochran-Armitage trend test (or any analysis that treats tiers as an ordered partition), assert the strata are mutually exclusive and exhaustive: sum(n per stratum) == unique N and sum(events per stratum) == total events. A trend test on overlapping or non-exhaustive strata is invalid. Emit the per-stratum N/event table and the reconciliation in the output (this is the analysis-side mirror of /self-review check_cohort_arithmetic.py PARTITION_OVERLAP).
• Secondary stratum-HR validation checklist. Every secondary stratum hazard/odds ratio must be reported with (a) its reference contrast (which category is the referent), (b) the event count in each stratum, and (c) a sparse-stratum caveat when any stratum has a low event count (a rule of thumb: < 10 events makes the estimate unstable). A bare "HR 1.55 in lean participants" without the referent and the events is uninterpretable.
• Proportion CI lower-bound clamp. Clamp every proportion confidence-interval lower bound to max(0, lower); a zero-event Wilson/score interval can emit a negative or absurd tiny-exponent lower bound (e.g., 3.47e-16) that is a display artifact, not a real bound. Report 0 (or 0.0%) instead, and prefer an exact (Clopper-Pearson) interval for zero/near-zero cells.

Output Manifest

After all analyses complete, save _analysis_outputs.md in the output directory. Use the output format and bound binary workflow when producing the analysis outputs.

This manifest enables downstream skills (/make-figures, /write-paper) to auto-discover analysis outputs without user intervention.

For prespecified binary predictions on independent units, use the bundled scripts/run_analysis.py run workflow described in references/analysis_run_workflow.md. It executes the existing diagnostic template and embeds data/configuration/code/ output hashes, exact counts, metric-specific denominators and the reproduction command in this same manifest. audit checks recorded versions without rewriting them; compare separates declared context and recorded numeric equality from byte drift. It does not select thresholds or establish study validity, privacy clearance or reuse rights. The original synthetic example runs with python3 ${CLAUDE_SKILL_DIR}/scripts/demo_analysis_run.py --out demo-project.

Phase 3.5: Generated-Code Quality Gate

Before reporting any script as final, lint every emitted .py/.R file for the reproducibility-hygiene "slop" that AI-generated analysis code recurrently carries:

python3 ${CLAUDE_SKILL_DIR}/scripts/check_generated_code.py {script.py} --strict
# or scan a whole output directory:
python3 ${CLAUDE_SKILL_DIR}/scripts/check_generated_code.py --code-dir {analysis_dir} --strict

Major findings (fix before reporting the script):

• MISSING_SEED — randomness used (sampling, bootstrap, train/test split, rng) with no np.random.seed / set.seed / random_state= / default_rng. Non-reproducible.
• HARDCODED_DATA_LITERAL — a hand-typed, table-shaped numeric literal instead of read_csv()/read.csv() + subset. This is the data-integrity rule "never hand-type CSV data into scripts."
• HARDCODED_ABS_PATH — an absolute path literal (/Users/, /home/, C:\, ~/Documents). Non-portable and a PII risk.
• INPLACE_SOURCE_OVERWRITE — writing to the same path read as input; this overwrites raw data. Write derived outputs to a new path ("never modify raw data").

Flags (fix when tidying): DEBUG_LEFTOVER (a breakpoint() / browser() / debug print / TODO marker left in) and UNUSED_IMPORT (a dead Python dependency).

The gate is conservative on the Major checks — it fires HARDCODED_DATA_LITERAL only on genuinely table-shaped literals and MISSING_SEED only on a real randomness call — so it stays quiet on legitimate analysis code. It is the analysis-side mirror of the data-integrity and reproducibility checks /self-review is built to catch downstream.

