Yes, biostatistics services can qualify for Research and Development (R&D) tax credits in the UK, provided they meet the eligibility criteria set by HM Revenue and Customs (HMRC). Under the SME R&D Relief scheme, eligible companies can claim up to 186% of their qualifying R&D expenditure. This includes costs related to staff, consumables, and subcontractors involved in biostatistics activities that aim to advance scientific or technological knowledge.

Eligible costs encompass:

• Staff Costs: Salaries and wages for employees directly involved in the R&D activities.
• Consumables: Materials and software used specifically for the R&D project, such as statistical software licences.
• Subcontractor Costs: Payments to third-party providers or consultants specialising in biostatistics.
• Software Development: Expenses related to developing or acquiring software tools essential for advanced statistical analyses.

Claim Process:

1. Identify Eligible Projects: Assess your biostatistics-related activities against HMRC’s eligibility criteria.
2. Document R&D Activities: Maintain comprehensive records of the R&D processes, including project objectives, methodologies, and challenges.
3. Calculate Eligible Expenditure: Compile all eligible costs associated with biostatistics services.
4. Submit the Claim: Include the R&D tax credit claim within your company’s Corporation Tax return, providing detailed explanations and supporting documentation.
5. Consult a Specialist: Given the complexity of R&D tax credit claims, it is advisable to consult with a tax advisor or specialist to ensure accuracy and maximise potential benefits.

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Data focus: As our entire focus is biostatistics and other data-related concerns we tend to have more up-to-date and innovative methodologies in our tool-kit.

Niche expertise: We can develop strategies which integrate data techniques from separate complimentary disciplines such as biostatistics, bioinformatics/genomics, data science/machine learning/ AI, and complex systems using a combination of data sources such as RWD and prior clinical trial data. Insights from this approach will enable more effective product development and clinical trial designs aligned to the right markets.

Cost: As a boutique firm we tend to have lower overheads and may be able to offer more competitive pricing than larger CROs.

Flexibility: Ability to be more agile and able to adapt to changing project needs or timelines more quickly than larger CROs.

See here for more details.

Real World Data (RWD) refers to the information collected outside of traditional clinical trials, such as electronic health records (EHRs), insurance claims, patient registries, and patient-reported outcomes. Utilising RWD can significantly enhance clinical research and the design of clinical trials in several key ways:

1. Tailoring Participant Populations: RWD enables researchers to analyse large-scale datasets to understand the demographic and clinical characteristics of specific disease populations. By examining trends in patient demographics—such as age, sex, race, and geographic distribution—researchers can design clinical trials that more accurately reflect the real-world population affected by the disease. This ensures that the trial results are generalisable and applicable to a broader patient base.

2. Enhancing Study Design: Incorporating insights from RWD during the design phase of clinical trials leads to more effective and efficient studies. RWD can help refine inclusion and exclusion criteria, identify relevant endpoints based on real-world outcomes, and optimise sample sizes to ensure adequate statistical power. This alignment with actual clinical practices and patient experiences reduces the likelihood of trial failure due to recruitment challenges or misaligned objectives.

3. Identifying Genomic and Biomarker Insights: RWD can be mined for genomic profiles and biomarkers associated with specific diseases. Understanding these biological markers allows for the identification of subpopulations that may respond differently to treatments, facilitating more personalised and targeted therapeutic approaches. This precision enhances the effectiveness of clinical trials and increases the likelihood of discovering successful interventions tailored to individual genetic profiles.

4. Reducing Costs and Time: By leveraging RWD, researchers can simulate trial scenarios and predict outcomes, potentially reducing the time and costs associated with trial planning and execution. RWD helps identify optimal trial locations, estimate recruitment rates, and foresee potential challenges, enabling more strategic and resource-efficient trial designs. This proactive approach minimizes delays and optimizes the allocation of resources.

5. Facilitating Regulatory Compliance: RWD supports regulatory submissions by providing comprehensive evidence of a treatment’s effectiveness and safety in real-world settings. This supplementary data can enhance the robustness of regulatory filings, offering a more complete picture of a therapeutic’s performance. Ensuring that statistical analyses based on RWD meet regulatory standards is crucial for gaining approval from authorities like the FDA or EMA.

