Execute your clinical research project with greater clarity, efficiency & accuracy.
Biostatistics expertise for the design & analysis of your R&D and clinical trials.
Biostatistics is the application of statistical methods to biological, biomedical, and public health research. In clinical trials, biostatisticians are responsible for planning study designs, determining sample sizes, developing randomisation schemes, and conducting statistical analyses of data collected during the study. Application of correct biostatistical methods in these areas ensures that a clinical study is appropriately powered to detect meaningful effects, and the the data analysis is able to draw valid conclusions about the efficacy and safety of the interventions being studied. Biostatisticians also help interpret the results, assess the statistical significance of findings, and communicate the outcomes to the research team and regulatory authorities.
- Biostatistics & Clinical Data Analysis
- Bayesian Techniques & Clinical Trial Design
- Bayesian Adaptive Designs
- Clinical Survey Design & Analysis
- Biomedical Complex Systems & Network Analysis
Our biostatistical consulting services are individually tailored to suit the project at hand and can cover a broad range of approaches. We can theoretically enter into collaboration at any stage of your research, whether it be the design stage, data collection, statistical analysis, reporting and preparing for presentations. Most commonly a statistical consulting project will cover one or more of the following areas:
Biostatistics Services for Study Design and Planning in Clinical Trials
Study design and planning are critical elements for the success and validity of clinical trials. Our biostatistical consulting services offer tailored solutions to ensure the scientific rigour and reliability of your research. Here are some key aspects of our biostatistics services involved in study design and planning for clinical trials:
1. Study Design and Protocol Development:
We collaborate closely with your research team to determine the most appropriate study design based on your research objectives, available resources, and ethical considerations. Our experts assist in defining the study population, eligibility criteria, endpoints, and estimands which form essential components of the study protocol.
2. Sample Size Calculation:
Accurate sample size calculation is vital for obtaining statistically significant results. Our biostatisticians utilise statistical methods to calculate the optimal sample size, considering factors like expected effect size, variability, significance level, and statistical power. This ensures that your trial has sufficient participant numbers to detect clinically meaningful differences.
3. Randomisation Schedules:
To reduce bias and ensure comparability between groups, randomisation is employed to allocate participants randomly to different treatment groups. We design randomisation schedules or algorithms, using techniques like simple randomisation, stratified randomisation, or block randomisation.
4. Statistical Analysis Plan (SAP):
The SAP is a comprehensive document that outlines the statistical methods to be used in analysing the collected data. Working closely with your research team, we develop the SAP, which includes descriptions of primary and secondary endpoints, analysis populations, statistical tests, and handling of missing data. The SAP guides the data analysis process, promoting transparency and consistency.
5. CDISC Compliant SDTM and ADaM Data Set Preparation:
Ensuring compliance with Clinical Data Interchange Standards Consortium (CDISC) guidelines, we prepare your clinical trial data in standardised formats, such as the Study Data Tabulation Model (SDTM) and the Analysis Data Model (ADaM). This conversion of raw data enhances data integration and facilitates regulatory submissions.
6. TLFs (Tables, Listings, and Figures) Mock Shells:
TLFs are essential components of clinical trial reporting, presenting key findings in a clear and concise manner. Our biostatisticians create mock shells for TLFs, outlining the layout and structure of tables, listings, and figures that will be generated during data analysis. These mock shells ensure consistency in reporting and enhance data visualisation.
Meta-Analysis and Bayesian Simulation Techniques using MCMC for Clinical Trial Planning
Meta-Analysis and Bayesian simulation techniques are valuable tools that can aid in the planning of clinical trials, providing insightful information for decision-making and study design optimization.
1. Meta-Analysis:
Meta-analysis involves the systematic review and quantitative synthesis of data from multiple clinical studies. It allows researchers and biostatisticians to pool information from various trials, providing a comprehensive and more precise estimate of treatment effects or associations. Meta-analysis is particularly useful when individual studies might have insufficient statistical power to detect small effects. By combining data from multiple trials, researchers can obtain more robust conclusions, informing the planning of new trials by identifying gaps in existing evidence or determining the most promising interventions to investigate further.