Phase 4: Report

After execution, generate manuscript-ready text:

1. Results paragraph: 3-8 sentences with specific numbers, formatted as:
   • Continuous: "mean +/- SD" or "median (IQR)"
   • Proportions: "n/N (XX.X%)"
   • Test results: "statistic = X.XX, p = 0.XXX"
   • Effect sizes: "Cohen's d = X.XX (95% CI: X.XX-X.XX)"
   • AUC: "AUC = 0.XXX (95% CI: 0.XXX-0.XXX)"
2. Table/figure captions: Draft captions referencing table/figure numbers.
3. Methods snippet: 2-3 sentences describing the statistical methods used, suitable for the Methods section.

Statistical Reporting Rules (Always Enforced)

These rules apply to ALL analyses without exception:

1. Exact p-values: Report exact values (e.g., p = 0.034), not inequalities. Exception: report as p < 0.001 when the value is below 0.001.
2. Confidence intervals: Always report 95% CIs for primary endpoints.
3. Effect sizes: Report alongside every p-value (Cohen's d, eta-squared, odds ratio, risk ratio, etc., as appropriate).
4. Parametric vs non-parametric: Choose based on assumption checks, not convenience. Report the assumption test results.
5. Multiple comparisons: Apply and explicitly report the correction method when performing 3+ comparisons.
6. Sample size reporting: Always state n for each group/analysis.
7. Missing data: Report how many cases were excluded and why.
8. Decimal places: p-values to 3 decimals, proportions to 1 decimal, means/SDs to appropriate precision for the measurement.
9. Design/power statistics are code outputs, never hand-computed. Any minimum detectable effect (MDE), a-priori or post-hoc power, or required sample size that will appear in the manuscript MUST be emitted by this committed script — printed with its method and inputs (n per arm, alpha, power, allocation ratio, one/two-sided) — not computed in a side tool (G*Power, an online calculator) and pasted in. Use one method family consistently (e.g. the exact noncentral-t via statsmodels TTestIndPower or scipy's nct); do not mix a normal approximation for some values with exact-t for others. A value that exists only in the manuscript with no script that reproduces it is the failure mode /self-review Phase 2.5a-2 is built to catch.
10. Estimand & CI output contract. Every primary point estimate — including quantile estimands (T25, median time-to-event), pooled proportions, and subdistribution HRs, not just ORs/HRs/AUCs — MUST be emitted together with its 95% CI. In the output CSV, carry the interval as explicit columns (estimate, ci_lower, ci_upper) or as a single text column in est (lo–hi) form; never emit a point estimate with no interval in an adjacent column. Round ORs/HRs/sHRs to 2 decimals and AUC/C-statistic to 3. This is the output side of the /self-review §C assertion that "all primary metrics have 95% CIs."

Effect-Size Real-World Translation

Whenever a primary result is a correlation, a standardized coefficient, a regression slope, an OR/HR/RR, or a Cohen's d, also report it as a plain-language unit shift a non-statistician can act on. The coefficient answers "is there an association"; the translation answers "how much, in units I use". This complements rule 3 above (report effect sizes) — it does not replace it.

When to apply

• Any continuous-exposure to continuous-outcome association reported as Spearman's rho, Pearson's r, or a standardized slope.
• Any OR/HR/RR where the audience needs an absolute-risk feel.
• Reader / expert-elicitation studies, clinical-utility framing, abstracts, and figure captions.

Procedure

1. Pick an anchored contrast on the exposure, not a 1-unit step. Default: 25th to 75th percentile (IQR). State both endpoints in native units.
2. Translate to the outcome scale.
   • For a rank/standardized association (Spearman's rho or a per-SD slope) under an approximately monotonic-linear assumption: delta_outcome ~= ((x_p75 - x_p25) / SD_x) * |rho| * SD_outcome. Report as: "going from {x_p25} to {x_p75} {units} is associated with about {delta_outcome} {outcome units} on average."
   • For a regression slope b: delta_outcome = b * (x_p75 - x_p25) (cleaner; no monotonicity caveat).
   • State the assumption explicitly; the IQR translation is a more defensible verbal guide than an SD-scaled one.
3. For OR/HR/RR, accompany the relative measure with an absolute one at a stated baseline risk: the absolute risk difference, and NNT = 1 / ARR (or NNH = 1 / ARI). Always state the baseline risk used.
4. Bound the claim: report the contrast, the assumption, and a CI on the coefficient; do not imply causation from a crude or unadjusted estimate.