6. Enabling In Silico Trials: Future advancements may include the use of in silico trials, where virtual patient populations based on RWD are used to simulate clinical trials. These computational models can incorporate demographic variables, biological variability, and disease burden, providing a cost-effective and rapid means to predict therapeutic impacts before conducting actual trials.  Although still emerging, this approach has shown promise, as demonstrated by its application in a brain aneurysm medical device study. This innovative approach has the potential to complement traditional trials, offering additional layers of validation and insight.

1. Early Planning Phase

Study Design: From the very beginning, involving a biostatistician ensures that your study is designed with robust methodologies. We assist in determining the appropriate sample sizes, selecting suitable randomisation strategies, and choosing the right study type (e.g., randomized controlled trial, cohort study). A well-designed study minimizes biases, maximises validity, and ensures that your research questions are adequately addressed.

Protocol Development: Integrating statistical considerations into your study protocol is crucial for scientific and regulatory rigor. Our biostatisticians collaborate with your team to define primary and secondary endpoints, develop detailed statistical analysis plans, and establish criteria for data collection and management. This comprehensive approach ensures that your protocol meets both scientific objectives and regulatory requirements.

Feasibility Assessment: Before initiating your study, we conduct feasibility assessments to evaluate potential recruitment rates, timelines, and resource allocations. By identifying and addressing potential challenges early on, we help you optimize study logistics, ensuring that your project is both practical and achievable within the desired timeframe and budget.

2. During Data Collection

Interim Analyses: For studies that incorporate interim data evaluations, our biostatisticians play a pivotal role in planning and executing these analyses. Interim analyses allow for monitoring progress, making informed decisions about study continuation, and implementing adaptive trial designs if necessary. This proactive approach enhances study efficiency and ethical standards by ensuring participant safety and resource optimization.

Data Monitoring and Quality Assurance: Maintaining data integrity throughout the collection process is vital for reliable outcomes. We establish robust data monitoring frameworks to detect and address inconsistencies, missing values, and potential biases in real-time. Our team ensures that data collection adheres to predefined protocols, thereby upholding the highest standards of quality and reliability.

Ongoing Statistical Support: As data accumulates, our biostatisticians provide continuous support to troubleshoot any emerging statistical issues. Whether it’s refining data collection methods or adjusting analysis plans, we work collaboratively with your team to keep the study on track and aligned with its objectives.

If you’re embarking on a clinical project and want to ensure that your statistical foundations are strong from the beginning, contact Anatomise Biostats to schedule a consultation today. We are committed to supporting your project’s success through every phase.

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Yes, adaptive trial design can be relevant for diagnostics, and it is increasingly being used in diagnostic studies for several reasons:

1. Refining Diagnostic Performance (Sensitivity and Specificity)

• Adaptive designs allow mid-trial adjustments to refine thresholds or cutoffs used to classify diagnostic results (e.g., positive/negative or disease/no disease).
• This is particularly useful if initial results indicate that the diagnostic tool's sensitivity (true positive rate) or specificity (true negative rate) can be optimized without compromising study validity.

2. Adjusting Sample Size

• If interim analyses suggest that the study is underpowered to meet its objectives, adaptive designs allow sample size adjustments.
• Conversely, if sufficient evidence is gathered earlier than expected, the trial can stop early, saving time and resources.

3. Enrichment Designs

• Adaptive trials for diagnostics can identify subsets of the population (e.g., demographic or clinical subgroups) where the diagnostic performs better. The design can then focus subsequent recruitment on those subgroups to strengthen results.

4. Evaluation of Biomarkers

• In studies assessing biomarker-based diagnostics, adaptive designs can be used to refine the selection of biomarkers being tested, prioritizing those with the best performance.

5. Seamless Designs

• Adaptive seamless designs combine phases of a diagnostic study (e.g., feasibility and pivotal phases) into a single protocol, enabling data from the first stage to inform the second without a pause in recruitment.

6. Stopping for Futility or Success

• If early interim results show that the diagnostic is unlikely to achieve the required accuracy or utility, the trial can stop for futility. Alternatively, if the diagnostic exceeds performance thresholds early, the trial can stop for success.

Example Applications

• Companion Diagnostics: Adaptive designs can adjust thresholds for predicting treatment response based on early patient outcomes.
• Infectious Disease Testing: Adaptive designs allow modifications in algorithms based on early data to better detect or differentiate disease strains.
• AI-Powered Diagnostics: Adaptive trials can refine machine learning models as additional data becomes available during the study.

Challenges

While adaptive designs offer flexibility, they require careful planning to maintain statistical integrity and regulatory acceptance. Pre-specification of adaptation rules and robust simulation work are essential.

