The Synthetic Control Mirage: Why Your Medtech RWD Strategy Will Fail In Latin America

By Julio G. Martinez-Clark, CEO, bioaccess

Medtech companies are facing a strategic contradiction that nobody wants to acknowledge. On one hand, regulatory agencies globally — from the FDA to Health Canada to European authorities — are actively encouraging companies to use real-world data (RWD) and synthetic control arms (SCAs) to accelerate device approvals and reduce patient burden. On the other hand, these same companies are aggressively expanding their clinical trial footprints into Latin America. This region has become increasingly attractive for medtech development due to its diverse patient populations, cost advantages, and growing healthcare infrastructure.

Here's the problem: the advanced, data-hungry methodology required for credible SCA development is fundamentally incompatible with Latin America's fragmented and low-quality real-world data infrastructure. Sponsors are constructing trials across the region without understanding this critical mismatch, and when the data comes due for regulatory submission, they will face a painful reckoning.

This is not a distant problem. Device manufacturers are making trial design decisions right now that will determine their competitive success or failure in the next 18 to 36 months.

Why SCAs Have Become The Expected Norm

Before explaining why SCAs don't work in Latin America, let's review why they've become so attractive in the first place.

Synthetic control arms leverage historical and cross-study patient-level data to construct a statistical comparator arm, eliminating the need for a traditional randomized control group in some settings.¹ The regulatory appeal is straightforward: reduced trial enrollment requirements, shorter timelines, lower costs, and reduced placebo burden — all qualities that appeal to both sponsors and regulators concerned about patient access and ethical obligations.

The FDA has adopted this approach through its Real-World Evidence Program, issuing multiple guidances on the integration of RWE.² Health Canada similarly encourages RWD incorporation into clinical evidence packages.³ In Europe, the Medical Device Regulation (MDR) explicitly contemplates real-world evidence as a valid component of clinical evaluation reports.⁴

For medtech sponsors, this regulatory encouragement has translated into a strategic imperative: build SCAs into your trial designs now, or risk appearing outdated to payers and regulators who increasingly expect sophisticated RWE strategies.

The Latin American Data Problem: Infrastructure Gaps, Not Just Economics

Here's what SCA methodology demands: comprehensive, longitudinal, patient-level data captured in standardized formats across multiple healthcare systems, with consistent definitions, completeness rates above 85%–90%, and temporal data spanning years or decades.

Now consider Latin America's actual data landscape:

Healthcare System Fragmentation. Latin America's healthcare infrastructure is characterized by parallel systems — public (IMSS in Mexico, ISAPRE in Chile, FONASA in Argentina), private insurance, and out-of-pocket providers — that rarely communicate with one another.⁵ Electronic health record (EHR) adoption varies wildly by country and institution. While Brazil and Chile have made significant progress with national digital health initiatives, most other countries lack coordinated data networks. Mexico's healthcare data is distributed across hundreds of unconnected providers. Colombia's system remains primarily paper-based in many regions outside major urban centers.⁶

Data Quality and Standardization. Even where EHRs exist, data quality remains inconsistent. Missing data fields, variable definitions that change between institutions, and incomplete follow-up records are endemic. Latin American healthcare providers have historically not captured the granular, standardized data elements required for SCA algorithms — laboratory values, comorbidity severity scores, medications, and clinical outcomes are frequently documented in unstructured narrative notes rather than in structured data fields.

Regulatory Expectations Mismatch. LATAM regulators (ANVISA in Brazil, COFEPRIS in Mexico, ISP in Chile, INABEM in Argentina) are increasingly sophisticated in their expectations for clinical evidence. However, their RWE guidance documents have not kept pace with the SCA methodologies being endorsed in the U.S. and Europe. When a sponsor submits an SCA constructed from fragmented, low-fidelity data from LATAM, local regulators often view it as insufficient to support their own approval decision, even if the FDA or EMA has accepted the same data.⁷

The Time Horizon Problem. SCA construction typically requires three to five years of retrospective patient data to identify matched comparators and establish robust baselines. In fast-moving medtech spaces (orthopedic, neurotech, structural heart), this historical window may encompass older device generations or superseded surgical techniques. The question becomes: how valid is your synthetic control arm when it's constructed from data generated under clinical practices that have evolved significantly?

