FAQ

Biostatisticians use statistical methods to analyse data related to the biological and health sciences. They may work on topics such as disease prevalence, drug efficacy, or genetic relationships.

Bioinformaticians use computational techniques to analyse and interpret biological data, such as DNA sequences or protein structures. They may work on tasks such as annotating genomes, predicting protein function, or analysing gene expression data.

Biometricians use statistical methods to analyse data related to the characteristics of living organisms. They may work on topics such as growth patterns, reproductive success, or the genetic basis of traits.

Epidemiologists study the distribution and determinants of diseases in populations. They may work on tasks such as identifying risk factors for diseases, evaluating the effectiveness of interventions, or tracking the spread of infectious diseases.

In general, these fields are all related to the use of data and statistical methods to understand phenomena in the biological and health sciences. However, they each have their own specific focus and areas of expertise. Each of these experts should hold a master's degree or higher in there respective disciplines.

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There are several advantages to combining biostatistics, bioinformatics, data science, and complex systems approaches to data analysis using real-world data (RWD) and clinical trial data for product development in the med-tech and pharma industries:

1. Improved accuracy: By using advanced statistical techniques and data science approaches, you can better understand and analyse the data collected from clinical trials and RWD, leading to more accurate conclusions about the safety and efficacy of your product.
2. Enhanced efficiency: By using bioinformatics and complex systems approaches, you can more efficiently analyse large and complex datasets, allowing you to make faster and more informed decisions about product development.
3. Greater understanding: By using RWD and clinical trial data in conjunction with biostatistics, bioinformatics, and complex systems approaches, you can gain a deeper understanding of the factors that influence the effectiveness of your product and how it is used in real-world settings.
4. Enhanced decision-making: By using advanced data analysis techniques, you can better understand the risks and benefits of your product, which can inform decision-making and help optimize product development.

In the context of clinical trials, clinical study design, statistical analysis plans (SAP), the statistical section of the study protocol, interim and final analysis of clinical data, as well as reporting and interpretation of results are our main focus. While these aspects alone are not sufficient to execute a clinical trial and bring a product to market, there are several advantages to working with a boutique biostatistical consulting firm, rather than solely relying on a full-service CRO. For a for a med-tech or pharma start-up the following points may be particularly relevant:

1. 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.
2. 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.
3. Cost: As a boutique firm we tend to have lower overheads and may be able to offer more competitive pricing than larger CROs.
4. Flexibility: Being a younger business we tend to be more agile and able to adapt to changing project needs or timelines more quickly than larger CROs.
5. Personalised service: We offer a more personalised and customised approach to consulting, as having fewer clients allows us to devote more care and attention to each project.
6. Niche expertise: As a boutique firm we may have specific expertise in certain areas or technologies that larger CROs may not have. This can be particularly useful for a life sciences start-up looking for specialized support.
7. Stronger relationships: Working with a boutique firm may allow for the development of stronger, more personal relationships with the consultants, which can be beneficial for ongoing projects or future collaborations.

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There are many cutting-edge data analysis techniques and statistical methods that are increasingly used in clinical research. Some of these include:
1. Machine learning: Machine learning algorithms can be used to predict outcomes, identify patterns, and make decisions based on data. They are particularly useful for analysing large and complex datasets.
2. Bayesian statistics: Bayesian statistics is a statistical approach that allows researchers to incorporate prior knowledge and subjective beliefs into statistical analyses. It can be particularly useful for making predictions and for dealing with small sample sizes.
3. Network analysis: Network analysis is a method for analysing relationships between entities. It is often used in clinical research to study the relationships between genes, proteins, and other biological molecules.
4. Longitudinal data analysis: Longitudinal data analysis is a method for analysing data collected over time. It can be used to study changes in outcomes or exposures over time, and to identify factors that may be associated with those changes. One of the most prevalent examples of longitudinal analysis in clinical trials is survival analysis.
5. Meta-analysis: Meta-analysis is a statistical technique that combines the results of multiple studies to provide a more precise estimate of the overall effect of an intervention or exposure. It is often used to synthesize the results of multiple clinical trials and can play a role in sample size calculation. Various approaches to meta-analysis exist, including Bayesian meta-analysis.

