The What and How of Clinical Data Sharing

The Data Silo Crisis: Why Up to 50% of Clinical Trials Go Unpublished

Clinical data sharing is the practice of making clinical trial data accessible to qualified researchers for secondary analysis. It involves sharing everything from individual participant data (IPD) to study protocols and reports, all under strict privacy and ethical controls.

The stakes couldn’t be higher. Research shows that between 25% and 50% of completed clinical trials remain unpublished, representing a massive loss for science. Worse, published articles often fail to report key findings, with 50% of efficacy outcomes and 65% of harm outcomes incompletely reported. This creates dangerous blind spots in our understanding of treatments.

This isn’t just about academic transparency—it’s about patient lives and healthcare costs. When data stays locked in silos, we miss critical safety signals, duplicate expensive trials, and delay life-saving treatments from reaching patients who need them most.

The good news is that the tide is turning. A growing number of secure, ethical data sharing initiatives are proving that this model works, changing how we find and develop new treatments.

I’m Maria Chatzou Dunford, CEO and Co-founder of Lifebit, where I’ve spent over 15 years building platforms that enable secure clinical data sharing across global healthcare organizations. My experience developing federated analytics solutions has shown me how breaking down data silos accelerates drug findy while maintaining the highest standards of privacy and compliance.

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The ROI of Data Sharing: Faster Cures, Safer Drugs, and Unlocked Discoveries

Imagine spending years and millions on a clinical trial, only for the results to sit in a digital vault. That’s what happens without clinical data sharing—and it’s costing lives. When we open up this data, the change is remarkable. We can accelerate scientific discovery, avoid redundant trials, and verify research findings to ensure patients get treatments that truly work.

Benefits for Patients, Researchers, and Public Health

For patients, sharing data honors their contribution to science by multiplying its impact. It leads to:

• Faster access to new treatments: Researchers can build on existing data instead of starting from scratch. For example, re-analyzing data from a trial for a drug that failed for a broad condition might reveal it is highly effective for a specific genetic sub-population, leading to a new, targeted therapy years sooner.
• Improved drug safety: Pooling data from multiple studies reveals rare side effects that a single trial might miss. This comprehensive view helps doctors and regulators make smarter decisions.
• Better understanding of diseases: Shared data creates a richer picture of how conditions work across different populations, geographies, and genetic backgrounds. Our Real-time Evidence & Analytics Layer is designed to extract these critical insights securely.
• Improved public trust: Transparency in research demonstrates that patient data is being used to its maximum potential for public good, which encourages more participation in future trials.

For researchers, data sharing opens doors to new possibilities:

• New research questions: Fresh eyes on existing data can lead to breakthrough discoveries. A cardiologist might spot a cardiovascular risk pattern in cancer trial data, or a geneticist could identify a novel biomarker for drug response in a dataset from a neurology study.
• Increased collaboration: Scientists can combine their expertise and work on the same dataset from anywhere in the world, fostering innovation and breaking down institutional silos.
• Powerful meta-analyses: Access to individual participant data (IPD) allows for the most powerful form of pooled analysis. Unlike meta-analyses based on published summary figures, IPD meta-analyses allow researchers to standardize endpoints, adjust for patient-level differences, and reliably investigate subgroups (e.g., treatment effects by age, sex, or comorbidity), answering questions single trials never could.
• Validation of results: The ability to re-examine data builds confidence in findings and strengthens the entire scientific enterprise. Scientific research consistently shows these benefits compound over time.

The real-world impact on drug safety and public health is revolutionary. Post-market surveillance becomes dramatically more effective, and we can identify rare adverse events by pooling data from many studies. This allows for ongoing optimization of treatment protocols and informs clinical guidelines with the most complete evidence available.

The case of rofecoxib (Vioxx) is a stark reminder of the stakes. The drug was withdrawn from the market after a meta-analysis of trial data—including some that was not publicly available—revealed a significant increased risk of heart attack and stroke that was not apparent from individual studies. Had this data been shared openly and proactively, these safety signals could have been detected far earlier, potentially saving thousands of lives. Similarly, pooled analyses of data for the diabetes drug rosiglitazone later revealed cardiovascular risks, proving that transparent data sharing is essential for patient safety.

