Targeted literature searches are a fundamental part of writing clinical manuscripts that will meet the standards of high-quality journals and contribute meaningfully to evidence-based practice. When physicians write clinical manuscripts, utilizing a targeted literature search can identify high-quality, relevant, and current evidence. While a general literature review is useful, a targeted literature search is specific to the clinical question and should be completed through frameworks established, such as PICO (Population, Intervention, Comparator, Outcome) and PRISMA [1].
An Overview of Biostatistics in Clinical Research
- Home
- Academy
- Research Impact
- An Overview of Biostatistics in Clinical Research
Biostatistics in Clinical Research
Interesting topics
An Overview of Biostatistics in Clinical Research
Biostatistics is the cornerstone of clinical research, converting sterile data into medically relevant information that can lead to groundbreaking discoveries. Biostatistics is integral in research from study design to regulatory submission to ensure clinical trials produce valid, reproducible, and meaningful results. [1]
1. What are Biostatistics?
Biostatistics applies statistical principles to biological research medical and health-related research. In a clinical trial, biostatistics contributes to studying a proposal by designing the study, analysing the data generated from a study and creating inferences from an analysis to inform appropriate practical applications for interventions relative to both safety and efficacy. [2]
The key contributions of biostatistics are
- Study design
- Data analysis
- Interpretation of results
- Determining safety and efficacy of interventions
2. Hypothesis
Research begins with an established knowledge gap followed by a hypothesis to derive predictions. In this section we will cover.[3]
- Definition and Types of Hypotheses: null hypothesis (H₀), alternative hypothesis (H₁), directional hypothesis, non-directional hypothesis
- Characteristics of a Good Hypothesis: testable, specific, measurable, relevant to the research quest
- Study Designs: experimental, observational, cohort, case-control, cross-sectional studies.
3. Hypothesis Testing
A hypothesis is either accepted or rejected based on supporting data. Key components of this are. [4]
- Interpretation using the p-value
- Misinterpretation of the p-value (discussed in detail below)
4. Power of a Statistical Test
Contemplates the probability of finding an effect when one exists.[5]
- Lots of power reduces the risk of making Type II errors (false negatives).
- Power is influenced by the sample size, effect size, significance level (α), and variance.
4.1. Sample Size
Sample size denotes the amount of variability or cases observed or surveyed in a study. Sample size calculation is important for reliability, validity, and generalizability.
- Study Power: A study’s power increases with the sample size that minimizes Type II errors.
- Confidence though precision: Larger sample sizes produce less variable confidence intervals and more reliable estimates.
- Factors influencing sample size include: the effect size, variability in the population, the level of significance (α), and the amount of power (1–β) desired.
- How to calculate sample size: formulas, software programs that do it, or from sample size tables associated with the type of study undertaken.
4.2. Relative Risk & Odds Ratio
Epidemiological measures of association [6]
| Relative Risk (RR) | Odds Ratio (OR) |
|---|---|
|
|
4.3. Correlation & Regression
Statistical methods to evaluate relationships among variables include. [7]
| Correlation | Regression |
|---|---|
|
|
Types
| |
5. Multiplicity Issue
Multiplicity occurs when two or more statistical tests are conducted simultaneously and the risk of a Type I error (false positives) increases. [8]
Problem: When you test multiple hypotheses, it increases the probability of finding a result that is statistically significant by chance.
Solutions:
- Bonferroni Correction: Uses a Bonferroni correction to adjust α by dividing it by the number of comparisons.
- False Discovery Rate (FDR): Controls the proportion of erroneous results among significant results.
- Pre-specification: limit the number of primary outcomes and comparisons will limit multiplicity.
6. Misconceptions with P-Values: Research-Based Insights
Although p-values are commonly used in statistical hypothesis tests, they are often misinterpreted. Some prevalent misconceptions include the following. [9]
- P-value probability that H₀ is true: P-value expresses how likely it is to observe the data if H₀ is true.
- Effect size: small p-values do not necessarily imply a practically significant effect of interest. In addition, it is easy to get a significant p-value when the sample size is large, even for trivial effects.
- Significance vs. Importance: noting other factors, p<0.05 does not make it relevant in practical terms.
- Non-significant no effect: A large p-value could indicate no effect, but it can also occur trivially with small samples or variability within the sample.
- Multiple testing: p-values can be inflated, so they require corrections.
- There is an alternative hypothesis: A low p-value suggests that the data are inconsistent with H₀ but it doesn’t verify H₁.
Connect with us to explore how we can support you in maintaining academic integrity and enhancing the visibility of your research across the world!
conclusion
Biostatistics serves as the foundation of clinical research by helping with study design, data analysis and valid conclusions. Methods like hypothesis testing, sample size calculation, correlation, regression, and risk assessment can help define studies as scientifically sound and clinically useful. Understanding the nuances of multiplicity and p-value misinterpretation will help you explicitly interpret the data, which will be beneficial for the overall credibility, reproducibility and importance of the research, while supporting the practice of evidence-based medicine.
An Overview of Biostatistics in Clinical Research? Our Pubrica consultants are here to guide you. [Get Expert Publishing Support] or [Schedule a Free Consultation]
References
- (N.d.). Retrieved September 4, 2025, from Sciencedirect.com website: https://www.sciencedirect.com/science/article/abs/pii/S0378375898002249
- What is biostatistics? (n.d.). Retrieved September 4, 2025, from Csueastbay.edu website: https://www.csueastbay.edu/statistics/biostatistics-psm/biostatistics.html
- No title. (2017, September 10). Retrieved September 4, 2025, from BYJUS website: https://byjus.com/physics/hypothesis/
- Banerjee, A., Chitnis, U. B., Jadhav, S. L., Bhawalkar, J. S., & Chaudhury, S. (2009). Hypothesis testing, type I and type II errors. Industrial psychiatry journal, 18(2), 127–131. https://doi.org/10.4103/0972-6748.62274
- Ranganathan P. (2021). An Introduction to Statistics: Choosing the Correct Statistical Test. Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine, 25(Suppl 2), S184–S186. https://doi.org/10.5005/jp-journals-10071-23815
- Feng, C., Wang, H., Wang, B., Lu, X., Sun, H., & Tu, X. M. (2016). Relationships among three popular measures of differential risks: relative risk, risk difference, and odds ratio. Shanghai archives of psychiatry, 28(1), 56–60. https://doi.org/10.11919/j.issn.1002-0829.216031
- Correlation and regression. (2020, October 28). Retrieved September 4, 2025, from The BMJ | The BMJ: leading general medical journal. Research. Education. Comment website: https://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one/11-correlation-and-regression
- (2019, December 9). Multiplicity problem in clinical trials and some statistical approaches. Retrieved September 4, 2025, from Medium website: https://statswork.medium.com/multiplicity-problem-in-clinical-trials-and-some-statistical-approaches-b9a7fcc4245f
- Palesch Y. Y. (2014). Some common misperceptions about P values. Stroke, 45(12), e244–e246. https://doi.org/10.1161/STROKEAHA.114.006138