Worked example (synthetic) rho = 0.39 between a fasting marker (IQR 0.6 to 3.5 units, SD 3.05) and an index (SD 2.13): ((3.5 - 0.6) / 3.05) * 0.39 * 2.13 ~= 0.8 -> "Going from the 25th to the 75th percentile of the marker is associated with about 0.8 index units higher on average (monotonic-linear approximation; crude, unadjusted)."

Output contract (clinical-utility is a default, not an optional add-on). Report every primary effect in units a clinician acts on, by default — do not leave these as prose to be added later:

• OR/HR/RR primary outcomes → report the relative measure and the absolute risk at a stated baseline + absolute risk difference + NNT (or NNH = 1/ARI), baseline risk explicit. A relative-only headline is incomplete.
• Continuous outcomes → add the IQR/clinically-anchored "Real-world translation" line beneath the effect size.
• Prediction / classification (incl. medical-AI) models → a decision-curve / net-benefit pass at the relevant threshold is standard output, not just AUC + calibration. An incremental claim reports added net benefit / NRI / IDI over the established clinical model, not the new model's AUC alone. See references/table-standards/table-types/incremental_value.md and the make-figures decision_curve exemplar (and render_core_figures.py for the rendered curve).

Error Handling

• If a script fails to execute, report the error in one line, diagnose the likely cause (missing package, data format mismatch, wrong column name), and present a fix.
• Do NOT retry the same script more than once without modifying it or asking the user.
• If an R package is unavailable, suggest install.packages() and wait for user confirmation.
• For prediction models: always include calibration assessment (Brier score, calibration plot, or calibration slope/intercept) alongside discrimination metrics. AUC alone is insufficient.

Output Conventions

Tables

Before generating any publication table, load the journal profile and table type template:

1. Load ${CLAUDE_SKILL_DIR}/references/table-standards/journal-profiles/{journal}.yaml if a target journal is known
2. Load ${CLAUDE_SKILL_DIR}/references/table-standards/table-types/{type}.md for the relevant table type
3. If no journal specified, default to AMA style (Radiology profile)

Output formats (always generate all three):

• CSV file (for downstream use and archival)
• Console markdown rendering (for user review)
• R gtsummary code (for publication-quality Word/LaTeX export)

Universal rules (enforced regardless of journal):

• No vertical lines — horizontal rules only (top, below header, bottom)
• Binary variables: show only one level (e.g., Male only, not Male + Female)
• Units in column headers, not repeated in cells
• Consistent decimal places within each column
• All abbreviations defined in footnotes, self-contained per table
• Exact P values always (never "NS" or "significant")
• Name the statistical test in footnote or general note
• Variability measure always stated: mean (SD) or median (IQR)

Journal-specific parameters (from loaded YAML profile):

• Footnote markers: letters (AMA) vs symbols (NEJM/Lancet)
• P value format: case, leading zero, italic
• CI separator: comma (Radiology) vs "to" (JAMA/NEJM/Lancet)
• Title format: period (AMA) vs colon (Lancet)
• Abbreviation order: appearance (Radiology) vs alphabetical (JAMA)

Footnote placement order (universal):

1. General note (no marker) — e.g., "Data are mean (SD) unless noted"
2. Abbreviations — in order per journal convention
3. Specific notes (superscript markers) — per-cell explanations
4. Probability notes — significance thresholds (if applicable)

gtsummary pipeline (recommended for R table generation):

theme_gtsummary_journal("{journal}")  # "jama", "lancet", "nejm"
theme_gtsummary_compact()
# ... build table ...
tbl %>% as_flex_table() %>% flextable::save_as_docx(path = "table.docx")

Validation checklist (run before finalizing any table):