Yes, adaptive trial design is highly relevant for therapeutic devices and can offer significant benefits in terms of efficiency, flexibility, and robustness. Here are key ways adaptive trial designs are applicable to therapeutic device studies:

1. Adjusting Sample Size

• Adaptive designs can use interim analyses to determine whether the sample size needs to be increased to maintain statistical power or reduced if early results are strong.
• For therapeutic devices, this is particularly helpful when initial assumptions (e.g., variability in outcomes or effect size) are uncertain.

2. Modifying Dose or Treatment Parameters

• For devices with adjustable settings (e.g., energy levels for ablation devices or pressure settings for mechanical devices), adaptive designs can refine these parameters based on early results.
• This ensures the trial is optimized to identify the most effective and safe configuration.

3. Enrichment Designs

• Adaptive trials can identify subpopulations that respond better to the therapeutic device and focus recruitment on those subgroups. For example, age, disease severity, or comorbid conditions might influence device efficacy or safety.

4. Stopping for Futility or Success

• If interim results show the device is unlikely to achieve the desired outcomes, the trial can stop early for futility, saving time and resources.
• Conversely, if early results show strong efficacy and safety, the trial can stop early for success, allowing quicker market access.

5. Seamless Phase Designs

• Adaptive seamless trials combine phases, such as feasibility and pivotal trials, into one continuous study. Data from the feasibility phase can inform the pivotal phase without halting the trial.
• This approach reduces the time and cost of device development.

6. Dose-Response Optimization

• For devices that deliver therapeutic agents (e.g., drug-eluting stents or infusion pumps), adaptive trials can refine the dose or delivery schedule during the trial to achieve the best balance of efficacy and safety.

7. Comparative Effectiveness and Non-Inferiority

• Adaptive designs allow adjustments when comparing a therapeutic device to a standard treatment. If the device shows early signs of being equivalent or superior, the design can focus more on confirming this without unnecessary additional data collection.

8. Learning and Confirming Cycles

• Especially relevant for new therapeutic devices, adaptive trials allow early learning phases to refine hypotheses and confirmatory phases to validate findings, all within a single trial structure.

Examples of Applications

1. Orthopaedic Implants: Refining surgical techniques or device positioning parameters based on early outcomes.
2. Cardiac Devices: Adjusting pacing algorithms or energy thresholds for defibrillators during a trial.
3. Neuromodulation Devices: Optimising stimulation parameters based on early efficacy and patient-reported outcomes.
4. Respiratory Devices: Fine-tuning pressure levels in ventilators to balance efficacy and patient comfort.

Regulatory Considerations

• Regulatory agencies such as the FDA and EMA accept adaptive designs for therapeutic devices but require pre-specified rules for adaptations to ensure statistical validity and trial integrity.
• Comprehensive simulation studies and detailed statistical analysis plans are critical for gaining regulatory approval.

Adaptive designs for therapeutic devices allow flexibility to address uncertainties, optimise trial outcomes, and accelerate development timelines. Let me know if you’d like assistance in integrating this into your brochure or other materials!

Clinical-translational studies encompass a broad spectrum of research activities aimed at bridging the gap between laboratory discoveries and their application in patient care. These studies integrate basic scientific research with clinical research to accelerate the development of new diagnostics, treatments, and preventative measures. The primary goal is to translate findings from the bench (laboratory) into bedside (clinical practice), ensuring that scientific advancements lead to tangible health benefits. This process often involves multiple phases, including preclinical studies, early-phase clinical research, and the subsequent implementation of successful interventions in routine healthcare settings.

In contrast, clinical trials are a specific type of clinical-translational study focused on evaluating the safety and efficacy of new medical interventions, such as drugs, devices, or treatment protocols, in human participants. Clinical trials follow a structured progression through distinct phases (Phase I to Phase IV), each designed to answer particular research questions, from initial safety assessments to long-term effectiveness and monitoring of adverse effects. While clinical trials are a critical component of clinical-translational studies, they represent just one stage in the broader translational research continuum. Essentially, clinical-translational studies encompass the entire pathway from early scientific discovery to the practical application of new medical innovations, with clinical trials serving as a key step in validating and implementing these advancements in patient care.