2. Bayesian Simulation Techniques using MCMC (Markov Chain Monte Carlo):
Bayesian methods are increasingly used in clinical trial planning due to their ability to incorporate prior information, update knowledge as data accumulates, and quantify uncertainty more intuitively. Bayesian simulation techniques, such as Markov Chain Monte Carlo (MCMC), provide a flexible approach to assess various design scenarios and their impact on trial outcomes. By simulating data under different assumptions and incorporating prior distributions, researchers can explore the potential influence of various factors on the study's statistical power and expected outcomes. This allows for informed decision-making and optimization of the study design before conducting the actual trial.
Our biostatistics services in clinical trial study design and planning are designed to cater to your specific needs, ensuring that your research yields robust and scientifically sound results. incorporating meta-analysis and Bayesian simulation techniques can enhance the planning of clinical trials by leveraging existing evidence and simulating different scenarios. We collaborate closely with your team to provide solutions that advance medical knowledge and improve patient outcomes. Contact us today to discuss how our expertise can benefit your clinical trials.
Statistical Analysis of Study Data and Statistical Analysis Report (SAR) for Clinical Trial Data
A well-defined Statistical Analysis Plan (SAP) guides the statistical analysis of clinical trial data. Following the SAP, the Statistical Analysis Report (SAR) becomes a crucial deliverable that summarizes the findings and conclusions of the study.
1. Statistical Analysis of Clinical Trial Data:
Upon completion of data collection, biostatisticians analyse the clinical trial data according to the pre-specified SAP. This process includes data cleaning, exploratory data analysis, and application of appropriate statistical methods to address the research questions and objectives. Biostatisticians perform inferential analyses, such as hypothesis testing and estimation of treatment effects, to draw meaningful conclusions from the data.
2. Statistical Analysis Report (SAR) as a Deliverable:
The SAR serves as a comprehensive documentation of the statistical analyses conducted in the clinical trial. It includes detailed descriptions of the statistical methods applied, any adjustments for multiple comparisons or potential confounding factors, as well as the results of all analyses performed. The SAR should be transparent and replicable, allowing regulatory agencies and other stakeholders to assess the validity of the study's findings.
3. Interim Analyses in Clinical Trials:
Interim analyses are an essential feature of some SAPs, particularly in long-duration trials or when ethical considerations necessitate early assessment of treatment effects. Biostatisticians conduct interim analyses at predetermined time points, using appropriate statistical methods to evaluate the accumulating data while preserving the overall trial integrity. Interim analyses can inform early termination, sample size adjustments, or modifications to the trial protocol, ensuring efficient use of resources and reducing participant burden.
During the analysis phase, adherence to the SAP and the subsequent generation of a comprehensive SAR ensures the validity and transparency of study results. Planned interim analyses provide an opportunity for data-driven decisions, optimising trial conduct and maximising the potential impact of clinical research.
We can assist with many aspects of grant proposals including sample size and power calculations, clinical study designs, cost estimations for the biostatistical consulting component of the research, and in the development and analysis of pilot studies. Biostatisticians can often play a critical role in helping with grant proposals by providing essential statistical expertise and support throughout the application process. Here's how our biostatistical services may be able to assist in more detail:
1. Study Design and Methodology: We collaborate closely with your team to develop robust study designs and methodologies that align with your research objectives. Our expertise helps define appropriate endpoints, sample sizes, randomization strategies, and statistical analyses, ensuring your proposed study is scientifically sound and feasible.
2. Power and Sample Size Calculation: Our biostatisticians perform power and sample size calculations to estimate the number of participants required to detect meaningful effects. This valuable information helps justify the feasibility of your study and demonstrates its statistical power to grant reviewers.
3. Data Analysis Plan: We assist in formulating a comprehensive data analysis plan that outlines the statistical methods to be used for analysing the collected data. Our well-structured analysis plan adds credibility to your proposal and assures reviewers that the data will be analysed appropriately.
