Clinical evaluation without pitfalls — the practice-oriented checklist for MDR

This blog post will explain the key questions that an MDR-compliant clinical evaluation must answer, from the precise definition of the intended purpose to the design of a robust Clinical Evaluation Plan (CEP), the selection and evaluation of suitable clinical data sources and the choice of the right evaluation route, to the systematic integration of PMS/PMCF data, reproducible literature research, measurable formulation of claims and active lifecycle management of the CER. 

Abbreviations

Underlying regulations, standards and guidelines

EU Regulation 2017/745 (MDR)

MDCG 2020-1

MDCG 2020-5

MDCG 2020-6

MEDDEV Guideline 2.7/1 Revision 4

Draft of ISO/DIS 18969

1 Introduction

Under the MDR, clinical evaluation is not simply an item on a to-do list to be completed and filed away. It is the central, dynamic instrument for ensuring the safety, performance, and clinical benefit of a product throughout its entire life cycle, and therefore a key point of review for notified bodies and regulatory authorities.

This blog post provides you with a manageable structure: a concise overview of the most important review and decision-making areas, linked to practical tips, quick checks, and requirements. Use it as a work guide: review, document, and fill in any gaps.

2. Practical guide to clinical evaluation

2.1 Everything begins with the intended purpose

Every clinical evaluation begins with a precise definition of the intended purpose. This is not just a formal slogan, but the benchmark against which the entire evidence strategy is measured: What clinical data do you need? Which patient group is affected? In what application context (indications, users, setting, duration of use) is the product used? Only when the intended purpose, information requirements (IFU), marketing materials, and clinical evaluation guidelines (CER) speak the same, unambiguous language can claims or endpoints be meaningfully substantiated. A common mistake is an overly broad or inconsistent intended purpose: this leads directly to contradictory data requirements, vague claims/endpoints, and avoidable audit findings. Therefore, first check whether the intended purpose is formulated identically in the CEP, CER, IFU, and other documents, and consistently correct any discrepancies.

2.2 The CEP is the timetable

Clinical evaluation doesn't begin with the Clinical Evaluation Report (CER), but with the Clinical Evaluation Plan (CEP). A robust CEP defines how you intend to demonstrate clinical safety and performance: It includes a description of the product and its intended purpose, precisely formulated claims and measurable endpoints, the planned data sources (in-house studies, literature, PMS/PMCF, equivalence data), the evaluation and analysis methodology, and an update and trigger strategy for CER revisions. Without this guidance, your CER will be reactive, incomplete, and difficult to defend.

2.3 What really counts as a clinical data basis?

“We have clinical data” is not a sufficient statement. The quality, relevance, evidence, and origin of the data are crucial.

Under the MDR, this primarily includes clinical trials with the product itself, systematically evaluated scientific literature on the product, structured PMS and PMCF data, and, only under strict conditions, equivalence data. Crucially, you must clearly document for each data source used why it is suitable for answering your claims.

2.4 Choosing the right route — strategically and with justification

One of the most important strategic decisions is the choice of evidence route: proprietary clinical data, equivalence, or performance/verification-based argumentation according to Article 61(10). Proprietary clinical data often provide the strongest methodological foundation. The equivalence route remains possible but has become considerably more difficult: technical, biological, and clinical similarity must be demonstrated in detail and verifiably; furthermore, you need access to the underlying data. The performance route can be appropriate for less risky, non-invasive products but requires a sound, rational justification for why clinical data are not necessary. Therefore, specify the chosen route in the CEP (Commissioned Evaluation Process).

2.5 State of the Art as a benchmark

The State-of-the-Art (SotA) chapter in the CER must not simply be a compilation of studies. It must function as a benchmark: medical SotA (guidelines, best available therapies, expected clinical outcomes) and technical SotA (comparable technologies and performance standards) must be analyzed separately. From this analysis, you derive quantifiable reference values, complication rates, measurement accuracies, and performance ranges against which your product is positioned. In this way, the SotA analysis becomes the basis for realistic, verifiable claims and simultaneously reveals where evidence gaps exist and which PMCF (Product Life Cycle Criteria) questions should be prioritized.

2.6 Literature review: systematic, reproducible, verifiable

A proper literature search is both plannable and reproducible. A predefined search plan with inclusion and exclusion criteria, databases, search strings, and timeframes is essential. Subsequently, the screening process must be transparently documented in a search log (title/abstract → full text), and the critical evaluation (bias, relevance of endpoints) must be traceable. Without this systematic approach, the literature search is not verifiable.

2.7 Claims: measurable, traceable, linked

Clinical claims are not advertising slogans; they must be objectively measurable. Categorize claims into safety, performance, and benefit claims, and for each claim, provide a claim ID, a precise formulation, and a measurable endpoint. Link claims in a traceability matrix to their corresponding endpoints and supporting evidence. This is the only way to prevent discrepancies between IFU statements, marketing messages, and CER claims—a classic reason for audit findings.

2.8 PMS and PMCF: the engines of the CER lifecycle

PMS and PMCF are two sides of the same evidence loop: PMS continuously collects feedback from the field (vigilance, complaints, trend data), PMCF specifically provides clinical answers to open questions and fills evidence gaps that may have existed at the time of CE marking or that may have arisen over time.

Together they keep the CER “alive”, PMCF data confirm or refine claims, provide reliable incidence rates and drive benefit-risk reassessments; PMS trends show where PMCF is needed at all.

In short: PMS shows what happens; PMCF explains why and how strongly; the CER is where these findings are versioned, justified and translated into action.

2.9 CERR lifecycle: check regularly, update immediately if necessary

The MDR does not require a rigid review schedule for all products, but it does stipulate that CERs must be actively maintained. For Class III and implantable products, an annual update is explicitly required; for other classes, a risk-based approach with regular reviews applies (often every 2–3 years for Class IIa/IIb, and for Class I at least at an appropriate interval, e.g., up to 5 years, or sooner if relevant signals emerge). Crucially, in addition to periodic reviews, clearly defined triggers must exist: PMS trends, changes to the product or product family, or new risks.

