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;
• complementary suppression when one cell could be recovered from a row;
• 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. 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.