Data
We first assess the scope of the data, its availability and access policies.
We evaluate whether the required population, exposure, outcomes, covariates, time zero, and follow-up are identifiable.
Home / Why international studies
The programme objective
Conducting reliable multinational studies for long-term impacts
The objective of IMPRESIVE is first to assess the readiness of available data environments to answer shared research questions, building a bridge that can ensure future collaborations and long-term impacts.
Assessing local needs
Before initiating the framework, we first assess readiness, to understand what your study needs and identify how your data environment can support those needs.
A converted database is not automatically ready for every question. Readiness is study-specific and should be reassessed when the protocol, data period, definitions, or operating environment changes.
Method
We then assess the comparability between databases, especially regarding common definitions.
We assessed whether quality checks, and analytic programs can be implemented consistently.
Collaboration
We assess the terms and conditions of the collaborative work, how the local teams and governance processes support distributed execution and permitted output sharing.
Interpretation
Finally, we identify whether differences can be understood in the context of coding, populations, coverage, and healthcare practice.
Enabling analysis across sites without centralizing source data
We aimed to execute common analytic programs within each partner’s approved secure environments to ensure person-level data remain local, while only governance-permitted outputs are returned for review and integration.
What is shared
Common question, protocol, specifications, analysis program, QA logic, and agreed output structure.
What stays local
Person-level source data, local access controls, data-owner approvals, and environment-specific implementation detail.
What returns
Permitted aggregate results, QA summaries, implementation notes, and limitations needed for interpretation.
The expected impacts of the IMPRESIVE framework
Scientific and academic impact
Harmonization · Reproducibility · Comparability
We align data and methods to produce transparent, reproducible, and comparable multinational evidence.
IMPRESIVE uses harmonized definitions, a common data model, shared protocols, and common analytical programmes across participating countries. This reduces avoidable differences in study implementation, improves transparency and reproducibility, and enables meaningful comparison of results across populations and healthcare systems.
Clinical, policy, and societal impact
Timeliness · Transportability · Evidence equity
We generate timely and privacy-preserving evidence for clinical, regulatory, and policy decisions.
IMPRESIVE combines multinational data with clinical expertise to accelerate evidence generation for drug safety and healthcare decisions. Its distributed approach keeps patient-level data within each country, while cross-country analyses assess whether findings are transportable across healthcare settings and improve evidence representation for Asian and other underrepresented populations.
Economic, industry, and technological innovation impact
Cost reduction · Reuse · Innovation readiness
We reuse data and analytical infrastructure to reduce research costs and support scalable innovation.
IMPRESIVE reuses validated data structures, cohorts, quality checks, and analytical programmes across studies and countries. Although initial infrastructure requires investment, reuse reduces duplicated work, shortens study timelines, and lowers the cost of subsequent research. The structured environment can also support trial feasibility assessment, external model validation, and future data-driven innovation.
Education and global development impact
Capacity building · Participation · Sustainability
We build shared skills and standards so more countries can participate in multinational research.
IMPRESIVE converts its experience in data mapping, validation, and analysis into shared guidance and training for researchers, clinicians, data personnel, and regulators. This creates a common research language, lowers barriers for emerging partners, and enables participating countries to contribute not only data but also to study design, analysis, and interpretation.
The “why” is only the beginning
See the eight-step process that makes a multi-database study consistent and reproducible.