Clinical trial data lays the foundational evidence for enabling access to new treatments and improving medical care. Early integration of biostatistics – the application of statistics to health-related scientific research fields – is crucial in planning, executing, and interpreting clinical trials effectively.
Clinical trial management has always been a complex endeavor with trials spanning across multiple regions and extended periods of time. In recent years, this has been further complicated with the requirements that more innovative medical practices – such as personalized medicine – bring to operationationalising these trials.
What role do biostatisticians play in clinical trial design?
“The clinical study protocol is the ‘recipe book’ for the trial, where the study objectives, endpoints, sample size, and more, are defined,” according to Maria Bertilsson, Biostatistics Group Manager at LINK Medical. “Determining an adequate sample size, which drives the number of patients to recruit and thus the cost of the trial, is one of the foremost challenges in trial design.”
Biostatisticians use their expertise in statistical analysis and study design to advise on the study protocol, determine appropriate sample size, and ensure that the enrolled patients are correctly randomized. They also advise on collection and curation of data, and collaborate to present the technical data outputs from the trials in an edible way, ensuring appropriate presentation and correct interpretation of the results.
“To begin with, finding a right balance in the trial sample size is crucial. Including too many patients can be costly, unethical, and can pose feasibility challenges. Alternatively, having too few patients can lead to underpowered studies where the treatment effect remains inconclusive due to the small sample size,” said Bertilsson.
Poorly designed and underpowered trials can have significant long-term consequences as they hinder the development of effective medical treatments. By integrating biostatistics into the trial design, researchers can ensure robust studies, thus enabling reliable results and informed downstream decision-making.
Bertilsson highlighted that the biostatistician can also contribute to other non-statistically-driven trial design specifications before sample size considerations can come into play: “By consulting statisticians on important design features such as choice of study objectives, endpoints, blinding, comparator groups and more early on, researchers can enhance the overall trial design.”
Additionally, using statistical advice in a cross-functional setting can help ensure that the trials are not only scientifically sound but also logistically feasible, she continued.
“A close collaboration between statisticians, clinical experts, data managers and others in the trial management team will help inform feasibility considerations such as patient availability, site selection, multicenter set-ups, choice of data sources for analyzing real-world data and more,” elaborated Bertilsson.
The complexity with choosing the ‘right’ biostatistical methodology
The many different use cases of biostatistics across trial planning is further complicated by the wide range of statistical methods and analyses that can be applied to make sense of the data collected from trials.
In fact, the statistical methodology applied can vary on a case-to-case basis and needs to be individually tailored for the product and the research questions being tested in a clinical setting, explained Bertilsson.
Further, even the type of statistical techniques applied may evolve over the course of clinical development.
“In early-phase trials, the patients are fewer in number,” she explained. “Thus, biostatisticians primarily focus on descriptive statistics – involving the use of measures such as mean and median – to help summarize and display data in a simple and interpretable manner. This establishes a foundation for subsequent study design and analyses in later phases of clinical development.”
On the other hand, the later trial phases typically involve larger patient groups. This means the resulting analysis can leverage inferential statistics which helps researchers to draw conclusions based on hypotheses. These can be used to make inferences, for instance, about the differences in effectiveness between treatments being tested in a trial.
“The inferential statistical methods utilized can range from basic tests such as t-tests for comparing treatment groups, to longitudinal analysis for analyzing complex data over multiple time points within a trial,” Bertilsson mentioned.
This makes it challenging to choose the ‘right’ methodology given each individual circumstance; expert statistical guidance is key in pre-specifying a statistical plan that aligns with the study design and its objectives. In fact, pre-specification of the analyses is key in order to avoid the introduction of bias, and is scrutinized by regulators, stressed Bertilsson.
Considering the complexities involved in optimally leveraging biostatistics, Bertilsson stressed the importance of involving statisticians in clinical study planning from the outset, even before the specific research questions are formulated, even though the framework is set by clinical researchers.
Early adoption of biostatistics ensures effective trial design, enabling robust research outcomes and helping trial sponsors mitigate risks in a feasible and cost-effective manner.
The evolving future of biostatistics in clinical trial management
Looking ahead to the future of trial design, Bertilsson hypothesized, “The future probably holds a combination of more complex studies and large “simple” studies. For the ‘simple’ trials, the simplicity would be centered on ensuring convenience for the patient as well as the clinical sites involved, even though data processing would get more challenging and time consuming.”
The increasing use of innovative and sometimes complex trial designs – including the integration of real-world data, personalized medicine and artificial intelligence – in recent years has started to cement the role of biostatisticians as crucial advisors to navigate the complexity of early trial planning.
As patient sample sizes get smaller with precision medicine, even as volumes of information get larger with big data, the impact of this advisory role is only expected to grow.
While acknowledging the rising importance of biostatistics in this field, Bertilsson cautioned that it is critical to not lose sight of the fact that the success of a clinical trial ultimately relies on a team effort.
“Accurate data collection and thorough processing is as critical to trial design as is applying the ‘right’ statistical techniques. This makes cross-functional collaboration key,” she added. “At LINK Medical, the close collaboration across our expert teams in various functions is an important advantage that results in a faster path to approval of treatments for patients in need.”
“By working with an experienced team and the latest technologies, LINK Medical has delivered robust and flexible solutions to our trial sponsors, enabling them to seamlessly take a decentralized approach that supports patients, sites and all the other stakeholders involved.”
The Biostatistics team also works closely with medical, clinical operations, pharmacovigilance as well as regulatory professionals. Over the last 28 years, this collaborative working style has allowed the LINK Medical team to deliver over 500 clinical trials with study sites in more than 50 countries.
With a total of 28 years of experience working across a wide range of therapeutic areas and supporting Phase I to IV studies, the LINK Medical Biostatistics team has 8 biostatisticians providing specialized statistical expertise on study design, data collection, validation, and analysis. To learn more about the Biostatistics team and how they can integrate biostatistics into your clinical trial to optimally meet your research objectives, visit the company’s website or contact us directly at Info@linkmedical.eu.