ISPOR and ISPE set out transparency framework for real-world evidence

The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the International Society for Pharmacoepidemiology (ISPE) have issued a joint position statement calling for greater transparency in real-world evidence (RWE). The statement was published on 29 September 2026 and presents documentation across the full study lifecycle as a foundation for reproducibility, credibility and practical use.

As reported by Mattheneus.com, the societies recommend that research teams record how evidence was planned, produced, changed and interpreted. Their proposals cover preregistration before a study starts, standardised protocol templates, access to analytic code, systematic data-quality assessments, data-use arrangements and reporting that allows readers to reconstruct the reasoning behind conclusions.

The guidance separates transparency from validity. A study’s validity depends on its design, data and analytical methods; transparency provides an inspectable account of the decisions made around those elements. Registries and structured templates are identified as tools that can preserve this account rather than leaving key decisions scattered across documents and communications.

From protocol to decision

RWE is increasingly used in health technology assessment (HTA), including work on treatment patterns, long-term effects, external comparators, resource consumption and inputs for economic models. Such studies require numerous judgements. These can involve who qualifies for inclusion, when follow-up begins, how confounding is addressed, what counts as an outcome, how missing observations are handled and which sensitivity tests are performed.

A record covering the whole lifecycle would allow reviewers to identify when each decision was made and whether it was later changed. That is more informative than a protocol document viewed in isolation, because a protocol normally represents the intended approach at a single point in time. Reviewers also need to connect that intention with the data-quality findings, cohort construction, code, executed analysis and claims ultimately presented.

This chain can help decision-makers distinguish between preplanned work, amendments supported by a stated rationale and analyses undertaken for exploration. It may also reduce confusion when one study supplies material for several audiences, such as regulators, payers and academic publications. Maintaining a common record of the estimand, population, data cut and analytical version can make later updates easier to check and lessen the risk of inconsistent descriptions.

What teams are being asked to preserve

The recommendations have different implications across the evidence process. HEOR groups would need to connect protocol intentions with implementation choices, findings and any inputs passed into an economic model. Pharmacoepidemiologists and statisticians would benefit from dated specifications for cohort entry, exposure, outcomes, confounders, missing-data procedures, negative controls and sensitivity analyses.

Market access teams could use the record to explain the status of findings before payer review. HTA agencies and payers could use it to examine study design, data quality and analytical changes more efficiently. Data custodians and governance teams would have a place to record access restrictions, permitted uses, transformations and disclosure safeguards alongside the scientific documentation.

The practical workflow described in the article begins with registering the protocol before data extraction. The registration should identify the target population, treatment strategies, index date, follow-up period, causal estimand, endpoints, confounder-selection method, missing-data approach and planned sensitivity analyses. The registration identifier, date and approved version should then be retained in the evidence record.

The next step is an amendment ledger. Each alteration should be linked to the earlier specification, the replacement, the reason for the change, its expected consequences, the person or group approving it, the date and the outputs affected. Related code and results should carry corresponding version information.

Data quality should be documented as part of the analysis rather than treated as a separate administrative exercise. Suggested checks include completeness, continuity, coding consistency, linkage performance, capture of treatment, outcome identification and follow-up. Teams should retain the thresholds used, what the checks found, any corrective action and the uncertainty that remains, then reflect those findings in the strength of their claims.

Economic models need the same lineage

The statement’s implications extend beyond the primary RWE analysis. When real-world estimates are used for survival, treatment duration, resource use, utility values or external comparisons, teams should identify the precise study version feeding each cost-effectiveness or budget-impact scenario. A change in that version should prompt a recorded review of the model’s inputs, results and reimbursement conclusions.

Mattheneus.com reports that AbangeLabs HTA Studio is designed to support this type of governed handoff, with versioned evidence inputs, assumptions, analytical changes, validation results and downstream outputs. Its workflow can retain links between a selected study version, an external comparison or model input, and the final reported scenario. The service supports bring-your-own-cloud deployment, keeping confidential data within a client-controlled environment.

The joint statement is professional guidance rather than a universal regulatory rule. HTA and regulatory expectations remain dependent on jurisdiction, while the effects of adopting the framework on compliance, appraisal decisions and reimbursement outcomes have yet to be measured. Its recommendations therefore offer a structure for traceability, but individual studies must still account for their research question, data source, causal design and local governance requirements.

For evidence teams, the central change is operational: transparency is presented as a continuous record, extending from protocol design through implementation, amendments, interpretation and economic use.