The 5 Landmines of Data Sharing: How to Avoid Privacy Breaches and Flawed Science

Clinical data sharing isn’t without risk. We’re handling highly sensitive information, and mishandling it could expose patients, erode public trust, and undermine scientific progress. These challenges demand smart solutions and careful planning.

Core Principles for Responsible Sharing

A successful data sharing initiative is built on a solid foundation of core principles:

• Protecting participant privacy: This is the non-negotiable bedrock of all data sharing. It means honoring the trust patients place in researchers through robust technical and procedural safeguards.
• Ensuring data security: Modern security requires multiple layers of defense, including end-to-end encryption, strict access controls, continuous monitoring, and regular security audits.
• Maintaining data integrity: Incomplete, inaccurate, or poorly documented data is not just useless—it’s dangerous. Quality control and comprehensive metadata are moral imperatives.
• Transparency in governance: Clear, open, and fair processes for how data access decisions are made build trust among all stakeholders, from patients to researchers to sponsors.
• Fair access for qualified researchers: Independent review panels must act as impartial gatekeepers to ensure only legitimate, scientifically sound research moves forward.

These principles align with the FAIR Guiding Principles: a framework designed to make data Findable, Accessible, Interoperable, and Reusable.

The Balancing Act: Utility vs. Anonymization

Here’s the core challenge: the more we strip away identifying information to protect privacy, the less useful the data becomes for detailed scientific analysis. With just a birthdate, gender, and 5-digit zip code, up to 87% of the U.S. population can be uniquely identified. This means simple anonymization is not enough.

Sophisticated techniques are required. K-anonymity, for example, ensures that any individual in a dataset is indistinguishable from at least ‘k-1’ other individuals based on their quasi-identifiers (like age and zip code). A more advanced approach is differential privacy, which provides mathematical guarantees of privacy by adding precisely calibrated statistical noise to the results of database queries. This allows researchers to analyze aggregate patterns without being able to isolate information about any single individual. The risk of re-identification is never zero, so our protection methods must constantly evolve to stay ahead of data linkage techniques.

Primary Risks in Clinical Data Sharing

Beyond privacy, several other risks require careful management:

• Misinterpretation of data: Without the full context provided by study protocols, statistical analysis plans, and detailed metadata, secondary researchers can easily misunderstand nuances in the data, leading to flawed conclusions that could misinform clinical practice.
• “Research parasitism”: Original investigators often worry about others publishing findings from their hard-won data without proper credit or collaboration. This valid concern must be addressed with fair governance, such as embargo periods or models that encourage or require collaboration.
• Data dredging: Also known as “p-hacking,” this is the practice of running countless statistical tests on a dataset until a statistically significant finding emerges by chance. This pollutes the scientific literature with false positives and can be mitigated by requiring pre-registration of analysis plans.
• Legal and ethical liabilities: Regulations like the EU’s GDPR and the U.S.’s HIPAA carry massive fines for privacy breaches. Navigating the complex web of international data protection laws is a major challenge.
• Loss of competitive advantage: Pharmaceutical sponsors invest hundreds of millions in trials and have legitimate concerns about protecting proprietary information and intellectual property. Sharing models must respect these commercial interests while still advancing public health.

These risks are manageable with the right technology, governance, and commitment. The key is to address them systematically from the start.

The Federated Future: How Lifebit Delivers Global Insights Without Moving Sensitive Data

The dream of global clinical data sharing is now a reality, but it requires technology that can handle massive, sensitive datasets securely and compliantly. Traditional data sharing approaches are often too restrictive to be useful or too open to be safe.

At Lifebit, our federated platform solves this puzzle by bringing the analysis to the data, rather than moving sensitive information around.

How Lifebit Enables Secure, Global Data Sharing

Our federated AI platform works by allowing data owners to keep full control of their datasets while enabling powerful collaborative research. Instead of asking hospitals to send you their data—a slow and risky process—you send your analysis to run inside their secure environment. The computation happens locally, and only the aggregated results come back to you.

This federated analytics approach changes everything. Data owners maintain complete custody, while researchers get the insights they need. Our Real-time Evidence & Analytics Layer (R.E.A.L.) delivers immediate insights and AI-driven safety surveillance, providing the speed modern pharmacovigilance demands. We also provide built-in harmonization capabilities to make disparate data formats work together seamlessly.