The Dangerous Assumption: "Good Data Exists"

Experienced medtech executives will recognize this narrative: "We'll run our trial in LATAM at reduced cost, leverage local RWD for our SCA, and achieve faster regulatory approval globally."

This assumption — that good real-world data exists somewhere in the Latin American healthcare system, just waiting to be accessed and analyzed — is the mirage. It typically emerges from one of three places:

1. Optimistic site selection. Companies identify one or two leading hospitals in São Paulo, Mexico City, or Buenos Aires where EHR adoption is advanced, then extrapolate that capability across their entire trial footprint. When data integration begins 12 months into the trial, they discover that 60% of their sites lack the infrastructure they assumed existed.
2. Vendor promises. Third-party data aggregators and contract research organizations offer to "unlock" Latin American RWD, sometimes with confidence that exceeds reality. They may have curated data sets from select institutions, but these are rarely representative or sufficiently detailed for the construction of rigorous SCA.
3. Regulatory optimism. Companies assume that because LATAM regulators have not explicitly rejected RWD strategies, those strategies will work. Many local regulators lack prior experience with complex RWE submissions and are unprepared to evaluate SCA methodologies that are built on uncertain data.

When SCA Strategies Fail In LATAM: The Likely Scenarios

There are several failure modes to anticipate:

Scenario 1: Regulatory Rejection. A sponsor submits an SCA-supported device application to ANVISA, COFEPRIS, or another LATAM regulator. The regulator's clinical reviewers flag concerns about data heterogeneity, missing patient outcomes, or methodological assumptions that appear questionable given the underlying data quality. The review clock stops while the sponsor conducts supplemental analyses or collects additional primary data. The timeline advantage evaporates.

Scenario 2: Post-Market Surveillance Mismatch. The device is approved in LATAM using an SCA strategy, but the synthetic comparator arm differs from actual real-world performance once the device reaches market. Adverse event signals emerge that were not anticipated because the SCA was constructed from inadequate historical comparators. Regulatory actions follow.

Scenario 3: Global Spillover. A trial executed in LATAM with weak RWD infrastructure undermines the credibility of the company's entire clinical evidence package globally. Regulators in other regions view the SCA with skepticism, knowing it was constructed from fragmented, low-fidelity data. FDA or EMA reviewers may demand additional primary data collection, negating the intended efficiency gains.

Scenario 4: Unplanned Cost Escalation. Recognizing data quality issues mid-trial, the sponsor pivots to supplement the SCA with additional primary data collection. Enrollment expands, timelines extend, and cost savings materialize elsewhere, if at all.

A Realistic Hybrid Framework

This is not an argument against running trials in Latin America or against leveraging real-world data. Both strategies are valuable. Instead, it's a call for honesty about what RWD infrastructure currently exists in the region and how to design trials that work within those constraints rather than assuming constraints away.

Here's a practical framework for medtech leaders:

Step 1: Audit Your Data Reality. Before locking in an SCA strategy, conduct a rigorous site-by-site assessment of available real-world data infrastructure in your target LATAM geography. Specifically:

• What is the EHR adoption rate, and what data elements are standardized?
• What is the availability of historical data for your specific patient population and condition?
• What are the data quality metrics (completion rates, consistency, follow-up duration)?
• What data governance and regulatory approvals will be required to access these records?

This audit typically requires four to eight weeks and should be treated as a hard gate before trial design is finalized.

Step 2: Right-Size Your RWD Expectations. Based on your audit, define what role RWD will realistically play in your evidence strategy:

• Full SCA Integration. If you've identified institutions with mature EHR systems and sufficient historical data depth (three or more years of structured patient-level data with greater than 90% completeness for key variables), an SCA strategy may be viable as your primary comparator approach.
• Hybrid RWD Integration. More commonly, LATAM data support a supplementary role, such as validating trial entry criteria, informing subgroup analyses, or informing post-market surveillance planning. Historical LATAM data might inform your understanding of baseline disease severity or comorbidity prevalence, but it should not anchor your primary statistical strategy.
• Primary Comparator Trial. If you cannot confidently construct an SCA from available LATAM data, default to a traditional randomized comparator arm (concurrent control). Accept the longer timeline and acknowledge that this is the most defensible approach given current regional data infrastructure.