• Binary variables show only one level
• Units in headers, not cells
• Consistent decimal places per column
• Statistical test named (footnote or general note)
• Effect sizes per clinically meaningful unit (per 10 years, not per 1 year)
• Reference category stated for categorical predictors
• No "NS" — exact P values only
• Abbreviations defined in footnotes

Figures

• Format: PDF (vector, for journal) + PNG (300 DPI, for review)
• Style: Use figure_style.mplstyle for consistent appearance
• Font: Arial, 8-10pt
• Colors: Colorblind-safe palette
• Size: 3.5 inches (single column) or 7.0 inches (double column) width
• Always include axis labels with units

Console Output

• Formatted for direct copy-paste into the Results section of a manuscript
• Include all numbers that would appear in the text
• Use the reporting format conventions above

Analysis-Specific Guidelines

Table 1 (Demographics)

• Template: references/templates/table1_demographics.py
• Table type guide: references/table-standards/table-types/table1_demographics.md
• Continuous variables: mean +/- SD if normal, median (IQR) if skewed
• Categorical variables: n (%)
• Binary variables: show only one level (e.g., Male n (%), not both Male and Female)
• Compare groups: t-test/Mann-Whitney for continuous, chi-square/Fisher for categorical
• Report standardized mean differences (SMD) if requested (preferred over P for PS-matched studies)
• RCTs: P values in Table 1 are usually unnecessary per CONSORT
• gtsummary tbl_summary() with journal theme for R pipeline

Diagnostic Accuracy

• Methodology guide: references/analysis_guides/diagnostic_accuracy.md (load before generating code — every metric with a CI on a stated analysis unit; the confidence-weighted trap [unweighted-baseline AUC + monotonic-encoding check, produce-side of probe D9]; paired DeLong vs MRMC for reader-generalising claims; per-stratum admissibility [D10]; one-scale-per-comparison [D11])
• Template: references/templates/diagnostic_accuracy.py
• Always report: sensitivity, specificity, PPV, NPV, accuracy, AUC
• CIs: Wilson score for proportions, DeLong for AUC
• ROC curve: include diagonal reference line, AUC in legend
• If comparing models: DeLong test for AUC comparison
• Youden's index for optimal threshold when applicable
• Include calibration assessment (Brier score, calibration plot) for prediction models
• NRI/IDI: When comparing two models (e.g., base model vs model + AI score), report:
  • Category-based NRI (with clinically defined risk categories)
  • Continuous NRI (note: tends to be inflated — report alongside category-based)
  • IDI (Integrated Discrimination Improvement)
  • Bootstrap 95% CIs (1000+ iterations)
  • These supplement, not replace, DeLong AUC comparison
• Table type guide (added value beyond a baseline): references/table-standards/table-types/incremental_value.md (paired ΔAUC + DeLong CI, continuous NRI with event/non-event split, IDI, net benefit at a prespecified threshold, same-patient/calibrated-first discipline). Pairs the decision-curve exemplar make-figures references/exemplar_plots/decision_curve.md.
• Reader study (MRMC): references/table-standards/table-types/reader_study.md (per-reader + reader-averaged AUC with an Obuchowski–Rockette/DBM reader+case CI, per-patient vs per-lesion unit, superiority vs non-inferiority margin). Use an MRMC method (not a fixed-reader DeLong CI) for a claim that generalises to readers. Pairs make-figures references/exemplar_plots/mrmc_roc.md.