DISCOVER LIFEBIT PLATFORM to see how this technology can revolutionize your research.

Feature	Open Access (e.g., public registries)	Controlled Access (e.g., traditional repositories)	Federated Access (Lifebit’s approach)
Data Location	Centralized, publicly available	Centralized, secure repository	Distributed, at source
Data Movement	Downloadable	Limited download, often in secure enclave	None (analysis moves to data)
Privacy Protection	High anonymization/aggregation	Vetted users, DUAs, anonymization	Data never leaves owner’s control
Data Utility	Lower (due to heavy anonymization)	Moderate to High	High (more granular data can be used)
Re-identification Risk	Moderate to High (if not well-anonymized)	Low (due to controls)	Very Low (data remains protected)
Access Control	Minimal	Strict (review boards, contracts)	Strict (owner-defined policies)
Interoperability	Varies	Requires harmonization by platform	Built-in harmonization across sources
Scalability	Limited by central storage	Limited by central storage	Highly scalable, distributed

Ethical and Legal Guardrails

Navigating the legal landscape of clinical data sharing is complex. Regulations like GDPR in Europe and HIPAA in the U.S. impose strict rules and heavy penalties for non-compliance. Our Trusted Research Environments are specifically designed to steer this maze. These secure, audited spaces allow researchers to analyze sensitive data without ever seeing the raw information directly, dramatically reducing re-identification risks and ensuring compliance.

Practical Tools for Effective Sharing

Technology alone isn’t enough. We integrate practical tools to streamline the process:

• Data sharing agreement templates and standardized informed consent language cut through legal problems.
• Data dictionaries and the availability of study protocols and statistical analysis plans provide crucial context for secondary researchers.
• Adherence to standards from the Clinical Data Interchange Standards Consortium (CDISC) ensures datasets are compatible and ready for analysis.

By making it easier to share data responsibly, we empower more researchers to contribute to life-saving breakthroughs.

The Mandate Is Here: How Regulators and Journals Are Forcing Data Transparency

The shift toward clinical data sharing is being driven by a powerful coalition of regulators, journals, and funders. They are creating an ecosystem where transparency isn’t just encouraged—it’s becoming mandatory.

The Role of Regulatory Bodies: FDA, EMA, and Health Canada

Regulatory agencies are now the heavyweight champions of data sharing, transforming it from a scientific ideal into a compliance necessity.

• The FDA Final Rule for Clinical Trials Registration and Results Information Submission implements Section 801 of the Food and Drug Administration Amendments Act (FDAAA). It legally requires the registration and submission of summary results for applicable clinical trials of FDA-regulated drugs, biologics, and devices on ClinicalTrials.gov. Non-compliance can result in significant civil monetary penalties.
• The European Medicines Agency (EMA) went further with its groundbreaking Policy 0070, which mandated the proactive publication of full, redacted clinical study reports (CSRs) for new medicines submitted for marketing authorization. While its application was temporarily paused during the pandemic (except for COVID-19 products), its existence marked a paradigm shift toward proactive transparency, and its principles continue to shape the future of European regulation.
• Health Canada has followed suit with its own Public Release of Clinical Information (PRCI) initiative, which, similar to the EMA’s policy, requires the public release of clinical information from drug submissions and medical device applications after regulatory decisions are made.

These actions, supported by public registries like ClinicalTrials.gov and the EU Clinical Trials Register, have fundamentally shifted the landscape. Transparency is now a core regulatory requirement.

How Medical Journals (ICMJE) Drive Data Sharing Mandates

The International Committee of Medical Journal Editors (ICMJE), whose members include the world’s most prestigious medical journals, wields enormous power over academic practice. Its recommendations now require authors to include a data sharing statement upon submission. No statement, no publication. This policy forces researchers to plan for data sharing from the very beginning of a project. The statement must specify: whether individual de-identified participant data (IPD) will be shared; what specific data will be shared; whether other documents like the protocol or statistical analysis plan will be available; when and for how long the data will be accessible; and the criteria for access.

This policy promotes reproducibility, as independent researchers can re-analyze published findings. It also helps reduce publication bias by creating a mechanism and expectation for sharing all results, whether positive, negative, or inconclusive.