Publicly available data can be used in large-scale bioinformatics analyses before clinical trial design to tailor the participant population to the disease in question as each disease has its own distinct demography. Hospital records spanning long periods of time can offer insights into the demographic makeup of a specific disease. Examining these records and looking for trends in patient populations can allow researchers to build a picture of how factors such as sex, age, and race are distributed across patients with the disease. Further investigation may even give insights on more specific circumstances, including geographic regions, which could indicate specific areas or hospitals to sample from.
Having this information before carrying out a clinical trial enables the recruitment of patients that better represent the disease population for the clinical trial. It should be noted, however, that there are limitations that need to be considered and not every disease population can be included in a clinical trial. Conditions that affect vulnerable groups including children and pregnant women cannot be examined so easily due to the increased risks and ethical considerations associated with trial participation.
Having a trial population that proportionally represents the disease population in terms of demographic features means that findings during the trial related to therapeutic effectiveness can be generalised and applied to the disease population. As a result, the therapeutic will have more predictable effects on patient’s post-market approval, and less adverse effects or reasons for recall.
This approach can be taken a step further by examining the genomic profiles of the disease population. Many diseases are associated with unique genomic biomarkers, often in the form of gene expression and mutations. These markers can be used in both the diagnosis of a disease and as indications for risk factors for becoming ill eventually. Existing data and literature can be mined for biomarker information associated with the disease under study, and if used to inform patient recruitment, could result in even more representative trial populations.
In the future, in silico trials and virtual patient populations based on specific disease demographics may be used to complement clinical trials. By using patient data from clinical databases to create a computational model that reflects age, sex, ethnicity, and disease burden, biological variability amongst individuals could be simulated. Including interactions between anatomy, physiology, and blood biochemistry in the model could further predict the impact of a therapeutic. Although far from current reality, this approach has been applied in a brain aneurysm medical device study with encouraging results, illustrating its potential use in the future.

multi-omics analyses can provide a higher resolution understanding of disease mechanisms by sub-setting patients, and allow for quantitative measurement of many putative drug targets with known binding affinities and kinetics. This facilitates the detection of potential drug-induced side-effects and additive or synergistic effects of multiple targets in advance, and the selection of drug targets likely to have higher safety and efficacy. In pre-clinical validations, prognostic and predictive biomarkers can account for variability in treatment response, be used to identify optimal dosage, and improve upon the generation of testing and animal models to represent the human disease physiology more accurately, thus increasing the likelihood of a novel therapeutic progressing to clinical trial and from trial to market successfully. Furthermore, omics data and biomarkers can aid in the discovery of unknown secondary targets of existing drugs that have already been tested in humans, reducing the time and budget required for a clinical trial.

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Biomarkers identified from genomic, transcriptomic, proteomic, and biomolecular data can improve clinical trial design in several ways. Biomarker data can be used to screen prospective patients for a trial so that participants more likely to respond well to the treatment are selected. Incorporating this screening procedure into the design of a trial increases the likelihood of demonstrating treatment efficacy, and therefore the likelihood of success. In addition, stratifying patients based on their biomarker profiles and applying clinical intervention based on these subgroups can decrease the incidence of adverse effects. As adverse effects are taken into account in sample size estimation for a clinical trial, using biomarkers to screen and stratify participants can potentially decrease the required sample size. This in turn could prevent delays associated with needing to re-do studies while stratifying participants through examining the biomarkers of a subpopulation who responded well in a certain trial phase could prevent a trial from failing.  Using biomarkers to monitor safety and efficacy throughout a trial can account for variability in treatment response and help to further understand why response to a therapeutic may stop or change over time. Optimised patient care through biomarker monitoring may encourage participant retention, allowing progression to the next trial phase.  Overall, these factors contribute towards lowering the financial risk of a trial and quickening the time taken for a therapeutic to get to the market, as well as reducing the risk of being recalled from the market afterwards.