2.10 Consistency in documentation: CER, IFU, RMF and marketing in harmony

Inconsistencies between the CER, IFU, Risk Management File, PMS, and marketing are among the most frequent audit findings. Therefore, conduct systematic consistency checks: Verdict and consistency of purpose across all documents; agreement between the risks identified as clinically relevant in the RMF and the risks discussed in the CER; and a clear mapping of all IFU references to data supported by the CER. Marketing claims may only be used if they are linked to an explicit, documented chain of evidence.

2.11 Practical Quick Checks

Before you release a document, you should keep the following points in mind:

✔ Is the intended purpose consistent?

✔ Does a final CEP exist with clear claims and measurable parameters?

✔ Is a reproducible literature search protocol available?

✔ And lastly: Is every revision versioned, justified, and technically approved?

3. Conclusion

A robust, MDR-compliant clinical evaluation is not achieved solely through lengthy reports, but through rigorous planning (CEP), methodological diligence (reproducible literature search, clean data specification), ongoing real-world evidence (PMS/PMCF) and complete traceability.

4. How we can help you

We provide pragmatic support throughout the entire clinical evaluation lifecycle—from strategic planning to audit-ready documentation. Our goal is to design your CER processes to be MDR-compliant, methodologically sound, and practically applicable. We combine regulatory expertise with practical project and study know-how to transform requirements into genuine, defensible evidence.

Specifically, we can support you with, for example:

• Gap analyses of your existing CEP/CER/PMCF documentation, including prioritized action planning.
• Creation and review of CEPs and CERs.
• Methodology decision & study design: We define endpoints, populations, statistical plans and monitoring concepts for clinical trials or PMCF studies.
• Literature review & evidence appraisal: systematic search, quality assessment and integration into the benefit-risk analysis.
• PMCF conception and implementation.

Want to know more? Contact us for a free initial consultation!

You can get a free initial consultation here: free initial consultation

Clinical evaluation without pitfalls — Common errors in CER and how to avoid them

This blog post provides a concise and practical overview of the typical errors in clinical evaluations (CERs) under the MDR that repeatedly lead to audit findings and requests for further information, how these weaken the evidence for claims and the benefit-risk profile — and, most importantly, what concrete measures and priorities you can use to quickly and sustainably eliminate these pitfalls.

Abbreviations

Underlying regulations, standards and guidelines

EU Regulation 2017/745 (MDR)

MDCG 2020-1

MDCG 2020-5

MDCG 2020-6

MEDDEV Guideline 2.7/1 Revision 4

Draft of ISO/DIS 18969

1 Introduction

Errors in clinical evaluations (CERs) are not mere formalities; they have immediate regulatory and operational consequences. Inadequate methodology, lack of traceability, or outdated data regularly lead to audit findings, can trigger follow-up requirements, and, in the case of initial clinical evaluations, delay approval processes. Against the backdrop of the MDR requirements (especially Article 61 and the PMCF/PMS requirements), the clinical evaluation is therefore not just a "document," but a dynamic, evidence-based management tool that must be maintained throughout the entire product lifecycle.

In this article, we systematically summarize the most common errors we encounter in reviews and gap analyses: from unclearly formulated claims/endpoints and measurable parameters and missing CEPs, to unclear literature searches and insufficiently substantiated equivalence claims, to a lack of traceability between CER, IFU, and label, as well as unstructured benefit-risk analyses. For each identified problem, we provide practical countermeasures, concrete steps, template recommendations, and prioritizations for rapid benefit.

2. Error Overview — The Top Pitfalls at a Glance

Here is a concise summary of the most common pitfalls in clinical assessments — each with a brief description and a direct countermeasure, so you know immediately what to do.

This overview helps to set priorities: It is best to start with the points that have the greatest audit risk and the highest impact on claims — typically traceability, claims definition, SoTA (derive measurable parameters!) and PMS integration.

3. Detailed error analysis & countermeasures

Below, we'll go through each top pitfall individually: briefly outlining the problem, explaining why it's critical, providing concrete, immediately actionable countermeasures, and concluding each section with a short checklist of 3-5 items that you can quickly tick off. The recommendations are pragmatic—the goal is audit-proofness, traceability, and practical implementation.

3.1 Treat CER as a one-off document

Problem & Impact: The CER is only created for approval purposes and then "filed away." This results in the loss of new insights from PMS/PMCF or the literature; claims may become outdated, and auditors may criticize the lack of lifecycle processes.
Countermeasures (specific):

• Define a CER lifecycle in the CEP: Review intervals (e.g., annually for Class III/implantable, risk-based for other classes) and ad-hoc triggers (e.g., significant PSUR findings, new guidelines).
• Implement a change log: versioning, date, trigger, responsible party, brief description of the change.

Mini-check:

• Review interval documented? ✔
• Is a trigger list available? ✔
• Change log/versioning available? ✔

3.2. Unclear or missing clinical claims/endpoints

Problem & Impact: Without clear, measurable claims, you cannot gather targeted evidence or meaningfully define endpoints.

Countermeasures (specifically):

• Formulate claims using the SMART criteria (Specific, Measurable, Achievable, Relevant, Time-bound). Example: "Reduces A-rate by X% within 30 days vs. standard.".
• Assign specific measurable parameters, metrics (numerator/denominator) and acceptance criteria to each claim/endpoint.
• Define the claim formulations in the CEP and maintain a claim/endpoint register file (claim/endpoint ID, formulation, measurable parameter, document list, status).

Mini-check:

• Are all claims/endpoints formulated in the SMART way? ✔
• Does each claim/endpoint have an associated measurable parameter? ✔

3.3 Weak State-of-the-Art (SotA) Analysis

Problem & Impact: If SotA remains merely descriptive, the benchmark for substantiating claims or improvements is lacking.
Countermeasures (specific):

• Derive measurable benchmarks (parameters) from SotA (e.g., mean complication rate, measurement deviation). These benchmarks define your target variables.