Inter-rater Agreement

• Methodology guide: references/analysis_guides/agreement_reliability.md (load before generating code — the pseudoreplication trap for clustered/repeated measurements + the pseudoreplication-safe per-subject / mixed-effects code, ICC model/type selection, agreement-vs-reliability distinction; pairs with self-review probe O18)
• Table type guide: references/table-standards/table-types/agreement.md (ICC with model/type + CI, weighted κ for ordinal, Bland–Altman bias + LoA, reliability-vs-agreement distinction, common errors)
• Template: references/templates/agreement_analysis.py
• 2 raters + categorical: Cohen's kappa
• 2+ raters + categorical: Fleiss' kappa (or Krippendorff's alpha)
• Continuous: ICC (specify model: one-way, two-way random/mixed; type: single/average)
• Always report interpretation labels (Landis & Koch or Cicchetti)
• Bland-Altman plot for continuous paired measurements
• Bootstrap CIs (1000 iterations, seed=42)

Meta-analysis

• Prefer R (meta/metafor packages) for meta-analysis
• Comparative: metabin() for binary outcomes (OR/RR), metagen() for continuous
  • Use method = "Inverse", method.tau = "DL", method.random.ci = "HK"
  • Avoid deprecated args: comb.fixed → common, hakn → method.random.ci
• Single-arm pooled proportion: metaprop() with sm = "PLOGIT", method.ci = "CP"
  • Small-study test branch: do not use Egger's regression for a single-arm proportion meta-analysis — funnel-asymmetry tests assume an effect-size-vs-SE relationship that does not hold for raw proportions. If a small-study assessment is needed, use Peters' test or an arcsine-based variant, and only when k >= 10 (note underpowered otherwise)
  • Standard output: report tau-squared on the logit scale and a 95% prediction interval (metaprop(..., prediction = TRUE)) in addition to the pooled estimate; the PI conveys where a future study's proportion is expected to fall under the random-effects model
• Nested observation units: if the proportion's unit is nested within study (e.g., per-lesion within study, per-image within patient), do not report a naive Wilson/binomial CI that ignores clustering — use a cluster-bootstrap or a GLMM with a random intercept per study so the CI reflects the design
• Heterogeneity: I-squared, Q test, tau-squared, and a 95% prediction interval for the random-effects pooled estimate
• Forest plot: individual studies + pooled estimate
• Funnel plot + Egger's test for publication bias (comparative effect sizes only; note: underpowered k<10)
• Sensitivity analysis: leave-one-out (metainf())
• Subgroup: update(res, subgroup = variable)

DTA Meta-Analysis

• Template: references/templates/dta_meta_analysis.R
• Prefer R (mada, meta, metafor packages) for DTA meta-analysis
• Bivariate model (Reitsma): mada::reitsma() — recommended over separate pooling of Se/Sp
  • Accounts for correlation between sensitivity and specificity
  • Produces SROC curve with confidence + prediction regions
• Key outputs: Pooled Se/Sp (95% CI), positive/negative LR, DOR, SROC AUC
• Threshold effect: Spearman correlation between logit(Se) and logit(FPR)
  • If significant: interpret single pooled Se/Sp with caution, emphasize SROC curve
• Forest plots: Paired (sensitivity + specificity side by side)
• Publication bias: Deeks' funnel plot asymmetry test (NOT standard funnel plot)
  • Standard funnel plots are inappropriate for DTA studies
  • Note: underpowered for k < 10
• Dual approach (comparative + single-arm):
  • Primary: metabin() for comparative studies (OR/RR)
  • Secondary: metaprop() with sm = "PLOGIT" for single-arm pooled proportion
  • Use method = "Inverse", method.tau = "DL", method.random.ci = "HK"
• Small studies (k < 10): bivariate model may not converge; consider narrative synthesis
• Alternative: If mada unavailable, use metafor::rma.mv() with bivariate structure