4. Statistical Justifications: Our team provides clear and concise statistical justifications for your proposed study design and methods. These justifications strengthen your grant proposal, emphasising the rationale behind the statistical approach.
5. Budget Allocation: We help allocate appropriate resources and budget for statistical support, including data management, statistical analysis, and reporting. A well-defined budget enhances the credibility of your proposal, demonstrating that statistical aspects are adequately addressed.
6. Pilot Studies and Feasibility Analysis: If needed, we can conduct pilot studies or feasibility analyses to gather preliminary data, bolstering your proposal and demonstrating the viability of your research plan.
7. Graphical Representations: We create visually compelling graphical representations of your study design, sample size justifications, and statistical analyses. These visual aids enhance the clarity of your proposal and help reviewers grasp the statistical aspects more easily.
8. Data Management Plan: Our biostatisticians contribute to developing a data management plan that ensures data integrity, confidentiality, and traceability. A well-structured data management plan reassures reviewers about the quality of data to be collected and analysed.
9. Statistical Software and Programming: Our team possesses expertise in using statistical software and programming languages (e.g., Stata R, SAS, Python) to conduct data analyses. This proficiency ensures accurate and reliable results for your proposed research.
10. Review and Editing: We can review and edit the statistical sections of your grant proposal, ensuring accuracy, clarity, and adherence to guidelines. Our input helps improve the overall quality of your proposal, increasing its chances of success.
By engaging a biostatistician early in the grant proposal process, medical device or pharma start-ups and SMEs can enhance the statistical rigor of their research plan, increasing the chances of securing funding for their innovative projects. Biostatistics expertise adds credibility to your proposal, demonstrating the robustness and validity of your study design and statistical analyses. Partner with us today and take the first step towards successful grant funding for your transformative research initiatives.
Bayesian meta-analysis
Meta-analysis or evidence synthesis is a necessary part of clinical trial design which serves to optimise the insights and accuracy of the present trial. This is achieved by combining evidence from previous trials. Accurately combining evidence from multiple sources requires a hierarchical framework. This can be more reliably achieved by use of Bayesian rather-than frequentist meta-analysis methods. Bayesian meta-analysis is beneficial on a stand-alone basis as well as in the planning of any clinical trial and is an integral initial planning stage of any adaptive clinical trial design.
Bayesian adaptive designs for phase 1-3 clinical trials
Adaptive clinical trial design entails the prospective planning and detailed specification of methodological adaptation to be implemented in clinical trials in cases where the need arises due to changes in trial environment and updates in knowledge such as drug effectiveness data. As well as avoiding clinical trial failure by managing uncertainty and optimising resources, thereby increasing time and cost efficiency, as well as the flexibility to end an unproductive trial early or update treatment doses without running subsequent dose-finding trials.
The initial protocol stage of a phase one trial is the most ideal stage to incorporate the adaptive design, the use of adaptive design can however be initiated at any phase of clinical trial provided the data for that phase remains blinded to all parties. Adaptive trial design can be of benefit within any individual phase of clinical trial as well as highly beneficial in optimizing the entire clinical research process between phases 1 to 3 of any clinical trial sequence.
Bayesian predictive models/ Bayesian generative models
Statistical models are a simplification of reality that aim to capture primary factors underlying a system under investigation based on observed data. The aim of model fitting is to increase understanding of specific factors making up an underlying system. Statistical models can vary from quite simplistic to increasingly complex as the situation necessitates. Model fitting involves estimating model parameters in line with observed data.
Bayesian model development consists of:
- Determining a prior distribution based on previous data and subjecting to a prior predictive checking process
- Determining the likelihood function
- Combining the likelihood function with the prior distribution to form a posterior distribution
- Summarising resulting probabilities with associated point estimates
Markov Chain Monte Carlo (MCMC) based methods:
- Metropolis hastings
- Reversible jump MCMC
- Hamiltonial Monte Carlo
- Gibbs Sampler
- Particles MCMC
- Evolutionary Monte Carlo
Other methods:
- Sequential Monte Carlo
- Approximate Bayesian Computation
- Integrated nested Laplace approximations
- Variational Bayes.