Mini-check:

• Benchmarks derived and documented? ✔
• SotA → CER/CEP issues linked? ✔

3.4. Excessive reliance on equivalence

Problem & Effect: Insufficiently documented equivalence leads to requests for further information from notified bodies; clinical data are then unusable.
Countermeasures (specifically):

• Create a complete equivalence dossier with three building blocks: technical equivalence (design, dimensions, functions), biological equivalence (materials, surfaces, contacts), clinical equivalence (intended purpose, indication, population, users).
• Systematically evaluate differences: small/neutral vs. relevant → if relevant, plan your own data (study or PMCF).

Mini-check:

• Is the equivalence dossier (tech/biol/clin) complete? ✔
• Differences assessed & documented? ✔

3.5 Literature search without reproducibility / traceability

Problem & Impact: Missing search protocols and undocumented selection criteria make results unreproducible — auditors demand reproducibility.
Countermeasures (specific):

• Use a written search log (databases, search terms, time period, date of search).
• Use digital tools that make it easier for you to document your literature search.

Mini-check:

• Search log available? ✔

3.6. PMS / PMCF data are not integrated

Problem & Impact: When field data remains isolated, benefit-risk arguments become obsolete.
Countermeasures (specific):

• Define a clear process in SOPs for how PMS and PMCF results are regularly reviewed, evaluated, and fed back into the clinical assessment.
• Include sections for evaluating the PMS and PMCF results in the CER template.
• Establish a fixed sequence: First, evaluate PMS/PMCF → then update the CER so that the new data can be integrated consistently.

Mini-check:

• PSUR/PMS events are documented in the CER. ✔
• PMCF results are used to confirm or adjust clinical statements.✔
• The benefit-risk assessment is regularly updated based on current field data. ✔

3.7. No structured benefit-risk analysis

Problem & Impact: Narrative perspectives are subjective; a lack of data makes decisions difficult.
Countermeasures (specific):

• Define a fixed methodology for benefit-risk assessment, e.g., using structured tables, scoring models, or qualitative categories with clear evaluation criteria.
• Explicitly link benefit and risk parameters to clinical data, including clinical trials, literature, PMS and PMCF results.
• Document assumptions and weightings in a transparent manner so that decisions remain consistent even with updates to the CER.

Mini-check:

• Benefits and risks are clearly defined and presented in a structured comparison. ✔
• Clinical data and PMS/PMCF results are demonstrably included in the assessment. ✔
• The benefit-risk assessment is reproducible and comprehensibly reasoned. ✔

3.8. Inconsistent documents (CER vs. IFU vs. Claims/Endpoints)

Problem & Impact: Discrepancies between IFU/Marketing and CER lead to audit findings.
Countermeasures (specifically):

• Establish a systematic reconciliation process that ensures all clinical claims/endpoints in IFU, marketing materials and technical documentation are supported by the clinical evaluation.
• In the CER, define a “claim reference” that explicitly lists all essential clinical claims and links them to the underlying data.

Mini-check:

• All clinical claims/endpoints in IFU and marketing are documented and substantiated in the CER.✔
• Indication, target population, and purpose are consistent across all documents.✔

3.9. No clear clinical evaluation strategy (route missing)

Problem & Impact: Arbitrary data collection without a goal leads to gaps and unnecessary effort.
Countermeasures (specific):

• Establish the assessment route in the CEP, justify the choice with risk and data situation, and define milestones.

Mini-check:

• Assessment route documented in the CEP? ✔
• Is there a fallback plan? ✔

 4. Conclusion

Under the MDR, clinical evaluation is no longer a static final document, but rather the central, evidence-based management tool for the safety, performance, and clinical benefit of a product throughout its entire lifecycle. Errors in CEP/CER processes—such as unclear claims/endpoints, lack of reproducibility of the literature review, insufficiently substantiated equivalence assumptions, or the failure to integrate PMS/PMCF data—regularly lead to audit findings and requests for further information, weakening the defense of your claims. Many of these deficiencies can be avoided through clear processes, transparent methodology, and consistent documentation.

Key recommendations for action can be summarized thematically:

Planning & Claims/Endpoints: Begin the clinical evaluation with a complete, finalized Clinical Evaluation Plan (CEP). Define claims early and precisely (SMART: specific, measurable, traceable) and link each claim/endpoint to concrete endpoints, data sources, and acceptance criteria. The CEP phase establishes the data route (own studies, equivalence, performance data) and determines if and when a clinical investigation is required. Early involvement of the clinical lead, biostatisticians, regulatory affairs, and quality assurance prevents later plan changes and reduces regulatory risks.

Evidence Building & SoTA: Conduct a systematic, reproducible literature search and document the search protocol, inclusion/exclusion criteria, and screening process (PRISMA style). Structure the state-of-the-art analysis along measurable, clinically relevant parameters and derive benchmarks and gaps from them. Use an extracted dataset as the source of truth (numerator/denominator, follow-up, limitations), not just narrative summaries.

Methodology & Equivalence: Critically assess the quality of each data source (bias, follow-up, endpoint coherence). If you intend to use equivalence data, provide a complete, verifiable equivalence matrix covering technical, biological, and clinical comparison points; document differences and their relevance. If robust equivalence is lacking, plan an alternative evidence route (e.g., prospective cohort, PMCF).

Benefit-Risk & Traceability: Work with a data-driven benefit-risk matrix in which benefits and risks are quantified, weighted, and supported by concrete data sources. Establish a traceability matrix that links claims, endpoints, the associated studies/data, and the relevant CER, IFU, and marketing sections. This is the only way to avoid inconsistencies and quickly answer auditor questions.

PMS/PMCF Integration: PMCF and PMS are not separate "reports" but rather permanent sources of evidence for the CER. Define the workflow "PMS/PMCF → CER Update" in your SOPs. Ensure that PSUR/PMS/PMCF results automatically trigger a CER review task.

