Introduction
Biostatistics is undoubtedly one of the core disciplines in health sciences but for some students is a source of anxiety because of its perceived mathematical difficulty. No, biostatistics isn’t only about formulas, it’s about understanding data to facilitate the making of better health decisions.
In medicine, nursing, public health and other health related professions, professionals are constantly looking at patient information, research and disease information, treatment outcomes and so on to interpret what is going on. It would be a guess that lacked evidence if there were no biostatistics. A solid conceptual foundation is therefore a key component of the education for all health students.
Biostatistics, in essence, is a body of tools that is used to gather, arrange, analyse and interpret biological and health-oriented information. This provides ease of movement between the raw numbers and insight into evidence that can lead to improvements in the care of the patient and the health of the population.
Before delving into this field, it is essential to delve into the basic nature and extension of this discipline and learn basic principles that underlie all statistical thinking in the health sciences, e.g. biostatistics.
A more detailed description of the academic definition and applications are available here:
fundamental principles of biostatistics
What is Biostatistics?
Biostatistics is the statistical analysis of biological, medical and public health data. Design, collect, analyze, and interpret data, and reach scientifically valid conclusions for decision making.
Biostatistics is specially designed for health related problems as compared to the general statistics. It is used in:
- Trials of new medicines or treatments in people or clinical trials
- Field surveillance of disease epidemics
- Government-funded studies examining programmes
- Knowing the genetics and conducting laboratory research to understand biological processes.
Biostatistics is a field that can convert information about health into knowledge that impacts medical practice and policy.
Importance of biostatistics in the health sciences field.
Biostatistics is a pivotal aspect in today’s health care systems. This is crucial in a couple of important points:
1. Evidence-Based Medicine
Modern medicine is centred on evidence and not on intuition. Biostatistics can be used to analyze clinical research studies and decide if a treatment is effective or ineffective.
2. Public Health Decision-Making
The public health authorities are dependent on statistical data to monitor disease occurrence, detect risk factors, and plan intervention programmes.
3. Research Validity
Through biostatistics, the results of research are assured scientific validity, reliability, and unbiasedness. Research conclusions can be misleading if not performed using appropriate statistics.
4. Resource Allocation
Statistic data is used by health administrators to optimally distribute scarce resources, including vaccines, beds, doctors, nurses, and other personnel.
Learning Outcomes for All Health Students!
In order to develop a sound background in biostatistics, students will need to have a grasp of several fundamental concepts that are the basis of statistical analysis.
1. Variables
Characteristic, number, or quantity that is measurable or categorizable and can take on different values among different persons or observations.
Types of Variables
a. Qualitative (Categorical) Variables
These are variables that describe “Qualities/Categories”. They have nothing to do with numbers.
Examples:
- Gender (male/female)
- Autoimmune disease (graves’ disease, Hashimoto’s thyroiditis)
- Disease status (positive/negative)
b. Quantitative (Numerical) Variables
The variables are measurable quantities.
Examples:
- Age
- Blood pressure
- Weight
- Temperature
There are more categories for quantitative variables:
- Discrete – values that can be counted (e.g., number of patients)
- Continuous variables: Values that can be measured (such as height, weight)
It is important to comprehend variables since they will dictate on which statistical analysis to be used.
2. Populations and Samples
Population
A population is the whole set of people or things that an investigator wishes to include in a study.
Example:
The complete population of patients that has diabetes in a country.
Sample
A sample is a collection of individuals in the population who are studied.
Example:
Schools selected with the objective of studying 500 diabetic cases in the hospitals of a city.
It is often not feasible to study a whole population, researchers study a sample and they use this to make inference about the larger population.
The results of these conclusions are subject to the accuracy of the results; the greater the accuracy that can be obtained the more accurate these findings will be.
3. Data Types
A biostatistician’s job critically depends on data. It’s basic data and numbers gathered while conducting research.
Types of Data
a. Primary Data
Data that is gathered directly by the researcher for the purpose of the research.
Survey responses from patients, e.g.
b. Secondary Data
Collected by others and used for analysis.
Example: Government health reports, Hospital records.
Levels of Measurement
It is crucial to know the levels of data measurement:
- Nominal: Categories without order (e.g. blood group)
- Ordinal: Categories that are ordered (e.g., pain scale: mild, moderate, severe)
- Interval: Numerical scales that do not have a zero (such as temperature on the Celsius scale)
- The ratio: A numerical scale having a natural zero value (such as weight, height).
4. Basic Statistical Measures
Statistics provide a way of summarizing and interpreting information.
a. Measures of Central Tendency
These refer to the central or median value of the data-set.
- Mode: Most frequent value(s) in the data set
- Median: middle value of data arranged in an order
- Mode: the value that appears the most times.
There are different types of measures available that offer up different aspects of the data.
b. Measures of Dispersion
These are measures of variation of the data.
- The data supports the graph.The range is the difference (highest value minus the lowest value) of the data.
- Median: Middle value for a set of numbers
- Standard deviation: A measure of the variation in a set of values from the mean or average value.
In health research, understanding of variability is crucial since it demonstrates the degree of consistency or inconsistency of the results.
5. Probability – Health Science
Probability is a measure of the chances of an event happening. In biostatistics, it is used to calculate the risks and forecast outcomes.
For example:
- The odds that a given individual will contract a disease.
- The chance of surviving the treatment.
- The threat of a negative impact caused by drugs.The danger of adverse reactions from drugs.
Many of the more complicated statistical tests employed in clinical research are based on probability.
6. Data Presentation and Interpretation
Information has to be presented neatly to be of any use. Common methods include:
Tables
- Bar charts
- Pie charts
- Histograms
- Line graphs
Using visualization, researchers and health professionals can visualize data patterns to gain quick insights and make informed decisions.
Interpretation involves the process of understanding the patterns and drawing reasonable conclusions.
7. Basic concepts in the design of epidemiological studies
In addition to providing tools for analysis, it is used in designing studies (biostatistics). The results of a study are accurate and reliable if it is well designed.
Key aspects include:
- To minimize bias, a random sampling process was used.
- Sample size determination
- Control groups in experiments
- The methods of data collection are appropriate.
It doesn’t take a good analysis to result in bad conclusions if the study design is flawed.
Difficulties in Learning Biostatistics.
Biostatistics is a challenging course for many health students for the following reasons:
- Fear of mathematics
- Poor written comprehension of abstract ideas
- This is because there is a shortage of application of the knowledge imparted.
- Inadequate teaching methods
But through practice and examples from the real world, these can be surmounted.
Effective learning of Biostatistics.
The student should:Students should have the following levels of confidence in biostatistics:
- Pay attention to ideas rather than formulas
- Simulate using actual health data
- Use statistical software e.g. SPSS, R, excel
- Make connections with concepts and clinical situations.
- Practice regularly – don’t cram.
The subject is best learned through practical experience.
Conclusion
A crucial subject relating data and decision making in health sciences is Biostatistics. StuDentiEngage® introduces key concepts of statistics and variables, populations and samples, data types and statistics/measurement, providing students with a solid basis for understanding more complex topics in research and analysis.
Most importantly, biostatistics helps health professionals make data-informed decisions that will have a positive effect on an individual’s health and will help support public health.
These core concepts are essential to academic achievement and provide a foundation for more contemporary content and experiences in medicine, nursing, and public health practice.
