Comparative effectiveness research (CER) for medical devices compares outcomes across competing products or care pathways, often using real-world data, rather than testing a single device against placebo or a sham comparator. In wound care, that distinction matters enormously. A cellular and tissue-based product (CTP) marketed as a 361 HCT/P, a human tissue product regulated solely under Section 361 of the Public Health Service Act, reaches the market without any FDA premarket review. A 510(k) clearance means FDA found a product substantially equivalent to a predicate device, usually on the strength of bench and other non-clinical testing. Even a premarket approval (PMA), the one pathway built on clinical trials, compares a product to standard of care, not to its competitors. None of these pathways says anything about how a product performs against the hundreds of other skin substitutes competing for the same diabetic foot ulcer patient. This article walks through how comparative effectiveness research medical device studies are built for CTPs (also called CAMPs) and the adjunctive therapies often used alongside them, like hyperbaric oxygen therapy (HBOT) and negative pressure wound therapy (NPWT), why wound care resists clean comparisons more than most device categories, and what buyers, payers, and manufacturers should scrutinize before they treat a comparative claim as decision-grade evidence.
How Does CER Differ from a Premarket RCT?
A premarket randomized controlled trial (RCT) answers a narrow question: does this device outperform placebo, sham, or standard of care in a defined, often tightly inclusion-criteria-limited population? That’s the evidence base behind most PMA applications and the minority of 510(k) submissions for which FDA requires clinical data. Most 510(k) clearances rest on non-clinical testing, and 361 HCT/Ps require no premarket submission at all. Where a pivotal trial does exist, it’s designed to isolate a single product’s effect under controlled conditions that rarely resemble a busy hospital-based wound center or a home health visit. Comparative effectiveness research asks a different question: how does this product perform against the other marketed alternatives a clinician could reasonably choose, under the conditions clinicians actually practice in.
Head-to-head RCTs count as CER too, but because head-to-head trials across CTP brands are expensive, slow, and commercially unappealing to sponsor, most wound care CER relies on pragmatic or observational designs: claims data, clinical registries, electronic health record (EHR) extracts, or hybrid real-world data platforms. These designs trade the internal validity of randomization for external validity, meaning the findings reflect actual practice patterns, product switching, and site-of-care variation, but they require more careful statistical adjustment to avoid confounding.
CER is also distinct from post-market clinical follow-up (PMCF). PMCF, a requirement under the EU Medical Device Regulation (MDR), is built around a single product’s safety and performance over time, confirming that the device continues to do what its manufacturer claimed. MDR does expect manufacturers to review clinical data on equivalent or similar devices and changes in the state of the art as part of PMCF, but that review exists to update the device’s own benefit-risk assessment, not to show that it outperforms competitors. FDA has no direct PMCF equivalent; its postmarket study requirements, such as Section 522 surveillance orders and PMA post-approval studies, are aimed at specific devices and specific questions. CER benchmarks a product against the alternatives on the market. A manufacturer can have a clean PMCF file showing a device performs consistently over three years and still have no evidence about whether it heals wounds faster or cheaper than the CTP a competitor is selling into the same wound centers. Payers and value analysis committees increasingly want both, but they are not interchangeable, and vendors sometimes blur the line to make single-product surveillance sound like a competitive claim it was never designed to support.
Why Is CER Especially Difficult in Wound Care?
Wound care resists the clean comparisons that make CER tractable in, say, cardiovascular devices or single-indication pharmaceuticals. A diabetic foot ulcer (DFU), a venous leg ulcer (VLU), and a pressure ulcer have different underlying pathophysiologies, different standard-of-care bundles, and different healing trajectories, yet they frequently get lumped into the same comparative dataset because ICD-10 diagnosis coding, which is where etiology lives on a claim, doesn’t always capture it with precision. A study that pools wound types without stratifying by etiology is comparing apples to a fruit salad, and any closure-rate difference it reports may reflect case mix rather than product performance.