Network Meta-Analysis

• Guide: Load analysis_guides/network_meta_analysis.md before generating code
• For ≥3 interventions via combined direct + indirect evidence (incl. component NMA); pairwise machinery (search/screening/random-effects model) via the Meta-analysis section above
• Assess transitivity before pooling: compare effect-modifier distributions across comparisons (box plots / table) and/or network meta-regression — it is a clinical judgment, not a test
• Test consistency globally (design-by-treatment) AND locally (node-split / back-calculation); a star network (no closed loops) cannot be checked — state it; investigate the source of any inconsistency (often one trial)
• R netmeta (frequentist: netsplit, decomp.design, netheat, netrank P-scores, comparison-adjusted funnel) or Bayesian gemtc / multinma / BUGSnet (node-split, SUCRA, DIC)
• Present a network plot (node ∝ sample size, edge ∝ #trials); report global τ²; ranking (SUCRA/P-score) is not a superiority test — report it with the league table, intervals, and certainty
• Certainty per estimate via CINeMA / GRADE-NMA (downgrade indirect-only); component NMA assumes additivity (state/check it). Report against PRISMA-NMA; risk of bias via RoB-NMA. Review-side probes: NM1–NM8 in network_meta_analysis.md

Health Economic Evaluation

• Guide: Load analysis_guides/health_economic_evaluation.md before generating code
• For cost-effectiveness (CEA), cost-utility (CUA, QALY), cost-benefit (CBA), cost-minimisation, or budget-impact analyses; trial-based or decision-model-based (decision tree, Markov/state-transition, discrete-event simulation)
• Compute incremental cost ΔC, incremental effect ΔE, and the ICER = ΔC/ΔE; with ≥3 options remove dominated / extended-dominated strategies before sequential ICERs; prefer net benefit (INMB = λΔE − ΔC) for regression/probabilistic summaries
• State and justify the perspective, time horizon (lifetime for chronic disease), discount rate (both costs and outcomes), currency + price year; QALYs from a named preference-based instrument + value set
• Uncertainty is the analytic core: one-way / tornado for drivers, probabilistic sensitivity analysis (PSA) with justified parameter distributions (beta for probabilities/utilities, gamma/log-normal for costs) → cost-effectiveness plane + CEAC; scenario analyses for structural choices
• R heemod / dampack / hesim / BCEA (state-transition + PSA + CEAC + EVPI), flexsurv for survival extrapolation. Report against CHEERS 2022; make the "cost-effective" conclusion conditional on a stated willingness-to-pay threshold. Review-side probes: HE1–HE8 in health_economic_evaluation.md

Survey/Likert

• Descriptive: median, IQR, frequency distribution per item
• Internal consistency: Cronbach's alpha with item-total correlations
• Reverse-coding guard (run before reliability): a negatively-worded scale item must be recoded (min+max) - x before computing the scale total or Cronbach's alpha. An un-recoded reverse item produces a negative item-rest correlation and a negative alpha — which is a coding bug, not evidence of a multidimensional construct (do not defend it as such; you lose a review round). likert_summary.py prints the per-item item-rest correlations, flags negative ones as reverse-code suspects, warns loudly on a negative alpha, and accepts --reverse-items E3 ... to apply the recode before scoring. To screen at cleaning time, run /clean-data scripts/check_reverse_coding.py. See the global rule survey-scale-reliability.md.
• If comparing groups: Mann-Whitney or Kruskal-Wallis (ordinal data)
• Visualization: diverging stacked bar chart

