# 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`.