Step 3: Prioritize Data-Rich Geographies. Within LATAM, concentrate your trial volume in countries and institutions where RWD infrastructure is most advanced: Brazil (particularly ANVISA-regulated research centers), Chile (strong public healthcare data systems), and select hospitals in Mexico, Argentina, and Colombia. This geographic concentration enhances your chances of accessing reliable historical data, albeit somewhat limiting your overall enrollment strategy.

Step 4: Invest in Data Standardization. If your trial design requires comparative RWD, allocate budget and timeline to work with your trial sites on data standardization before you need it. This might include:

• implementing standard data model (CDM) translations at trial sites
• establishing data quality validation protocols tied to site regulatory compliance
• training site personnel on structured data capture for your device-specific outcomes.

This up-front investment often pays dividends by the time you're integrating RWD into your statistical analysis.

Step 5: Engage Regulators Early. Before finalizing your RWE strategy, present your plans to relevant LATAM regulatory authorities, particularly if the data quality or methodology represents a novel approach in that jurisdiction. Regulators are more receptive to innovation when sponsors transparently acknowledge limitations and propose concrete mitigation strategies.

The Broader Implication: RWE Maturity Varies Globally

The push toward RWE is real and appropriate. However, the global regulatory ecosystem is not yet uniform in its ability to leverage high-quality, standardized real-world data. Latin America is advancing rapidly — Brazil's digital health initiatives and Chile's integrated public healthcare system represent meaningful progress. But the region remains three to five years behind North America and Europe in terms of data maturity and infrastructure readiness for sophisticated statistical methodologies.

This is not a permanent constraint. In five to 10 years, Latin America's digital health landscape will evolve significantly, regulatory frameworks will mature, and the region will likely become a world-class platform for RWE-driven clinical research.

However, in the next 18 to 36 months, device manufacturers that design trials assuming infrastructure already exists may be making a strategic bet they will regret. The companies that will succeed are those that audit their data reality, right-size their RWE expectations, and design trials that work with current regional constraints rather than against them.

The synthetic control mirage is real. The question is whether your trial design will acknowledge it or fall into it.

References

1. FDA. (2021). Considerations for the use of real-world data and real-world evidence to support regulatory decision-making for drug and biological products. U.S. Food and Drug Administration, Center for Drug Evaluation and Research (CDER).
2. FDA Real-World Evidence Program. (2021). Evaluation of Real-World Evidence Studies. U.S. Food and Drug Administration.
3. Health Canada. (2020). Submission requirements for real-world evidence to support approval of new drugs. Therapeutic Products Directorate (TPD) and Biologic and Radiopharmaceutical Drugs Directorate (BRDD).
4. European Commission. (2017). Medical Device Regulation (MDR), Regulation (EU) 2017/745. Official Journal of the European Union.
5. Pan American Health Organization. (2022). Digital health in the Americas: Building the foundation for health security. PAHO/WHO Regional Office for the Americas.
6. OECD. (2021). Health at a glance: Latin America and the Caribbean 2021. OECD Publishing.
7. National authorities across LATAM (ANVISA, COFEPRIS, ISP, INABEM) do not have published RWE guidance comparable to FDA, EMA, or Health Canada frameworks as of December 2025.

About The Author:

Julio G. Martinez-Clark is co-founder and CEO of bioaccess, a market access consultancy that works with medical device companies to help them do early-feasibility clinical trials and commercialize their innovations in Latin America. Julio is also the host of the LATAM Medtech Leaders podcast: A weekly conversation with Medtech leaders who have succeeded in Latin America. He has a bachelor's degree in electronics engineering (BSEE) and a master's degree in business administration (MBA).