Biostatistics plays a critical role in key fields like biology, medicine and public health, making it possible to track and understand the spread of diseases. It is also critical for designing clinical trials and experiments, predictive modelling, clinical decision support and so much more.

But this is a very complex subject and one that must be fully understood if you hope to utilise biostatistics for clinical development.

With that in mind, let’s take a look at what this statistical approach includes, key terminology and real-life applications, as well as recent examples of how biostatistics have supported scientific fields and public health.

Let’s get started.

What is biostatistics?

The dictionary definition of biostatistics is the branch of statistics that deals with the collection, analysis and interpretation of biological data, especially data relating to living organisms, human biology, health and medicine.

But more than this, it is a field of science that applies statistical methods to biological, medical and health-related research.

It plays a crucial role in designing clinical studies and trials, analysing data and interpreting results in these various areas of the life sciences.

Key terminology in biostatistics

Understanding some of the key biostatistics terminology is essential for interpreting and conducting research in the life sciences. With that in mind, here are some fundamental terms you should know:

General: 

• Clinical Trials: A clinical trial is a biomedical or behavioural study of human participants used to answer a specific question or hypothesis.
• Randomised Controlled Trials (RCTs): This type of trial is when participants are randomly assigned to receive either the intervention or a placebo/control, allowing for strong inferences and better results.
• Population: This term refers to the group of individuals that you want to study or make inferences about (for example, participants with a specific illness).
• Sample: This is a subset of the aforementioned population and is used to make conclusions about said population.
• Cohort Studies: These follow a group of people over time to assess the impact of various predetermined factors on specific outcomes.
• Case-Control Studies: These studies compare individuals with a specific condition (cases) to those without it (controls) to identify factors associated with that condition.
• Cross-Sectional Studies: These are studies that analyse data from a population at a single point in time.
• Blinded Studies: Blinding prevents bias by ensuring that participants and/or researchers do not know who receives which specific intervention.

Variables:

• Independent Variables: This refers to a variable that is manipulated or categorised to observe its effect on the dependent variable (such as a type of treatment).
• Dependent Variable: On the other hand this is the outcome variable that is measured and influenced by the independent variable (for example patient recovery rate).
• Confounding Variable: This is an external variable that may affect the relationship between the previous two variables, potentially leading to biased results.

Bias: 

• Bias: An error within the design or conduct of a trial that distorts the results and leads to inaccuracies.
• Selection Bias: This is the systematic differences between the characteristics of selected individuals and the population intended to be analysed during the trial or study.
• Measurement Bias: Similarly, this is any errors in data collection that may result in inaccurate measurements and insights.

Descriptive:

• Mean: The average value of a set of data (e.g. sum of data/number of data).
• Median: The middle value in a set of ordered data (e.g. the central figure when values are in order from smallest to largest).
• Mode: The most frequently occurring value in a data set.
• Range: This is the difference between the highest and lowest values.
• Variance: A measure of how much the data points differ from the mean.
• Standard Deviation: The square root of the variance, indicating the spread of data around the mean.

Applications of biostatistics

Although we’ve already briefly touched on some of this in the initial definition of biostatistics, let’s now take a closer look at some of these applications and how they impact our everyday lives.

Firstly, there is public health. This field can be used for assessing the impact of public health interventions (such as a new vaccination program) and studying the spread of diseases across communities or populations.

This technique is also key for designing clinical trials and analysing the results of experiments in order to test new medical treatments or drugs. Biostatistics can be used to advance genetic research, allowing doctors and scientists to investigate the relationship between genetic factors, diseases, treatments and more.

On top of this, biostatistics can also support environmental health. It does this by assessing the impact of environmental exposures (like pollution) on specific health outcomes.

So, as we can see, this is an essential discipline that supports a vast amount of evidence-based research and decision-making in the life science fields.

Examples of recent biostatistics work

To dive even deeper into these applications, let’s take a look at some real-world examples. Understanding how these techniques have been applied to life sciences, especially over the last decade or so, can be very beneficial in building a stronger understanding of the field as a whole.

Research during COVID-19 pandemic

The most recognisable role biostatistics has played in recent years is research during the COVID-19 pandemic.

This technique was used to design and analyse clinical trials for vaccines, as well as

epidemiological modelling that mapped the spread of COVID-19, helping to predict the course of the pandemic and evaluate the impact of public health interventions like lockdowns.

Dietary research

Nutritional epidemiology is also a huge project right now, as biostatistics are being used in large cohort studies to understand the relationship between diet and chronic diseases.

With one in eight people in the world living with obesity (as of a 2022 study) and many suffering chronic diseases as a result, this is very much at the forefront right now.

Recent work has focused on the role of dietary patterns in the prevention of conditions like diabetes, heart disease and obesity.

Of course, these are just two examples. There is a lot going on behind the scenes as scientists focus on ageing and Alzheimer’s diseases, cancer, HIV/Aids, Malaria eradication and more.