Advancing Clinical Trials with Biostatistical Methods: Bayesian Adaptive Designs and Master Protocols
In the realm of medical research and drug development, clinical trials play a pivotal role in assessing the safety and efficacy of new treatments. Biostatistics, a crucial discipline within the field of clinical research, offers innovative and adaptive approaches to optimise trial efficiency and enhance decision-making. Transformative biostatistical methods of Bayesian Adaptive Designs and Master Protocols are revolutionizing the landscape of clinical trials.
1. Understanding Bayesian Adaptive Designs
Bayesian Adaptive Designs are a powerful class of clinical trial designs that continuously update trial parameters based on accumulating data. Unlike traditional fixed designs, Bayesian methods allow for seamless incorporation of new information, making trials more efficient and responsive to real-time evidence.
1.1 Bayesian Inference in Clinical Trials
Bayesian inference is at the core of Bayesian Adaptive Designs. It allows researchers to combine prior knowledge (prior probabilities) and newly collected data (likelihood) to update beliefs about treatment effects (posterior probabilities). This iterative process helps researchers make data-driven decisions and adapt the trial as it progresses.
1.2 Advantages of Bayesian Adaptive Designs
The key advantages of Bayesian Adaptive Designs include:
- Efficient Sample Size: Bayesian methods often require smaller sample sizes than traditional designs to achieve the same level of statistical power, reducing costs and accelerating trial completion.
- Early Stopping: Bayesian designs enable early stopping for futility or efficacy, sparing patients unnecessary exposure to ineffective treatments.
- Seamless Adaptation: Researchers can adapt treatment arms, patient allocation ratios, or even eligibility criteria during the trial based on emerging data, making the trial more responsive to changing circumstances.
- Personalised Medicine: Bayesian methods facilitate subgroup analysis, allowing for the identification of patient populations that benefit the most from the treatment.
2. Master Protocols: Unifying Multiple Trials
Master Protocols, also known as umbrella, basket or platform trials, are innovative and adaptive trial designs that unify multiple sub-studies under a single infrastructure. These protocols streamline the evaluation of multiple treatments or interventions for a specific disease or condition.
2.1 Umbrella Trials
Umbrella trials group patients with a particular disease into different sub-studies based on specific biomarkers or genetic characteristics. Each sub-study evaluates a different targeted therapy or treatment, allowing for a personalized approach to patient care based on individual biomarker profiles.
2.2 Basket Trials
In contrast to umbrella trials, basket trials group patients with different diseases or conditions based on shared biomarkers or genetic alterations. This design allows for the evaluation of a specific targeted therapy or treatment across diverse patient populations, enabling efficient assessment of treatments for rare genetic abnormalities.
2.3 Platform Adaptation Trials
Platform Adaptation Trials are dynamic master protocols that continuously evolve to accommodate new treatments or interventions during the trial. They allow for the seamless addition or removal of treatment arms based on real-time data, promoting flexibility and efficiency in clinical research.
3. Biostatistical Considerations and Challenges
While Bayesian Adaptive Designs and Master Protocols offer numerous advantages, they also present unique biostatistical considerations and challenges.
3.1 Bayesian Sample Size Calculations
Bayesian sample size calculations require careful consideration of prior information, effect size assumptions, and the desired level of statistical power. Biostatisticians must strike a balance between updating prior beliefs and incorporating newly acquired data to ensure optimal trial efficiency.
3.2 Adaptive Randomization
Adaptive randomization methods need to be carefully designed to maintain trial integrity while accounting for potential biases and ethical considerations. Proper allocation mechanisms are essential to ensure unbiased treatment assignment and control potential confounding factors.
3.3 Data Monitoring and Interim Analyses
Biostatisticians play a crucial role in designing robust interim analysis plans to safeguard trial integrity. Continual data monitoring and adaptive decision-making require sound statistical methodologies to prevent unwarranted bias and maintain the validity of trial results.
