Blog | Contact

Biostatistics plays a pivotal role in contemporary clinical research. It provides the quantitative tools and statistical methods to collect, analyze and interpret data from clinical trials and health studies. Without biostatistics, it would be impossible to draw reliable conclusions from clinical data.

biostatistics in clinical trials, clinical study statistics, biostatistics and clinical trials

Why Biostatistics is Crucial for Evidence-Based Healthcare

In recent years, evidence-based medicine has become the gold standard for optimal healthcare. The goal of evidence-based medicine is to apply only healthcare practices and solutions that are supported by well-designed medical research and statistical evidence. This ensures effective and consistent patient care guided by facts, rather than opinions or conventions.

Biostatistics clinical trials make evidence-based medicine possible by enabling practitioners to quantify and analyze clinical data. Statistical analysis helps establish causality, measure the effectiveness of treatments, model disease progression, and much more. Without biostatistics, the evidence in evidence-based medicine simply wouldn’t exist.

Purpose of This Guide

This guide aims to provide a high-level overview of biostatistics and its role in clinical research. It explains key concepts and introduces common statistical methods used to collect, summarize, analyze and extrapolate clinical data. The goal is to equip healthcare professionals without a statistical background with core biostatistical knowledge to better understand clinical study designs and results.

The Basics of Biostatistics in Clinical Research 

Biostatistics for clinical trials is the application of statistical techniques to clinical research data. It includes the design of studies, quantification and analysis of data, and statistical inference to derive conclusions. Professionals who work in the field of biostatistics are known as biostatisticians.

Key Concepts and Terminology

Understanding biostatistics requires grasping a few key terms:

The Importance of Data Collection and Quality Control

High quality data is crucial for sound biostatistical analysis. Great care must be taken in designing data collection protocols, safeguarding against missing or inaccurate data, and ‘cleaning’ data prior to analysis.

Study Design and Sampling

Experimental vs Observational Studies

Biostatisticians help design rigorous clinical studies to minimize bias:

Randomization and Blinding

Biostatisticians may use techniques like randomization and blinding to reduce bias in clinical studies:

Sample Size Determination

Biostatisticians help determine optimal sample sizes to generate statistically significant results and accurate inferences about the population. Larger sample sizes produce more reliable results but are costlier to obtain.

Ethical Considerations

Biostatisticians must ensure clinical studies adhere to ethical guidelines for informed consent, patient privacy, risk minimization and other protections mandated by review boards.

Data Presentation and Clinical Study Statistics

Graphical Representation of Data

Biostatisticians may use visual graphs to easily comprehend clinical data:

Measures of Central Tendency

These Statistics In Clinical Trials describe the central position of a dataset’s distribution. Examples include:

Measures of Variability

These statistics describe the dispersion of a dataset. Examples include:

Probability and Probability Distributions

Understanding Probability

Probability measures the likelihood of an event. It ranges from 0 (impossible) to 1 (certain). Basic axioms govern mathematical probability.

Common Probability Distributions

Continuous variables commonly follow these distributions:

Hypothesis Testing

Formulating Research Hypotheses

Hypotheses make specifiable predictions that can be tested statistically, e.g.:

Null and Alternative Hypotheses

The null hypothesis assumes no effect or difference. The alternative hypothesis is the desired outcome that research evidence can support or reject.

Significance Level (alpha)

The significance level is the threshold p-value for rejecting the null hypothesis. Typical levels are 0.05, 0.01 or 0.001.

p-values and Statistical Significance

The p-value represents the probability of obtaining results as or more extreme than observed if the null hypothesis is true. If p < alpha, results are deemed statistically significant.

Common Statistical Tests

Different tests determine statistical significance:

Interpreting Test Results

Biostatisticians determine which statistical test to use, carry it out correctly, and interpret the results in context of the clinical study.

Confidence Intervals

Confidence intervals provide a range of plausible values for an unknown population parameter based on sample statistics. Wider intervals indicate less precision.

Calculating Confidence Intervals

Confidence intervals can be calculated using sample statistics, standard error, the desired confidence level and the critical value from a probability distribution.

Interpreting Confidence Intervals

95% confidence intervals are commonly reported. There is a 95% chance that the true population parameter lies within the calculated range.