5. How we can help you

We provide pragmatic support throughout the entire clinical evaluation lifecycle—from strategic planning to audit-ready documentation. Our goal is to design your CER processes to be MDR-compliant, methodologically sound, and practically applicable. We combine regulatory expertise with practical project and study know-how to transform requirements into genuine, defensible evidence.

Specifically, we can support you with, for example:

• Gap analyses of your existing CEP/CER/PMCF documentation, including prioritized action planning.
• Creation and review of CEPs and CERs.
• Methodology decision & study design: We define endpoints, populations, statistical plans and monitoring concepts for clinical trials or PMCF studies.
• Literature review & evidence appraisal: systematic search, quality assessment and integration into the benefit-risk analysis.
• PMCF conception and implementation.

Want to know more? Contact us for a free initial consultation!

You can get a free initial consultation here: free initial consultation

Back to the Basics - What is a clinical evaluation according to MDR regulations? Fundamentals & Requirements

This blog post provides a concise and practical overview of what a clinical evaluation according to the MDR (Medical Device Regulation) entails, the relevant legal and regulatory frameworks, and how the entire process works step by step. Using the three possible evidence routes as examples, we demonstrate which data is crucial, how to continuously update your clinical evaluation, and which typical mistakes to avoid.

Abbreviations

Underlying regulations, standards and guidelines

EU Regulation 2017/745 (MDR)

MDCG 2020-1

MDCG 2020-5

MDCG 2020-6

MEDDEV Guideline 2.7/1 Revision 4

Draft of ISO/DIS 18969

1. Introduction

The clinical evaluation is the core of every MDR-compliant Technical Documentation (TD) – it explains why a medical device is safe and effective. Precisely because the MDR places high demands on evidence, timeliness, and traceability, the clinical evaluation must never be considered a one-off final document. Rather, it is a dynamic document that requires continuous updating, linking preclinical and clinical study data with real-world evidence from PMS/PMCF, thus supporting the entire product lifecycle.

For manufacturers, this means that clinical evaluation must be strategically planned from the outset—from the Clinical Evaluation Plan (CEP) and study design to the integration of PMCF and PSUR results. Only when endpoints, data types, and analysis methods are clearly defined and the evidence pathways are transparently documented can claims be formulated effectively, benefit-risk analyses defended, and audits successfully passed.

2. Legal Foundations and Standards

The MDR not only provides the legal framework — it also defines what is meant by "clinical evaluation".

MDR, Article 2:

(44) ‘Clinical evaluation’ means a systematic and planned process of the continuous generation, collection, analysis and evaluation of clinical data relating to a product, which is used to verify the safety and performance, including clinical benefit, of the product when used as intended by the manufacturer.

Article 61 requires manufacturers to plan, conduct, and document clinical evaluations to demonstrate compliance with the essential safety and performance requirements. Article 61, paragraph 11, and Annex XIV, Part B, specify the obligation for life-cycle assessment: PMCF and PMS data must continuously update the CER. PMCF is understood as ongoing clinical follow-up, the results of which must be summarized in an evaluation report and integrated into the CER.

The Medical Device Coordination Group (MDCG) provides the interpretations that are crucial for practical application. MDCG 2020-1 (Clinical evaluation / performance evaluation of Medical Device Software) offers a clear, methodological framework specifically for software products. The document emphasizes that clinical evaluation of software must address three closely related components: valid clinical association (scientific justification that the software output is clinically relevant), technical/analytical performance (verification/validation evidence), and clinical performance (evidence that the software delivers the expected, patient-relevant outcomes in the target population).

MDCG 2020-5 (Guidance on Clinical Evaluation — Equivalence) focuses on the requirements for using equivalence data. The guideline defines which technical, biological, and clinical criteria must be met for a third-party product to be considered "equivalent," and it requires rigorous documentation and justification of the equivalence claim. The key message is that equivalence should not be used as a shortcut without evidence—the manufacturer must demonstrate that differences (e.g., in materials, design, indications, or instructions for use) do not compromise the transferability of the data; in cases of uncertainty, PMCF measures or the manufacturer's own clinical data are required.

MDCG 2020-6 (Clinical evidence needed for legacy devices) addresses medical devices that were previously CE marked according to the directives (MDD/AIMDD). The guidance requires a systematic gap analysis: manufacturers must assess whether the available historical pre- and post-market data meet the MDR requirements for "sufficient clinical evidence." MDCG 2020-6 describes what a Clinical Evaluation Plan (CEP) adapted to the MDR level should look like for legacy devices, which evidence hierarchy should be established (from high-quality clinical studies to vigilance/PMS data), and when additional PMCF studies or new clinical data are needed. The document explicitly links MEDDEV principles with MDR-specific requirements and provides concrete guidance on the practical aspects of closing the gap. 

The MEDDEV guideline 2.7/1 Revision 4 is still operationally helpful. Although it originates from the MDD era and does not replace the MDR, MEDDEV 2.7/1 contains very concrete tools: search strategies, selection and exclusion criteria for literature searches, evaluation grids, and reporting templates. Many continue to use MEDDEV as a practical "how-to" guide to translate the abstract MDR requirements into concrete work steps—always with the caveat that MEDDEV guidance should be adapted to the current MDR/MDCG requirements.

At the international standards level, the ISO/DIS 18969 standard – “Clinical evaluation of medical devices” – currently under development should be mentioned. The draft, currently in inquiry status, aims for more uniform terminology, structure, and evaluation logic and will help to harmonize and standardize clinical evaluation processes internationally. For manufacturers, this means that processes currently set up to comply with the MDR and MDCG should be designed in such a way that they will also comply with a potential ISO standard in the future.

3. Clinical Evaluation Process — Step by Step

Clinical evaluation is not a one-off event, but a clearly structured cycle that begins during product development and is repeated throughout the entire product lifecycle. A practical process, proven in many guidelines (MEDDEV/MDCG) and in practice, can be described in five stages: Scope/Plan (Stage 0), Identification (Stage 1), Appraisal (Stage 2), Analysis (Stage 3), and Report (Stage 4). Importantly, the earlier you begin planning, the clearer the data flow—and the easier it is to decide whether a clinical trial is necessary.

