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About IMPRESIVE

Paving the Way to Robust Global Evidence

Starting from common protocol approaches, the IMPRESIVE framework has evolved into a structured programme encouraging the implementation of Common Data Model (CDM) standards.

A complete pathway from databases to decisions

International Multi-database study PREparedness: Databases Standardization, Integration and Visualization for timely Evaluation on Disease and Treatment.

The programme prepares local data environments for common study questions, transparent implementation, quality review, distributed analysis, and contextual interpretation. It does not require person-level partner data to be centralized.

Our work spans the entire research pipeline, from feasibility assessment, protocol and variable specification, and data standardization (CDM implementation) to quality validation, common analytical programming, distributed analysis, and cross-database interpretation.
We provide the methods, reusable code, quality-assurance processes, and analytical support required to transform research questions into decision-ready evidence, while person-level data remain securely under local control.

Why collaborate across data environments?

  • Diverse Populations & Settings: Evidence from a single database may not generalize to populations in other regions. Differences in reimbursement systems, care pathways, and prescribing practices vary significantly across databases, directly impacting measured health outcomes.
  • Feasibility & Precision: Aggregating multiple data sources enables investigations into rare events, rare exposures, or small patient subgroups. This improves statistical precision and yields more reliable treatment effect estimates when definitions and follow-up periods are standardized.
  • Consistency & Heterogeneity: Cross-country comparisons reveal whether diagnosis and prescribing patterns are globally consistent or where clinically and operationally meaningful variations exist.
  • Transportability: Multi-database analyses assess whether evidence generated in one setting can reliably inform clinical and policy decisions in another.

    • The IMPRESIVE journey

    From individual studies to collaborative research infrastructure

    1. 2019
      Foundation · BTA

      Applying a multinational common protocol

      We evaluated bone-targeting-agent utilization and effectiveness in breast, prostate, and lung cancer patients with bone metastasis across multiple countries.

      Read the BTA origin story

      Before the cases

      The BTA project established the starting point

      1. The work

        A multinational common-protocol collaboration

        The BTA project studied utilization patterns and effectiveness of bone-targeting agents in breast, prostate, and lung cancer with bone metastasis. A standardized protocol was delivered to each data partner.

      2. The cost

        Alignment consumed the effort

        Substantial work was required to align protocol details, ensure appropriate variable definitions at every site, and independently develop analytic programs at each location.

      3. The decision

        Move the standardization upstream

        The next programme stages moved from protocol-only alignment toward common data models and shared analytic programs, while retaining local implementation and review.

    2. 2021
      IMPRESIVE 1.0

      Initiating an OMOP CDM framework

      We leveraged hospitalized atherosclerotic cardiovascular disease (ASCVD) cohorts to align definitions and examine baseline characteristics and evaluate recurrent risk.

    3. 2023
      IMPRESIVE 2.0 and 3.0

      Comparing approaches and extending the framework

      We evaluated consistency across varying analytical approaches and follow-up choices, expanding the framework's application to dermatological conditions like atopic dermatitis and prurigo nodularis.

    4. 2025
      July 2025 · Investigator meeting

      Promoting the common-data-model approach

      We promoted international collaboration and established transportability as a core research priority.

    5. Now
      Current direction

      Studying transportability and expanding our network

      We examined how evidence from one population can be transported to another while preserving local feasibility, data context, and clinical interpretation.

      How the current transportability study is framed

      Aim. Examine whether treatment-effect estimates from Taiwan can inform a Japanese setting without treating population differences as noise.

      Design. Retrospective target-trial emulation in Taiwan NHIRD and Japan DeSC, comparing observed estimates with estimates transported by a model developed in Taiwan.

      Setting. Patient-level data remain in the approved local environments; the public page presents the study design and current aggregate result summaries.

      Open the transportability case →

    6. Next
      Open horizon

      Expand the network

      We plan on expanding partnerships across new countries, data environments, and therapeutic areas to drive shared methodological development and broader real-world evidence applications.

      Join us

    The Advantages of the Common Data Model (CDM)

    Upfront Standardization for Long-Term Analytical Efficiency

    A Common Data Model reorganizes local data into a shared structure and set of definitions. This allows identical analytical code to run across all participating sites while patient-level data remain protected inside local firewalls. While standardization requires an initial investment, once databases are mapped and validated, that infrastructure is reused seamlessly across subsequent studies.

    Timeline of common-data-model adoption in multi-database research, showing the successive programmes and data models that established the approach IMPRESIVE follows.
    Compare the OMOP and Sentinel routes

    Three core principles

    Prepared databases. Timely analyses. Informed decisions.

    Start with the core workflow
    01 · INPUT

    Database

    Understanding and respecting each partner’s data environment, coding culture, and feasibility constraints.

    Partner → survey → readiness

    • Source-data survey
    • Study-specific readiness
    Open partner readiness →
    02 · ENGINE

    Analytics

    Ensuring models are standardized, code is reusable, and quality checks guarantee comparable execution across all sites.

    Harmonize → execute → validate

    • CDM routes
    • Reusable module library
    • Quality validation
    Open the methods →
    03 · OUTPUT

    Knowledge

    Reviewing cross-setting findings while accounting for local uncertainty and limitations before delivering actionable recommendations.

    Evidence → context → application

    • Case accomplishments
    • Reviewable evidence figures
    • Transportability
    Open accomplishments →

    PHDc–AsPEN–IMPRESIVE

    The role map separates organizational coordination, application support, and research infrastructure. PHDc is the primary public contact for this website.

    Organizational bridge & contact

    PHDc

    The Population Health Data Center (PHDc) serves as the core analytical organization, connecting institutions, coordinating multinational projects, and driving the IMPRESIVE framework.

    Visit PHDc →

    Application support

    AsPEN

    The Asian Pharmacoepidemiology Network (AsPen) facilitates collaborative research applications across a broad network of researchers, clinicians, and regulators in the Asia-Pacific region.

    Visit AsPEN →

    Research infrastructure

    IMPRESIVE

    IMPRESIVE is the underlying infrastructure ensuring study-preparedness, standardized structures, reusable analytical programs, and seamless evidence workflows.

    Internal rules

    • Proposal & scope review: intended uses, roles, feasibility, and priorities are thoroughly evaluated before any project execution begins.
    • Local data control: our framework guarantees that person-level data remain within approved local environments, sharing only aggregate, privacy-compliant outputs.
    • Ethics & regulatory approval: we strictly follow applicable ethics committees, privacy laws, data-owner requirements, and institutional guidelines.
    • Authorship & publication governance: we ensure clear credit allocation, peer-review responsibilities, and publication expectations are established prior to public release.
    • Conflict-of-interest management: we disclose relevant interests through the research cycle.

    What are the objectives of the IMPRESIVE framework?

    Discover the scientific rationale for advancing cross-country healthcare research across federated data networks.

    Why international studies