Site of care introduces a second layer of confounding that has no clean analog in pharma. The same CTP applied in a hospital-based wound center with weekly sharp debridement and documented offloading compliance will perform differently than that same product applied in a skilled nursing facility (SNF) or a home health visit where debridement frequency, offloading adherence, and even application technique vary widely. Any comparative claim that doesn’t stratify by site of care, or at least control for it, is at risk of attributing a practice-setting effect to the product itself.
Product switching mid-episode compounds the attribution problem. It’s common for a clinician to start a wound on one CTP, see slow progress, and switch to a second or third product before closure. Claims-based studies that assign the entire episode’s outcome to whichever product was billed last, or whichever was used most frequently, systematically misattribute outcomes. Adjunctive therapies add another layer: when a DFU receives a CTP, NPWT, and HBOT during the same episode, a study has to account for each before crediting any one of them with closure. Combined with inconsistent ICD-10, CPT, and HCPCS coding practices across billing systems, isolating a single product’s independent contribution to closure, recurrence, or cost outcomes requires far more careful episode-level reconstruction than most published wound care CER studies actually perform. This is precisely why registry-based wound care research and cross-setting utilization tracking matter more in this category than in most device categories: without granular, longitudinal, site-aware data, comparative claims default to noise.
What Data Sources Make a Wound Care CER Study Credible?
Claims data give you volume and utilization patterns across a wide swath of care settings, which is valuable for seeing how often a product is actually used and where, but claims carry only coarse severity information (ICD-10 codes can record pressure ulcer stage and ulcer depth when they’re coded specifically) and are generally blind to serial wound measurements, wound bed characteristics, and granular clinical detail. A claims-only study can tell you that Product A was billed more often than Product B in SNFs during a given period; it usually cannot tell you why, or whether the patients receiving each product were clinically comparable.
EHR and registry data go the other direction: they capture wound staging, comorbidities, debridement frequency, and offloading documentation with much more clinical fidelity, but they typically cover fewer sites and can be skewed toward whichever health systems or wound center chains contribute data. A registry drawing from twenty hospital-based wound centers on one EHR platform is not automatically representative of national practice, particularly if that platform serves a specific referral pattern or geographic region.
Real-world dashboards that track product utilization across CTPs and wound types, spanning hospital-based wound centers, SNFs, and home visits, close some of this gap by letting researchers observe substitution patterns and site-of-care shifts that neither claims data nor a single-site registry can show on its own. Intellicure Analytics’ Wound Care Industry Dashboard was built for exactly this kind of cross-setting visibility, compiling utilization data across care settings so that a manufacturer or researcher can see how a product’s use shifts when a competing product enters the market, or when a coverage policy changes.
Whatever the source, sample size alone is not the credibility marker readers should default to. A study with a million claims lines but no wound-specific detail can be less informative than a smaller registry with careful staging and outcome adjudication. That applies to every dataset, including ours: ask explicitly how many wound centers or care sites contributed data, whether the dataset is concentrated in a single EHR vendor’s client base, and whether the site mix resembles the setting you actually care about.
Which Outcomes Should a CER Study Actually Measure?
Time to closure and closure rate at a defined week marker, commonly 12 or 16 weeks, are the standard endpoints in wound care CER, largely because they mirror the endpoints used in premarket trials and can be measured directly from EHR and registry documentation. Claims data can only approximate closure, usually by inferring it when wound-related billing stops. But standard doesn’t mean sufficient. Recurrence rates and amputation-free survival are underreported endpoints that matter enormously to payers and to patients with DFUs at risk of lower-extremity amputation, and their absence from a comparative study should prompt questions about why they weren’t tracked or reported.
For health economics and outcomes research (HEOR) teams, payers, and integrated delivery network (IDN) value analysis committees, cost-per-closure and applications-to-closure ratios often carry more decision weight than closure rate alone. A product with a modestly lower closure rate but a substantially lower applications-to-closure ratio may still win on total episode cost, and that’s the framing formulary committees actually use when comparing CTPs on a per-episode basis rather than a per-application basis.