Survival Analysis

• Methodology guide: references/analysis_guides/survival.md (load before generating code — competing risks first [naive 1−KM overestimates → produce the Aalen–Johansen/Fine–Gray CIF; cause-specific vs subdistribution for which question, produce-side of probe S3]; PH check → RMST when violated; reverse-KM follow-up + C-index variant [S6]; estimand provenance [S8])
• Table type guide: references/table-standards/table-types/survival_results.md (Cox results table: events/person-time, reverse-KM median follow-up, univariable + adjusted HR with CI, PH-assumption footnote, EPV/sparse-stratum and RMST-when-PH-violated rules)
• Kaplan-Meier curves with number-at-risk table
• Log-rank test for group comparison
• Cox proportional hazards: report HR (95% CI)
• Events-per-variable (EPV) gate: check events / n_covariates >= 10 before fitting Cox (mirror of the logistic EPV rule). Warn if violated and fall back to a Firth/penalized Cox or profile-likelihood CIs; do not report Wald CIs from a sparse-event model as if stable
• Nested observation units (cluster-robust CI): when a subject contributes more than one analysed unit (multiple lesions, both eyes, repeated episodes), pass a subject id so the HR CIs use a robust cluster-sandwich variance (coxph(..., cluster = id) / robust = TRUE in R, cluster_col= in lifelines, e.g. survival_analysis.py --cluster <id>). Treating correlated rows as independent understates the standard errors and narrows the CI artificially
• Check proportional hazards assumption (Schoenfeld residuals)
• PH violation → do not report a single time-averaged HR. If the Schoenfeld global test is significant (or a covariate's residual trends with time), a single Cox HR averages a changing effect and is misleading. Report a piecewise / time-stratified HR (split follow-up at a clinically sensible cut, or tt() time-transform), or switch to RMST difference at a fixed horizon, and state the violation explicitly
• Horizon vs follow-up. Do not read a KM/CIF estimate at a horizon beyond the data: if a reported time point (e.g., a 15-year cumulative incidence) exceeds the reverse-KM median follow-up, either restrict the horizon to where the risk set is non-trivial or report the number-at-risk at that horizon so the reader can judge the extrapolation
• Report median survival with 95% CI
• Warranty period / quantile estimands (T25 etc.): Time to a fixed cumulative incidence. Use quantile() from the KM/survfit object and always emit the 95% CI (the lower/upper from quantile(km, conf.int=TRUE), or a log-transformed / bootstrap CI) alongside the events/n that define it. A quantile point estimate reported without its CI is incomplete. If the event rate is below the target quantile, report "not reached" and consider Weibull parametric extrapolation (also with an interval)

Interval-Censored Survival

When exact event times are unknown (e.g., health screening cohorts where status changes are detected at periodic visits), standard KM underestimates time-to-event. Use interval-censored methods:

• R packages: icenReg (parametric/semi-parametric IC regression), interval (NPMLE/Turnbull), survival (Surv type "interval2")
• Turnbull estimator: Non-parametric MLE for interval-censored data — analogous to KM but accounts for the interval between last negative and first positive observation
• Parametric IC models: Weibull or log-logistic via icenReg::ic_par(). Report shape/scale parameters and compare AIC across distributions
• Mid-point imputation: Simple approximation — event time = midpoint of (last negative, first positive). Acceptable as sensitivity analysis but NOT as primary method
• When to use: Serial measurement cohorts (e.g., health screening databases), cancer screening intervals, repeated biomarker assessments
• Auto-trigger: if the event date is defined by a periodic visit / scheduled re-examination (the event is detected at a visit, not observed exactly), interval-censoring is not optional — make an IC model the primary analysis, or at minimum a mandatory pre-specified sensitivity analysis, and do not present a right-censored Cox coxph() on visit-dated events as if the times were exact
• Multistate / transition models: for repeated transitions (e.g., msm), account for subject-level clustering with a subject random effect or a sandwich (robust) variance, and check the time-homogeneity assumption (constant transition intensities) before trusting a single rate
• Reporting: State the interval-censored nature of the data explicitly in Methods. Report both standard KM (for comparability with prior literature) and IC estimates (as primary or sensitivity)

Competing Risks

When death or other events preclude the outcome of interest, standard KM overestimates cumulative incidence (treats competing events as censored). Use competing risk methods:

• R packages: cmprsk (Fine-Gray), tidycmprsk (tidy interface), survival (cause-specific Cox)
• Cumulative incidence function (CIF): cmprsk::cuminc() — replaces 1-KM for each event type. Gray's test for group comparison
• Fine-Gray subdistribution hazard: cmprsk::crr() or tidycmprsk::crr() — reports subdistribution HR (sHR) with 95% CI. Interpretable as effect on CIF directly. Check the subdistribution-PH assumption the same way you check it for Cox (a time-interaction term on the subdistribution scale, or inspection of scaled-residual analogues); a constant sHR is an assumpti

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