Incentivizing Researchers and Institutions

Mandates are effective, but lasting change also requires the right incentives.

• Academic credit for data sharing is a growing movement, with systems being developed to recognize data generation and curation as valuable scholarly outputs, equivalent to publications in promotion and tenure decisions.
• Grant funding requirements are now a powerful lever. The 2023 NIH Data Management and Sharing (DMS) Policy is a landmark example. It requires all research funded by the NIH that generates scientific data to include a robust plan for how that data will be managed and shared. This expands the requirement from a few large-scale projects to the entire NIH research portfolio.
• Reducing the burden of sharing through standardized templates, clear guidance, and powerful platforms like federated analytics systems removes the technical and administrative barriers that once made researchers reluctant to share.

This multi-pronged approach is building a comprehensive framework for responsible clinical data sharing.

Your Top 3 Data Sharing Questions, Answered

We get it – clinical data sharing can feel overwhelming. Here are answers to the most common questions we hear.

How is patient privacy protected when data is shared?

Patient privacy is the absolute foundation of responsible data sharing. Protection is achieved through multiple layers working together:

• Sophisticated Anonymization: We use advanced de-identification techniques to remove or mask both direct identifiers (name, address) and indirect identifiers (rare demographic combinations) that could be used to re-identify someone.
• Controlled Access Environments: Our Trusted Research Environments create secure digital labs where researchers can analyze data without ever downloading it. They can run experiments, but only take home aggregated results, not the raw data.
• Legally Binding Agreements: Every researcher signs a Data Use Agreement (DUA) that legally prohibits any attempt at re-identification and enforces strict security protocols.
• Independent Review: Before access is granted, an independent panel vets every research proposal to ensure its purpose is legitimate and ethical.
• Federated Technology: Our platform’s federated approach is the ultimate protection. Sensitive data never moves from its secure source. Instead, analytical tools travel to the data, process it locally, and return only non-sensitive results.

What is the difference between sharing summary data and individual participant data (IPD)?

This distinction is critical. Summary data is the aggregated information you see in published papers (e.g., “65% of patients improved”). It gives a high-level overview.

Individual participant data (IPD) is the complete, patient-level dataset: every single data point collected for each person in a study. IPD is far more valuable because it allows researchers to:

• Ask new questions the original study didn’t consider.
• Validate original findings by re-running the analysis.
• Combine data from multiple studies for more powerful meta-analyses.

While IPD requires much stronger privacy protections, its scientific value is enormous when handled correctly.

Can researchers get academic credit for sharing their data?

Yes, absolutely. The culture of academic medicine is shifting rapidly. For decades, only publications were rewarded, which created an incentive to hoard data.

That’s changing. Major funders like the NIH now require data sharing plans. Universities are updating promotion and tenure criteria to recognize data sharing as a valuable scholarly achievement. The concept of “data citation” is also gaining traction, allowing data generators to receive formal credit when their datasets are used by others.

Researchers who accept clinical data sharing today are positioning themselves at the forefront of this cultural shift, building reputations as collaborative scientists who advance the entire field.

The Choice Is Yours: Lead the Data Revolution or Get Left Behind

The move toward clinical data sharing is a fundamental shift in medical research. We’ve come a long way from a world where up to half of all clinical trials were never published and critical safety data was buried. The cost of data silos was never just about wasted research dollars—it was measured in human lives.

Today, a complete ecosystem change is underway. Regulators like the FDA and EMA are demanding transparency. Top medical journals require data sharing statements. Academic institutions are finally rewarding data sharing as a scholarly achievement.

The benefits are no longer theoretical. We are seeing faster drug development, better safety monitoring, and more personalized treatments because data is finally flowing securely.

Challenges like protecting privacy while maximizing utility remain, but they are now problems we are actively solving with technology, not excuses for inaction. This is where our federated approach at Lifebit becomes a critical enabler. By bringing analysis to the data, we eliminate many of the privacy risks that have historically held back progress.

The future of medical research is collaborative and transparent. We are creating a world where every clinical trial contributes its full value to human health. The revolution in clinical data sharing is here, and the technology to make it secure, compliant, and powerful is ready. The only question is whether you’ll be part of leading it.

Find how to securely share and analyze clinical data and join us in building the future of collaborative medical research.