One of the more common ways a weak comparative effect gets overstated is through selective use of relative risk or odds ratios without absolute event rates or confidence intervals. A relative risk reduction of 40% sounds impressive until you learn the absolute event rates were 5% versus 3%, a two-percentage-point difference that may not be clinically or economically meaningful and, without a confidence interval, can’t be distinguished from chance or confounding. Any comparative claim that reports only relative measures, without the underlying absolute rates and a confidence interval, should be treated as incomplete at best.
How Do Payers and Regulators Use CER Findings?
Medicare Administrative Contractors (MACs) have already signaled what evidence they expect for skin substitutes. The local coverage determinations for skin substitutes and CTPs that the MACs finalized in November 2024 would have limited coverage for DFUs and VLUs to products backed by published, peer-reviewed clinical evidence. Those LCDs were withdrawn in December 2025, days before they were set to take effect, leaving Novitas, First Coast, and CGS with their existing skin substitute policies and the other MACs deciding coverage case by case. CMS separately restructured payment for the category through rulemaking, moving nearly all skin substitutes to a single rate of $127.14 per square centimeter beginning in 2026, a rate CMS has proposed to keep for 2027. That makes CER a defensive tool as much as a marketing one: manufacturers without independent comparative evidence showing how their product performs and is utilized relative to the field are poorly positioned the next time an LCD, a payer policy, or a payment rule puts their coverage status on the table.
Post-market expectations in Europe point the same way. Under EU MDR, PMCF plans and evaluation reports are expected to address clinical data on equivalent or similar devices and changes in the state of the art, so a PMCF report that only restates premarket findings without that context is likely to draw more scrutiny going forward, not less. FDA’s postmarket requirements are narrower and more targeted, but manufacturers selling in both markets increasingly need real-world evidence that holds up against comparators.
IDN value analysis committees and group purchasing organizations (GPOs) make formulary and contracting decisions using exactly this kind of comparative utilization and cost data, whether or not the manufacturer supplies it. If a manufacturer shows up to a value analysis committee meeting with single-arm data and a competitor shows up with a comparative effectiveness study or a documented benchmarking report, the committee’s decision is shaped by whoever brought comparative evidence. Manufacturers who sit out CER don’t avoid the comparison, they just cede the narrative to whichever competitor’s data reaches the committee first.
What Should a Skeptical Reader Check Before Trusting a CER Claim?
Start with funding and comparator selection. A sponsor-funded study that compares its own product against a comparator the sponsor chose is structurally different from an analysis built on an independent dataset spanning the full market. Ask who selected the comparator, and whether the comparator was the market leader, a similarly positioned product, or a weaker alternative chosen to flatter the sponsor’s results.
Selection bias deserves the same scrutiny. If clinicians steer more severe wounds, or wounds with better payer coverage, toward a specific CTP, that steering will produce outcome differences that have nothing to do with the product’s actual performance. A credible study addresses this directly, through propensity matching, multivariable adjustment, or at minimum a transparent description of how patients were assigned to each product arm.
Finally, check the publication and disclosure trail. Peer-reviewed, ideally pre-registered studies with named authors, a stated methodology, and an explicit limitations section carry more weight than a branded white paper with no visible statistical methods. A reasonable checklist:
- Is the comparator chosen by an independent data source or by the study’s sponsor?
- Does the study report absolute event rates and confidence intervals, not just relative risk?
- Does the methodology address selection bias or product steering explicitly?
- Is the study peer-reviewed or pre-registered, with named authors and disclosed funding?
- Does it stratify by wound etiology and site of care, or pool them together?
Comparative effectiveness research is only as trustworthy as the data and design behind it, and in wound care the gap between a rigorous study and a marketing document is often invisible until you ask about population, comparator selection, and dataset provenance. Before acting on any comparative claim, whether you’re a manufacturer defending an LCD, a payer weighing coverage, or an IDN committee building a formulary, ask what population, comparator, and dataset actually produced the number. When internal evidence isn’t sufficient to answer that question, an independent real-world data platform or a dedicated Comparative Effectiveness Study engagement fills the gap that a single-site registry or an unpublished white paper cannot. Learn more about our services.