Planning: CEP and Data Route (Stage 0 — Scope)
The clinical evaluation begins with the Clinical Evaluation Plan (CEP). The CEP defines the framework of the study: the intended purpose of the product, the target populations, primary and secondary endpoints, clinically relevant comparators, the planned data sources, the fundamental methodological approaches, and the responsibilities. A crucial question here is which evidence routes are possible and permissible—are literature, preclinical data, and real-world data sufficient, or is a clinical trial necessary? This decision depends on the relevance and quality of existing data, as well as the risk and innovation level of the product. The CEP should therefore be finalized at an early stage of development; it guides search strategies, endpoint definitions, and, if necessary, the study design.

Clinical Data Identification (Stage 1):
Based on the CEP, the systematic identification of all relevant data sources begins. These include preclinical and technical study reports, in-house clinical trials, scientific literature, data from safety databases (vigilance), PMS data from the field, and existing PMCF results.

Appraisal — Critical Evaluation of the Evidence (Stage 2)
Not all data are created equal. In the appraisal phase, each data source is examined for relevance, validity, completeness, and bias. This includes evaluating study designs (RCT vs. observational study), follow-up times, endpoint definitions, population equality (including equivalence questions), statistical robustness, and data quality (e.g., missing data, selection bias). For literature searches, transparent search strategies, inclusion/exclusion criteria, and evaluation rubrics must be documented.

Analysis – Synthesis of Evidence and Benefit-Risk (Stage 3)
In the analysis phase, the reviewed datasets are compiled and systematically evaluated. This involves weighting the evidence, determining relevant key performance indicators, and assessing whether the totality of the data supports the safety and performance claims. The benefit-risk analysis is the core of this stage: Are the benefits and risks for the intended application still within an acceptable balance? If gaps or uncertainties become apparent, a decision is made as to whether and which PMCF measures are necessary, or whether a clinical trial (e.g., a PMCF study) needs to be initiated.

Report — Preparation of the CER (Stage 4)
The final step in this cycle is the Clinical Evaluation Report (CER). The CER documents in a targeted and transparent manner how the data were identified, evaluated, and integrated, and what conclusions were drawn. A good CER includes a concise executive summary, the purpose and scope, a description of the methods (including CEP reference), detailed results with measurable parameters, the benefit-risk analysis, a state-of-the-art (SoTA) assessment, and a clear presentation of any remaining data gaps.

Continuous Loop and Versioning:
Clinical evaluation is not a linear, one-off process. CER, CEP, and PMCF plans are versioned and revised when new data (PMS, PMCF, studies, literature) become available. Conclusions drawn from PMS or PMCF results are fed back into the Identification/Appraisal/Analysis stages and may trigger new studies, IFU changes, or CAPA measures.

In short: an early completion of the CEP defines the data route and avoids costly surprises. Systematic identification, rigorous critical appraisal, sound synthesis of the evidence, and a clearly structured CER are the building blocks that allow you to clearly justify, if necessary, whether or not a clinical trial is required.

4. Routes of clinical evaluation

The MDR allows three fundamentally different ways in which the necessary clinical evidence for the clinical evaluation can be provided. The route you choose significantly determines the structure, scope, and argumentation of your CER—and should therefore be precisely justified in the CEP.

Route 1 — Own Clinical Data
The strongest and easiest-to-defend evidence base is your own clinical data. This can be divided into two subgroups: a) prospective clinical trials initiated and conducted by the manufacturer, and b) clinical data from the literature, i.e., published studies in which the product itself was investigated and evaluated. Prospective studies offer the clearest correlation between effect and product—they allow for precise endpoint definitions, controlled follow-up, and statistical design. Literature data on your own product is useful if high-quality, relevant studies exist; however, their reliability depends on their design, population, and the alignment of the endpoint definitions with your claims. In any case, the study methodology, monitoring, dropout rates, and statistical analyses must be transparently documented and included in the CER.

Route 2 — Equivalence Route:
The equivalence route allows the use of clinical data from another product already on the market—but only under strict conditions. It must be demonstrated that the equivalent product is so closely related to the company's own product in technical, biological, and clinical terms that the external data allows valid conclusions to be drawn for the company's own medical device. Technical equivalence encompasses design principles, materials, and functional parameters; biological equivalence concerns materials and their interaction with the body; clinical equivalence means that indications, user profiles, conditions of use, and expected clinical effects are comparable. The requirements for justification are high—MDCG 2020-5 makes it clear that equivalence must not be used as a standard route but requires a robust, risk-based argument. If a convincing justification for equivalence is lacking, reviewers usually request supplementary data or a separate clinical investigation.

Route 3 — Performance data according to Art. 61(10) — no need for clinical investigation.
For certain products where a clinical investigation would be either technically unreasonable, ethically unacceptable, or disproportionate, the MDR allows the clinical evaluation to be based on analytical/technical performance data and other non-clinical evidence. Typical elements of this route include laboratory/bench tests, in vitro tests, simulation studies, validation of measurement accuracy, robustness and lifetime tests, usability tests, and, where appropriate, comparative tests against a recognized reference method.

When to choose which route? Practical criteria:
The choice depends on the risk profile, the level of innovation, the availability of relevant data, and ethical/practical considerations. For new technologies, ambitious claims, or high risks, a dedicated clinical trial is usually unavoidable. If the product is derived from existing solutions and equivalence can be rigorously demonstrated, the equivalence route can save time and effort—but it requires robust technical and clinical comparative documentation. For simple measuring devices or products where clinical endpoints cannot be meaningfully measured, the performance data route may be adequate—provided that the transferability to clinical practice is justifiable and PMCF measures address any gaps.