Bayesian Adaptive Designs and Master Protocols represent cutting-edge biostatistical methods that revolutionise the landscape of clinical trials. These innovative approaches enable more efficient and responsive studies, ultimately accelerating medical research and improving patient outcomes. By incorporating Bayesian methods and unifying multiple trials under a master protocol, researchers can optimize trial efficiency, facilitate personalised medicine, and drive medical advancements towards a brighter future.
Survey design and development:
We ensure that a survey addresses your overarching research goals and evaluates the constructs it intends to measure by fine tuning every detail.
Development of your survey may involve the collection of qualitative responses (such as open-ended or free response style questions) or quantitative responses (such as nominal or Likert scale questions), or a degree of combining the two. Once the response style has been established we will assist in determining the right questions to include and how to ask them in order to optimise data collection in service of evaluating your constructs.
Sampling method determination:
This decision with be based on feasibility criteria in line with what best serves your research parameters such as distribution capabilities, budget, timeline and study questions. Randomised probability or stratified sampling, convenience sampling, or more targeted selective sampling can be used dependent on circumstances.
Online platform navigation:
We can help set up and implement your survey on the platform of your choice such as Qualtrics, survey monkey or Google Forms.
Pilot testing:
This involves running a pilot test of the survey and analysing results for reliability and construct validity using statistics such as test-retest reliability and Chronbach’s alpha. From here any tweaks to the survey content can be made and confirmatory factor analysis can establish the statistical power of your final survey, which enables the appropriate sample size to be calculated.
Final survey analysis:
statistical analysis of your final survey data can involve factor analysis, principle components analysis, latent variable models, predictive models or many other options depending on the data and research questions involved.
Modelling of biomedical complex systems, whether on a micro or macro level, requires a multi-disciplinary approach and data obtained from diverse parties. We combine biostatistics and bioinformatics expertise in biomedical data analysis to collaborate with third party experts on the development of simulated CAS models.
Complex Systems Analysis including complex adaptive systems (CAS) simulations using:
- Systems dynamics models (SDM)
- Agent-based models (ABM)
- Hybrid models
Network Analysis including branching networks
Biostatistics help for R&D or exploratory studies
Whether you are a pharma or medical device start-up or SME, the analysis of early-stage testing and the biostatistical design of pre-clinical R&D studies for your therapeutic can help to provide insight and pre-clinical validation sufficient for entry into clinical trials. We can also perform initial research and comparative studies of existing therapeutics as a preparatory step towards evaluating the efficacy and safety of a novel therapeutic under development.
Biostatistical design & analysis of pharma & medtech clinical trials
Up-to-date biostatistical methods and clinical study design techniques ensure that your research is as innovative as your therapeutic. Assistance in the development of a statistical analysis plan for your trial, including sample size calculations, adjustments for missing data, and establishing early-stopping conditions are just some of the ways our biostatistical expertise could add value to your project.
Biomarker-related components can be woven into your clinical trial alongside traditional biostatistical approaches. Particularly in the context of pharmaceutical therapeutics, biomarker-guided clinical trials allow a novel therapeutic to be tailored to the patient, or a study population to be restricted to specific biomarkers. In either case, this increased precision improves the chance of a successful trial and allows treatment efficacy to be more clearly determined.
An adaptive clinical trial design, in the case of medical device trials, ensures that modifications can be made to your device during the trial. For pharmaceutical trials, they provide the flexibility to end an unproductive trial early or update treatment doses without running subsequent dose-finding trials. These services can help to establish the reasonable assurance of safety & effectiveness for your therapeutic.
Biostatistics Services for Pharma Start-ups & SMEs
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Biostatistics Services for MedTech Start-ups & SMEs
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We help you to develop and implement a clear plan of action from the outset of your project to ensure you remain on your optimal trajectory for the project duration.
In the initial stages we refine you research questions, fine tune your hypotheses for precise testing and enable a statistical analysis plan that optimizes your study results. Study power and required sample size can vary wildly as a function of statistical test and data type. Determining a statistical analysis plan up front is key as it enables us to calculate the most appropriate sample size and power for your study.