Regression Analysis

Regression estimates statistical relationships between variables to predict outcomes. It models how changes in independent variables impact a dependent variable.

Linear Regression

Linear regression predicts continuous outcomes based on the linear influence of predictor variables. It assumes a straight-line relationship.

Logistic Regression

Logistic regression predicts binary categorical outcomes, like disease/no disease, based on one or more predictors. It assumes a sigmoidal relationship.

Multiple Regression

Multiple regression incorporates multiple predictor variables. It can reveal how those variables independently or jointly impact the outcome variable.

Interpreting Regression Results

Key regression outputs include coefficient estimates, R-squared values, p-values for variables, confidence intervals, and predictions.

Survival Analysis

The Basics of Survival Analysis

Survival analysis examines and models the time it takes for an event of interest to occur, like death or disease recurrence. Censored observations are those for which the event was not observed.

Kaplan-Meier Survival Curves

These plots estimate survival over time from observed data. They account for censored observations. The curves descend as cumulative survival worsens over time.

Hazard Ratios

Hazard ratios compare risk between groups. A HR of 2 means the first group has double the instantaneous risk of the outcome compared to the second group.


A meta-analysis statistically combines data from multiple independent studies investigating the same clinical question. This increases statistical power and improves estimates.

The Steps Involved

Key steps include:

The Advantages and Limitations of Meta-Analysis

Meta-analysis yields more robust statistics by combining data from multiple smaller studies. However, it is only as good as the quality of the original studies. Garbage in, garbage out.

Data Management and Software

Meticulous data preparation and management ensures data quality for sound biostatistical analysis. Steps include data cleaning, validation, merging datasets, transforming variables, etc.

Common Statistical Software Packages

Reporting and Publishing Results

Ethical Considerations

Researchers must adhere to ethical obligations when reporting findings, including disclosing conflicts, detailing limitations, and avoiding misrepresentation.

The Structure of a Research Paper

Papers present key statistical results aligned to the study aims. Sections include Introduction, Methods, Results, and Discussion. Tables, figures and statistics support findings.

The Peer-Review Process

Submitted manuscripts are rigorously critiqued by experts before publication. Biostatistical methods and interpretation of results are checked for soundness.

Practical Applications of Biostatistics

Case Studies and Examples

Real-world examples bring biostatistical concepts to life. For instance, seeing Kaplan-Meier curves depicting patient survival or reviewing the statistical methods in a landmark clinical trial paper.

Real-World Implications in Clinical Research

Practical biostatistical applications include:

Ongoing Developments in Biostatistics

Biostatistics continues to evolve with advances like personalized medicine, big data analytics, predictive modeling, and data visualization. Training in emerging techniques ensures biostatisticians stay relevant.


Biostatistics provides the essential data analytic tools to generate medical evidence and guide clinical practice. It continues to expand in scope and sophistication. New biostatistical techniques propel clinical research forward in the era of big data and precision medicine. Biostatisticians must stay abreast of the latest developments. This guide only scratches the surface of biostatistics. To apply biostatistics in real-world research or practice, comprehensive training and hands-on experience are necessary. However, the foundation established here provides a springboard to launch into deeper biostatistical learning for clinical applications.

Why ClinVigilant biostatistical services for your clinical trial?

ClinVigilant’s Biostatistics Consulting services provide critical support for the design, execution, analysis, and reporting of clinical trials. Their experienced biostatisticians are involved throughout the entire clinical trial process, from initial protocol development to final statistical analysis and reporting.

During the protocol development stage, ClinVigilant biostatisticians provide input on study design, sample size calculations, randomization methods, and statistical analysis plans. This helps ensure the trial is properly powered to detect meaningful treatment differences and uses appropriate statistical methods. Once a trial is underway, ClinVigilant biostatisticians monitor patient enrollment, data quality, and interim analyses. 

They identify potential issues early so any necessary adjustments can be made. At the conclusion of a trial, ClinVigilant biostatisticians conduct comprehensive statistical analyses in accordance with the pre-specified plan. They generate tables, listings, figures, and other outputs to summarize key efficacy and safety data. Their expertise in regulatory statistical analysis and reporting ensures trial results are presented accurately and effectively to support regulatory submissions.

Provide brief info about what kind of support you are looking for
Provide your work email