Conclusion:
Whichever route is chosen, the justification must be included in the CEP and fully traceable in the CER. The three routes are practical ways to fulfill the CER obligation, guided by risk and evidence requirements. The decisive factor is not the "most convenient" route, but the one that provides the necessary evidence for the product and claim—and which you can defend appropriately and comprehensibly before notified bodies and authorities.

5. Update of the clinical evaluation

The clinical evaluation is not a static document—the MDR explicitly mandates this. Article 61, paragraph 11 stipulates that the clinical evaluation and related documentation must be updated "throughout the product's life cycle based on clinical data" from the PMCF and PMS. For Class III and implantable devices, the MDR goes a step further: the post-market clinical follow-up evaluation report (and, where applicable, the summary report pursuant to Article 32) must be updated at least annually based on this data. In practical terms, this means that for particularly high-risk products, the clinical evaluation itself should also be updated at least annually.

Updating means more than just cosmetic changes to the text. It's a defined process that begins with the systematic collection and analysis of new clinical data—be it PMCF results, findings from PSUR/PMS reports, new literature, registry data, results from user studies, or findings from CAPA and field safety measures. Once relevant data are available, it's essential to assess whether the assumptions regarding safety, performance, or clinical benefit have changed. Crucially, the question here is whether the new findings affect the benefit-risk assessment: Has the incidence of a relevant event increased? Do subgroup analyses reveal new risks? Do field data confirm—or contradict—the claims made?

For Class III / implantable products, this procedure must be carried out at least annually; for other classes, a risk-based, documented rhythm must be defined (in the CEP), supplemented by clear trigger rules for ad-hoc revisions.

Trigger rules are crucial: define which signals automatically trigger a CER revision. Such triggers could include, for example, a statistically significant increase in the complication rate, a cluster in a specific batch, a new serious incident, a relevant study in the literature, or the finding of ineffectiveness in a defined user group.

In short: The clinical evaluation update is a cycle-based, evidence-driven process that must be performed at least annually for Class III/implantable products and must be risk-based for all products. Early definition of trigger rules, rigorous data analysis, and complete traceability are prerequisites for ensuring that CER updates are not merely formal but actually and sustainably safeguard the clinical safety and performance of your product. 

6. Conclusion

Clinical evaluation is the central instrument you use to demonstrate the clinical safety, clinical performance, and clinical benefit of your product under the MDR. It doesn't begin with the completed CER, but with a well-thought-out strategy: an early development of a CEP that defines the data route, endpoints, and responsibilities. Those who strategically plan the CER from the outset avoid costly surprises during later investigations—especially the question of whether a clinical investigation is necessary, as this allows for a well-founded answer.

Crucially, the CER must be viewed as a dynamic process. The MDR explicitly requires that the clinical evaluation be updated throughout the entire lifecycle using PMCF and PMS data; for Class III and implantable products, this review must be conducted at least annually. Updates are not merely editorial changes, but evidence-based reassessments of the benefit-risk balance with clearly documented decisions and traceable actions.

Three routes of evidence are available—your own clinical data, the equivalence route, and the performance data route—and each has its merits. The crucial factor is not the most convenient choice, but the one that provides the necessary evidence for your product and your claims.

7. How we can help you

We provide pragmatic support throughout the entire clinical evaluation lifecycle—from strategic planning to audit-ready documentation. Our goal is to design your CER processes to be MDR-compliant, methodologically sound, and practically applicable. We combine regulatory expertise with practical project and study know-how to transform requirements into genuine, defensible evidence.

Specifically, we can support you with, for example:

• Gap analyses of your existing CEP/CER/PMCF documentation, including prioritized action planning.
• Creation and review of CEPs and CERs.
• Methodology decision & study design: We define endpoints, populations, statistical plans and monitoring concepts for clinical trials or PMCF studies.
• Literature review & evidence appraisal: systematic search, quality assessment and integration into the benefit-risk analysis.
• PMCF conception and implementation.

Want to know more? Contact us for a free initial consultation!

You can get a free initial consultation here: free initial consultation

The clinical trial – direct data input for clinical evaluation

This blog post explains the role of clinical investigations as a primary source of evidence and direct data input for clinical evaluation (CER), the relevant regulatory requirements of the MDR, and how questions from CER, CEP, and PMS/PMCF are translated into a methodologically sound study design. We also demonstrate, in a practical way, how clinical investigations provide the crucial data to confirm safety and performance assumptions, substantiate claims, close data gaps, and seamlessly integrate results into CER, risk management, and the entire evidence loop.

Abbreviations

Underlying regulations, standards and guidelines

EU Regulation 2017/745 (MDR)

ISO 14155

1 Introduction

Clinical investigations are the strongest and clearest source of evidence within the MDR – yet they are often treated as an isolated compliance component in technical documentation. They are, however, far more than that: clinical investigations provide the primary, prospective data that underpin clinical evaluation (CER), support claims, and answer key questions regarding a product's safety and clinical performance. This is precisely where their strategic importance lies as a direct interface to clinical evaluation.

Under the MDR, the expectation is clear: clinical evidence does not arise by chance, but follows a systematically planned, methodologically sound process. The clinical trial is the point at which hypotheses from the Clinical Evaluation Plan (CEP), data gaps from the Clinical Evaluation Framework (CER), and findings from the Product Management System (PMS) and Product Monitoring and Competence Framework (PMCF) converge. It is the place where crucial uncertainties are examined, endpoints are reliably quantified, and benefit-risk assumptions are objectively validated – before the product is launched and long afterward.

From a regulatory perspective, clinical trials are never isolated events, but rather embedded in the evidence loop required by the MDR: PMS identifies signals, PMCF confirms or refutes open questions, and clinical trials provide the robust, intervention-related data that are crucial for both marketing authorization and subsequent updates to the CER. This closed-loop system, as already described in the article on PMS as an interface to clinical evaluation, applies here in a more stringent form: No other data source provides comparable quality of evidence or methodological control.

