Biostatistics is the application of statistical methods and principles to scientific research in biological, medical and health-related fields. 

It plays a critical role in the design, execution and analysis of clinical trials to ensure robust and reliable results. This can be applied to different areas of science, including public health, clinical research, epidemiology, genetics and even environmental science.

If you’re implementing biostatistics into your clinical trial, you need to ensure this is done as accurately and effectively as possible. 

To help you do this, we’re going to take a look at the key steps, considerations and best practices for incorporating biostatistics effectively into your clinical trial design.

1. Understanding the role of biostatistics in clinical trials

The first important step is understanding the role biostatistics plays in clinical trials. 

Essentially, biostatistics involves the application of statistical principles to biological and medical research. In clinical trials, this helps to:

• Design a study that addresses research objectives efficiently
• Minimise bias and variability
• Determine the appropriate sample size
• Analyse data periodically to draw valid and reproducible conclusions

By introducing biostatistics from the earliest stages of trial design (phase 1), researchers can maximise the reliability of their findings and ensure compliance with regulatory requirements.

2. Defining the research question

Once you’ve built and understanding of biostatistics in clinical trials, you can begin to design your own. 

Every clinical trial must begin with a clear research question, often articulated in terms of a hypothesis. Biostatistics helps translate this question into specific, measurable objectives. For example:

Your primary objective may be to determine whether ‘Drug A’ reduces blood pressure more effectively than a placebo over three months.

The secondary objective could then be does ‘Drug A’ improve quality of life compared to the placebo.

By implementing statistical considerations, you can ensure that these objectives are precise, measurable and aligned with clinical relevance. This will also help you to choose the right design for your study. 

3. Choosing the right study design

In order to implement biostatistics effectively, you need to ensure you select the appropriate study design. This is critical for generating reliable and interpretable results. Some of the most common designs include:

• Randomised Controlled Trials (RCTs): This is where participants are randomly assigned to treatment or control groups.
• Crossover trials: Participants receive multiple treatments in a sequential order, minimising inter-patient variability.
• Parallel design: Participants are divided into separate groups that receive different treatments concurrently, without crossover.
• Observational studies: This design is useful for studying real-world effectiveness, though it is more prone to bias.
• Adaptive Design: This allows for modifications to the trial procedures (e.g., dosage or sample size) based on interim results without compromising validity.

You should choose the right design for your trial using biostatistics as a guide, considering factors like population characteristics, endpoint types and ethical considerations.

Determining sample size 

As part of designing your trial, you need to determine the sample size. Biostatistics is applied by using statistical formulas and power calculations that consider the expected effect size, significance level, power and variability in the data. 

This ensures the study has sufficient power to detect meaningful differences while avoiding unnecessary costs or ethical concerns.

Randomisation and blinding

Randomisation reduces bias by ensuring treatment groups are comparable at baseline.

Biostatistics can be implemented to guide the development of randomisation methods (e.g., simple, stratified, or block randomisation) to evenly distribute participants across the different placebo and treatment groups.

It also informs the design and maintenance of blinding procedures to reduce bias, ensuring the reliability and validity of trial outcomes.

Defining endpoints

Endpoints are the outcomes used to evaluate the effectiveness of a treatment or intervention, and these should be specified in advance to prevent selective reporting.

Biostatistical input can be used to ensure endpoints are clinically relevant, measurable and categorised appropriately. These endpoints can then be predefined and set out in the trial design. 

Managing Interim Analyses

Biostatistical expertise must be introduced to ensure interim analyses are conducted appropriately without compromising the integrity of the trial.

Interim analyses involve examining data at those aforementioned predefined points during the trial. These analyses can inform decisions about trial continuation, modification or termination.

3. Selecting statistical methods for analysis

The choice of statistical methods depends on the nature of the data and the study design. Some of the most commonly used methods include:

• Descriptive statistics: Summarising data through means, medians and standard deviations.
• Inferential statistics: Testing hypotheses using methods like t-tests, ANOVA or regression analysis.
• Survival analysis: Analysing time-to-event data using Kaplan-Meier curves or Cox proportional hazards models.
• Multivariable Models: Adjusting for confounders using regression techniques

It is essential to predefine analysis methods in a statistical analysis plan (SAP) to maintain transparency. Again, this should be set out in the initial design of the trial. 

4. Leveraging the right software tools

Biostatistical analyses often require specialised software, such as SAS, which is widely used for data analysis and regulatory submissions or SPSS, user-friendly software for basic and intermediate analyses.

The selection of this software depends on the trial’s complexity and the biostatics team’s expertise.

5. Interpreting and reporting results

Biostatistics supports the accurate interpretation of trial results by contextualising the trial’s findings and relating statistical significance to clinical relevance.

It can also be implemented to avoid misinterpretation and to communicate the results clearly using graphs, tables and plain language to convey results effectively.

Post-trial analyses

On top of this, biostatistics extends beyond the primary analysis phase; this can also contribute towards: 

Subgroup analyses: Exploring the outcomes in specific patient groups

Meta-analyses: Combining results from multiple trials for broader insights

Real-World Evidence: Analysing post-marketing data to assess long-term safety and effectiveness

These post-trial analyses are important for informing future research and clinical practices. 

6. Collaborating with biostatisticians

Effective implementation of biostatistics in your trial design requires close collaboration between statisticians, clinicians and other stakeholders. Early and ongoing engagement ensures the alignment of goals, bridging clinical and statistical perspectives.

This can also lead to efficient problem-solving, proactively addressing challenges, and producing more high-quality outputs by producing analyses that are both scientifically rigorous and clinically meaningful.

In summary 

Implementing biostatistics in clinical trial designs is a multifaceted process that ensures scientific rigour and reliability. 

From formulating your research questions to analysing and reporting the results, biostatistics underpins every stage of a clinical trial. 

By adhering to best practices and collaborating with experts, researchers can maximise the impact of their findings and contribute meaningfully to modern medical science.