Aggregate Cohort Evaluation

Scope

This workflow documents cohorts using pre-aggregated counts and summaries. It does not ingest records, classify people, estimate a patient-specific risk, or recommend care.

Protocol Before Results

Pre-specify:

• objective and target population;
• study design and setting;
• index date/time zero;
• eligibility and sampling;
• exposure, comparator, outcomes, covariates, and time windows;
• causal estimand if making a causal claim;
• confounding strategy;
• missing-data strategy;
• subgroup and interaction analyses;
• multiplicity control;
• sensitivity and negative-control analyses;
• disclosure policy.

For routinely collected data, document code sets, phenotypes, database versions, linkage quality, data provenance, and validation.

Participant Flow

Report aggregate counts for:

1. source population;
2. eligibility assessed;
3. excluded by reason;
4. included;
5. analysis populations;
6. missing outcome or follow-up;
7. subgroup availability.

Apply suppression before releasing the flow. Do not reconstruct suppressed values through totals.

Table 1

Use summaries appropriate to distributions and measurement:

• categorical: count, denominator, percentage, missing;
• continuous: mean and standard deviation or median and quartiles;
• time-dependent or repeated measures: define the summary window;
• assay measurements: units, platform, detection limits, batch, and transformation.

Baseline significance tests do not measure meaningful imbalance and are not generated by the bundled table helper. If comparison is needed, pre-specify descriptive standardized differences or another justified measure and interpret it in context.

Effect Estimation

Match measure to question:

• prevalence/risk: risk difference and risk ratio;
• rates: rate difference and rate ratio;
• odds: odds ratio, with care when outcomes are common;
• time to event: estimand-aligned survival measures;
• repeated outcomes: model and covariance assumptions;
• diagnostic accuracy: sensitivity/specificity and predictive values at prespecified thresholds.

Report absolute and relative effects with uncertainty when both are relevant. A p-value is not an effect size and “not significant” is not evidence of no difference.

Confounding and Bias

Address:

• confounding by indication;
• selection and collider bias;
• immortal-time and time-varying treatment bias;
• informative observation/censoring;
• measurement error and misclassification;
• missing data;
• outcome ascertainment;
• site and calendar-time effects;
• data-driven subgroup or cut-point selection;
• unmeasured confounding.

State which variables were selected before analysis and why. Do not select confounders solely by univariable p-values. Distinguish prediction from causal inference.

Subgroups and Fairness

Subgroup work must document:

• rationale and prespecification;
• representation and missingness;
• sample sizes and event counts;
• effect estimates with intervals;
• interaction tests when effect heterogeneity is the question;
• multiplicity;
• measurement validity across groups;
• intersectional and site effects where feasible;
• whether categories are self-reported, assigned, or derived;
• risk of reinforcing structural inequities.

Do not rank groups or declare fairness from one metric. Small groups may require pooling, secure analysis, or non-release rather than unstable public estimates.

Biomarker Cohorts

Record:

• biomarker category using FDA-NIH BEST terminology;
• biological and analytical rationale;
• specimen collection and handling;
• assay platform, version, units, and quality controls;
• prespecified threshold and source;
• analytical validation;
• blinding to outcomes;
• missing/failed assays;
• internal and external validation;
• distinction among prognostic, predictive, and treatment-effect interaction claims.

Never derive a threshold on the evaluation cohort and present it as validated without independent confirmation.

Disclosure Controls

The table generator implements:

• a configurable minimum cell size;
• primary suppression for small nonzero cells;
• an additional within-row suppression when exactly one group cell is suppressed;
• group-level suppression when denominators are too small;
• bounded groups and rows;
• omission of raw values and identifiers.

The default threshold is a conservative operational setting, not a universal rule. The helper does not model row/column dependencies, overlapping groups, or prior releases, so its within-row suppression does not guarantee protection from reconstruction. All supplied counts must remain internally consistent even if a group is suppressed. Validation errors prevent table export. CSV labels and summaries are escaped as text for spreadsheet use. It does not address all differencing, linkage, longitudinal, geographic, genomic, or rare-combination risks. Follow an approved disclosure policy and privacy review.

Interpretation Template

Use:

In this aggregate [design] evaluation, [effect/summary] was estimated as [value and interval] for [defined outcome and horizon]. The analysis is [prespecified/exploratory] and is limited by [bias, missingness, precision, transportability]. It does not establish causality, clinical utility, or an action for any person.

Reporting

• STROBE for observational design.
• RECORD for routinely collected data.
• REMARK for tumor prognostic-marker studies.
• TRIPOD+AI for prediction-model development/evaluation.
• Appropriate causal-inference and target-trial reporting when making causal claims.

See study_reporting.md and privacy_and_disclosure.md.