We advise on data collection and inputting techniques that reduce the need for data cleaning and preparation at the analysis stage, whether STDM and ADaM for clinical trials or the collation of RWD for modelling purposes. This helps to streamline the study process and enables a more efficient investigation of your research questions.
We use a rigorous approach to ensure the most appropriate statistical methodology has been implemented at the final analysis stage. We will fine tune your statistical analysis plan as needed in line with any unexpected developments arising from the nuances of real-world data. We follow protocol to ensure your final analysis is as accurate and free from bias to every possible extent. We then write up the results of your study and guide your interpretation to fully ensure that any conclusions are correct and concise.
Benefits of outsourcing biostatistics services for med-tech start ups and SMEs
Outsourcing biostatistics services can offer numerous advantages to med-tech start-ups and small to medium-sized enterprises (SMEs) engaged in R&D, product development, and clinical trials. Here are some of the key benefits:
- Specialised Expertise: Biostatistical consulting firms possess highly specialised expertise in the design and analysis of clinical trials. By outsourcing to these firms, start-ups and SMEs can tap into the knowledge and experience of seasoned biostatisticians, ensuring their studies are robust, statistically sound, and compliant with regulatory standards.
- Cost-Effectiveness: Building an in-house biostatistics team can be expensive for smaller companies. Outsourcing allows start-ups and SMEs to access top-notch biostatistical services without the overhead costs associated with hiring and maintaining a dedicated team.
- Access to Adjacent Experts: In the case of Anatomise Biostats, adjacent experts such as statistical programmers, bioinformaticians, data scientists can be added to projects without the hassle and cost of sourcing, recruiting and managing multiple temp employees or contractors to internal teams. This means that a project benefits from a broader range of experts at an expense that is limited to input needed for the project at hand.
- Faster Study Start-Up: Biostatistical consulting firms are well-versed in the intricacies of clinical trial design and planning. Their expertise streamlines the study start-up process, reducing delays and accelerating the time to market for novel therapeutics.
- Optimized Resource Allocation: Outsourcing biostatistics services enables start-ups and SMEs to focus on their core competencies, such as R&D and product development. It allows them to allocate their resources efficiently while leaving the complex statistical aspects of clinical trials to experts.
- Improved Study Design: Proper study design is crucial for obtaining meaningful results. Biostatistical consultants can guide start-ups and SMEs in choosing the most appropriate study design, sample size calculations, and statistical analyses, increasing the likelihood of successful outcomes.
- Mitigation of Risks: By working with experienced biostatisticians, start-ups and SMEs can identify potential risks and challenges early in the planning phase. This proactive approach helps mitigate risks and ensures a smoother trial process, reducing the likelihood of costly mistakes.
- Regulatory Compliance: Anatomise Biostats is well-versed in regulatory requirements and industry standards. Outsourcing biostatistics services to us ensures that clinical trials are conducted in compliance with relevant regulations, streamlining the regulatory submission and approval process.
- Data Quality and Integrity: Biostatistical consultants play a vital role in data quality and integrity, including monitoring data for inconsistencies, missing values, and outliers. Their involvement enhances the reliability and credibility of the study results.
- Interpretation of Results: Biostatisticians possess the skills to interpret complex data, providing valuable insights from clinical trial outcomes. Their expertise helps start-ups and SMEs understand the significance of study findings and make informed decisions for future development strategies.
- Flexibility and Scalability: Outsourcing biostatistics services offers flexibility, allowing start-ups and SMEs to tailor the level of support to their specific needs. As projects evolve, the scope of biostatistical services can be easily adjusted, providing scalability without long-term commitments.
Outsourcing biostatistics services provides med-tech start-ups and SMEs with access to expert knowledge, cost-effective solutions, and streamlined trial processes. By leveraging the expertise of biostatistical consulting firms, companies can enhance the quality and efficiency of their clinical trials, facilitating the successful development and market entry of their novel therapeutics.
More information on the types of outsourcing models we offer.
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