This places clinical trials in a central position:

• They are the number one source of input for clinical evaluation.
• They define the basis of all clinical claims.
• They provide the prospective data that neither literature nor PMS/PMCF alone can replace.

And they ensure that statements about safety and performance are based on real clinical results, not assumptions.

In this article, we show how clinical trials function as an integral part of the CER, how questions can be systematically derived from CEP, CER and PMS/PMCF, and how manufacturers can set up clinical trials to deliver maximum regulatory, methodological and strategic value.

2. Key requirements for clinical trials

Under the MDR, clinical trials are no longer an optional tool, but rather form the backbone of robust clinical evidence. The regulatory requirements are clearly defined: While the MDR (Articles 62–82) establishes the legally binding requirements for planning, conduct, monitoring, and reporting, ISO 14155, as an international GCP standard, describes the scientific, ethical, and operational principles according to which every clinical trial must be conducted. Together, they form the binding framework for every study involving human subjects – from study design to data analysis.

At their core, both regimes require that clinical trials be scientifically valid, ethically sound, and methodologically sound. A clinical trial must never be a mere formality: it must generate prospective data that support the clinical evaluation, substantiate claims, and document the product's safety and performance under real-world conditions.

Basic principles that every clinical trial must meet

1. Ethics and patient protection

The protection of study participants is paramount. No trial may commence without informed consent, a proper risk analysis, continuous safety monitoring, and an independent ethics review. The MDR makes it unequivocally clear: patient safety is non-negotiable.

2. Scientific integrity

A clinical trial must follow a clearly defined study protocol – with precise endpoints, defined inclusion and exclusion criteria, and a statistically determined design from the outset. Only in this way can data be generated that are methodologically sound and regulatory valid.

3. Complete traceability

Each collected data record must be clearly attributable to the product version and configuration used. This traceability is crucial for subsequent clinical evaluation, especially when multiple generations or variants of a product exist.

4. Monitoring & GCP Compliance

ISO 14155 requires systematic monitoring to ensure that the study is conducted in accordance with the study protocol, regulatory requirements, and ethical standards. This includes site qualification, data validation, deviation management, and consistently documented GCP compliance.

5. Transparency and complete documentation

A clinical trial must be registered, all serious adverse events reported, and any deviations clearly documented. Completeness, traceability, and transparency are key criteria for notified bodies and authorities.

3. Endpoints as the linchpin – how clinical trials “feed” your CER

Clinical trials don't provide "just any" data, but ideally precisely the evidence you need for your clinical evaluation. The key to this is clearly defined endpoints. They translate the study objectives into measurable results – and thus directly into usable evidence for the CER.

3.1 Primary vs. secondary endpoints

Primary endpoints provide the central evidence for the safety or clinical performance of your product.

Typical examples include:

• Success rate of a procedure
• diagnostic accuracy (sensitivity, specificity)
• Rate of complications or adverse events

These endpoints are ultimately used to measure whether your core claims regarding safety and clinical performance are sustainable.

Secondary endpoints provide additional, in-depth information and help to round out the benefit profile, for example:

• Time until result or recovery
• User-friendliness and handling of the product
• Patient satisfaction or quality of life measures
• Workflow or resource effects in everyday clinical practice

The primary endpoint chosen is often key to convincingly demonstrating clinical benefits and benefit-risk assessments – especially in comparison to the state of the art.

3.2 Consistently link endpoints to the CER

For your clinical trial to truly function as data input for clinical evaluation, endpoints, CERs, and claims must be aligned from the outset:

1. Define your endpoints in the Clinical Investigation Plan (CIP) so that they directly reflect the claims that will later be included in the CER (safety, clinical performance, clinical benefit).
2. Link endpoints to the measurable parameters and benefit-risk criteria that you use in CEP and CER.
3. Choose objective, quantifiable and clinically relevant measures – subjective assessments without a clear scale are difficult for Notified Bodies to use.

3.3 Study design “backwards”: from CER to clinical trial

A well-planned study design not only meets GCP requirements but is also conceived from the outset as a source of evidence for the CER. The most pragmatic approach: Think of the study backwards.

Start with the preclinical assessment, which provides initial data for the clinical trial, or with the CER if one exists (in the case of PMCF studies), or:

Systematically analyze which gaps in the evidence exist:

• Where is the data weak or only indirect?
• Which claims are currently supported only by equivalence or literature?
• What open questions has the risk management identified?

Derive specific endpoints:

• Formulate primary and secondary endpoints in such a way as to close these gaps – e.g., through precise complication rates, performance indicators, or patient-relevant outcomes.
• Consider realistic usage conditions:
• Include representative users (e.g., different experience levels) and representative patient populations.
• Choose real-world settings so that the results can be credibly transferred to the CER and the assessment of the state of the art.

Plan the statistics according to the benefit-risk framework:

Specify in the CIP:

• which hypotheses are being tested,
• which effect sizes are considered clinically relevant,
• which confidence intervals or non-inferiority limits are needed to support your benefit-risk profile.

Pro tip:

"Backward design" – start with the questions you need to answer in the CER and plan the clinical trial precisely so that it addresses these questions with clear endpoints and robust statistics. Ultimately, it's not the quantity of data, but the quality and relevance of your endpoints that determines the true strength of your evidence in the CER.

4. How clinical trials directly support your claims – and why they are a key interface to risk management

Clinical trials provide not only data, but also the crucial, measurable parameters with which you can substantiate your clinical claims in the CER. This is precisely where their strategic importance lies: they generate those prospective, controlled, and reproducible results that no other data source can replace in this form.

4.1 Measurable parameters that substantiate claims

Every claim – whether relating to safety, clinical performance, or clinical benefit – must be substantiated with objective clinical data under the MDR. Clinical trials provide the most reliable basis for this, e.g.:

Performance claims

→ Accuracy, success rates, comparison to benchmark methods

Security claims

→ Complication rates, device-related adverse events, procedural success

Benefit claims

→ Patient outcomes, time to recovery, QoL scores

These parameters are not generated randomly – they are specifically collected via the defined primary and secondary endpoints. Therefore, the clinical trial is the direct source of input for the data you use in the CER to demonstrate that your product delivers on its promises.