Clinical trials for medical devices typically progress through several key phases to ensure their safety and efficacy. Initially, preclinical studies are conducted in laboratories and animal models to assess the device’s fundamental functionality and safety. Following this, pilot or feasibility studies involve a small number of human participants to evaluate usability and gather preliminary effectiveness data. The next phase, pivotal clinical studies, comprises larger-scale trials that provide definitive evidence of the device’s performance, which is crucial for regulatory approval. Finally, post-market surveillance monitors the device’s long-term performance and safety in real-world settings after approval. These phases ensure that medical devices meet regulatory standards and perform reliably in clinical practice.

Yes, some clinical trials for medical devices incorporate randomised controlled trial (RCT) designs, although their application can differ from those used in pharmaceutical studies. The phases of clinical trials for medical devices—preclinical studies, pilot or feasibility studies, pivotal clinical studies, and post-market surveillance—may involve RCTs depending on the study’s objectives and the nature of the device.

Randomised Controlled Trials in Medical Device Studies

• Pivotal Clinical Studies: This phase often includes RCTs, which are considered the gold standard for evaluating the efficacy and safety of medical devices. In an RCT, participants are randomly assigned to receive either the new device or a control (such as a standard treatment or placebo). This randomisation helps minimise bias and allows for a robust comparison between the intervention and control groups.
• Pilot or Feasibility Studies: These early-phase studies typically focus on assessing the feasibility, usability, and preliminary effectiveness of a device. While they may not always employ randomisation, some pilot studies do incorporate randomised elements to refine study protocols and gather initial comparative data.

Considerations for Using RCTs with Medical Devices

• Ethical and Practical Challenges: Conducting RCTs with medical devices can present unique challenges, such as ensuring blinding (which is often difficult with physical devices) and managing patient or clinician preferences for certain treatments.
• Study Design Adaptations: To address these challenges, researchers may use alternative designs or incorporate elements like randomisation in the allocation of device features or settings rather than the device itself.

Other Study Designs in Medical Device Trials

• Comparative Effectiveness Studies: These studies compare the new device with existing standard treatments without necessarily using randomisation. They aim to determine which treatment performs better in real-world settings.
• Observational Studies: In some cases, observational studies are used to monitor the real-world performance of a device, particularly during post-market surveillance. These studies do not involve randomisation but provide valuable data on long-term safety and effectiveness.

While randomised controlled trials are a crucial component of clinical trials for medical devices, particularly in pivotal studies seeking regulatory approval, they are not always employed in every phase. The decision to use an RCT design depends on the specific objectives of the study, the nature of the device, and practical considerations related to the study population and ethical standards. Incorporating randomisation in pivotal trials enhances the robustness and credibility of the evidence supporting the device’s safety and efficacy.

If you have further questions or need assistance with designing clinical trials for medical devices, feel free to contact our team at Anatomise Biostats.

Yes, there are certain products for which comprehensive clinical-translational studies may suffice without the need for extensive clinical trials. This typically applies to lower-risk diagnostic tools, medical devices, and healthcare interventions that have established safety profiles or are modifications of existing technologies. For example:

• Diagnostic Devices: Tools such as imaging software, laboratory assays, and non-invasive diagnostic instruments often require thorough validation through clinical-translational studies to confirm their accuracy and reliability. If these diagnostics enhance existing technologies or have a well-documented safety record, extensive clinical trials may not be necessary.
• Software as a Medical Device (SaMD): Medical software designed for specific diagnostic functions can sometimes bypass traditional clinical trials if it consistently demonstrates performance and compliance with regulatory standards through rigorous clinical-translational research.
• Health Monitoring Tools: Wearable devices and mobile health applications intended for monitoring vital signs or managing chronic conditions may rely on clinical-translational studies to ensure they provide accurate and actionable data without the need for large-scale clinical trials.
• Non-Therapeutic Medical Devices: Products such as surgical instruments, mobility aids, and certain prosthetics, especially those that are iterations of existing devices with proven safety, may only require clinical-translational studies to validate their functionality and safety.

However, it is important to note that the necessity for clinical trials versus clinical-translational studies is determined by regulatory authorities based on the product's risk classification, intended use, and potential impact on patient health. Regulatory bodies like the Medicines and Healthcare products Regulatory Agency (MHRA) in the UK or the European Medicines Agency (EMA) assess each product individually to determine the appropriate level of evidence required for approval.

While clinical-translational studies can be sufficient for validating certain lower-risk or well-established products, more novel or higher-risk interventions typically require comprehensive clinical trials to meet regulatory standards and ensure patient safety. Consulting with regulatory experts is essential to determine the specific requirements for your product.