4.2 Focus on security: The interface to risk management

Safety issues arising from clinical trials are not only relevant for the CER, but are also an essential component of risk management. The MDR requires a closed feedback loop between risk data and clinical evidence – and the clinical trial is the first and most important checkpoint in this loop.

The following safety-relevant information from clinical trials are key inputs for the risk management file:

Adverse Events (AE)

→ Frequency, severity, expected vs. unexpected events

Serious Adverse Events (SAE) and Serious Adverse Device Effects (SADE)

→ direct assessment of risk acceptance and residual risk

Complications and procedural errors

→ Identification of use errors, training needs, IFU adjustments

Device Deficiencies

→ potential product defects that may lead to corrective actions

This data is essential because it:

- validate the risk analysis,

- confirm or refute the assumption that "risks are acceptable",

- uncover new risks,

- and can trigger specific CAPA, IFU and design decisions.

4.3 The direct way back to CER and Risk File

The interlocking mechanism functions according to a clear sequence:

→ The clinical trial collects endpoint data that either confirm or relativize claims.

→ Security data is incorporated into the risk management file, including assessments of frequencies, severity levels, and probabilities of occurrence.

→ Performance and benefit parameters are incorporated into the CER, where they provide evidence of safety, performance, and clinical benefit.

→ If necessary, results lead to actions (IFU adjustment, design change, training, labeling, CAPA).

→ Changes are documented in the CER, Risk File and PMS system, creating the regulatory required evidence loop.

This makes clinical trials the main source of objective evidence – and at the same time the essential interface between CER and risk management.

5. Clinical trials in the evidence loop – how data flows cleanly back into CER, PMS, PMCF and risk management

Clinical trials are the starting point of the regulatory evidence loop. They generate the highest-quality data on the safety and clinical performance of a product – and this data must then be methodically and rigorously integrated into all related documentation and processes. The MDR requires a closed loop in which clinical evidence is continuously developed and updated.

5.1 From study data set to Clinical Evaluation Report (CER)

The clinical trial provides the primary data on which the CER is based – both for the initial approval and for subsequent updates:

Primary endpoints → direct confirmation of core claims (e.g., success rate, performance, accuracy)

Secondary endpoints → support clinical benefit and usability

Statistics and hypothesis testing → Basis for benefit-risk analysis

The results are systematically integrated into the CER at the following points:

• Evidence of safety and clinical performance
• Justification for risk acceptance
• Assessment of clinical benefit
• Comparison with the state of the art

Thus, the clinical trial forms the evidence-based core of the entire clinical evaluation.

5.2 Input for risk management – ​​validation, detection, adaptation

The safety-related results of the clinical trial are direct inputs for the risk management file (ISO 14971):

Validation of the existing risk assessment

→ Do the expected risks match the actual study results?

Identification of new risks or higher frequencies

→ Emergence of new AEs or variability in the application

Evaluation of use errors or design weaknesses

→ Basis for IFU updates, user training, or design changes

Quantification of residual risks

→ necessary for the final benefit-risk assessment

This makes clinical trials a crucial link between clinical evidence and risk management.

5.3 Relevant results for PMS & PMCF

For post-market processes, the clinical trial is more than just a starting point – it defines the basis against which all subsequent real-world data is measured:

PMS:

The event rates defined and measured in the study setup serve as comparison and reference values ​​to determine whether new signals are emerging in the market.

PMCF:

The clinical trial often defines the data gaps that need to be further investigated post-marketing:

• Long-term results
• rare complications
• Subgroup analyses
• Performance with extended user groups

PMCF thus builds directly on the results of the clinical trial and extends them into the post-market phase.

5.4 The regulatory process – the evidence loop in practice

The integration process is a closed one:

Clinical trials provide primary evidence:

→ Safety, performance, benefits, complication rates, usability

Data flows into CER, benefit-risk analysis, and state-of-the-art comparison. Security data is simultaneously integrated into risk management.

→ Verification, new risks, CAPA, IFU/label adjustments

PMS and PMCF strategies are defined based on the study data.

→ What open questions remain? Which hypotheses need to be tested after the market launch?

PMS/PMCF generate new real-world data that flows back into the CER and Risk File.

The CER is continuously updated:

→ as required by the MDR in Article 61

This creates a regulatory-required, audit-proof control loop in which the clinical trial is the first and strongest building block.

6. Conclusion

The clinical trial – if conducted – is the most evidence-based component of the clinical evaluation: methodically sound planning and GCP-compliant execution provide the primary, reliable data that substantiate claims, support benefit-risk analyses, and place the CER on an audit-proof foundation. It generates the measurable parameters with which manufacturers can demonstrate both safety and clinical performance and benefit – thus forming the backbone of any sound technical documentation.

At the same time, clinical investigations are a key interface with risk management: Adverse events, complications, and device-related incidents validate risk assessments, uncover new risks, and guide IFU, labeling, CAPA, and design measures. When properly integrated, clinical investigations link regulatory requirements with clinical reality and create the foundation for a complete evidence loop encompassing CER, PMS, and PMCF.

In short: A clinical trial is not a regulatory component in isolation, but a strategic instrument that increases patient safety, defines the quality of your clinical evidence and ensures long-term product compliance – provided it is properly planned, methodically documented and fully integrated with CER and risk management.

7. How we can help you

We support you throughout the entire clinical investigation lifecycle: from deriving precise research questions from CER, CEP, PMS, and PMCF, through study design, protocol development, sample size estimation, and setup, to data management, monitoring, biostatistics, and analysis. We prepare CIP and final reports and ensure that all results are seamlessly integrated into CER and PMCF reports and risk management – ​​for a consistent, robust, and audit-proof clinical evidence base.

Want to know more? Contact us for a free initial consultation!

You can get a free initial consultation here: free initial consultation