Service List
You Still Have a Question Contact Now !
Get In Touch
Biostatistics in Clinical Research
Sound Statistics. Scientifically Valid Conclusions.
Biostatistics is the application of statistical methods to the design, analysis, interpretation, and reporting of clinical research data. It helps researchers determine whether observed results are meaningful, quantify uncertainty, and draw scientifically valid conclusions.
Biostatistics runs through the whole life of a clinical trial — from selecting the study design and calculating the sample size, to planning and running the analysis, and finally interpreting the findings for the Clinical Study Report.
Our approach to biostatistics emphasizes scientific rigor, pre-specified analysis methods, and results that can be translated into clinically meaningful conclusions.
Our Biostatistics Services
From Study Design to Clinical Study Report.
We work closely with sponsors, investigators, and research teams across the major areas of biostatistics — helping ensure that every study is designed to answer its research question and that its data is analyzed and reported with care.
Study Design
Selecting appropriate designs such as randomized controlled trials, cohort studies, case-control studies, and cross-sectional studies.
Sample Size Calculation
Determining how many participants are needed to detect a clinically meaningful treatment effect.
Randomization
Developing appropriate methods for allocating participants to treatment groups.
Statistical Analysis Plan (SAP)
Defining statistical methods and analyses before examining the study results.
Descriptive Statistics
Summarizing data using means, medians, standard deviations, proportions, and other measures.
Inferential Statistics
Using statistical tests and confidence intervals to make conclusions about a population from study data.
Hypothesis Testing
Assessing whether observed differences or associations are likely to have occurred by chance.
Efficacy Analysis
Evaluating whether an investigational treatment achieves its intended clinical outcomes.
Safety Analysis
Assessing adverse events, laboratory abnormalities, vital signs, and other safety outcomes.
Survival Analysis
Analyzing time-to-event outcomes such as overall survival or progression-free survival.
Missing-Data Analysis
Assessing and appropriately handling missing observations.
Interim Analysis
Evaluating accumulating trial data before the planned end of a study, when appropriate.
Statistical Programming
Creating analysis datasets, tables, listings, and figures using tools such as SAS, R, or Python.
Results Interpretation
Translating statistical findings into clinically meaningful conclusions.
Biostatistics Workflow in a Clinical Trial
From Protocol to Clinical Study Report.
Every trial follows the same path, with each step building on the one before it.
-
1
Protocol
The protocol sets the objectives, endpoints, and population that shape every statistical decision.
-
2
Study Design
Choosing the design that answers the research question with scientific validity.
-
3
Sample Size
Calculating how many participants are needed to detect a clinically meaningful effect.
-
4
Randomization
Defining how participants are allocated to treatment groups.
-
5
Data Collection
Trial data is gathered in line with the protocol.
-
6
Data Cleaning
Data is reviewed and discrepancies resolved so the dataset is reliable for analysis.
-
7
Statistical Analysis Plan (SAP)
Statistical methods and analyses are defined before the study results are examined.
-
8
Statistical Analysis
Efficacy, safety, and other planned analyses are run as specified in the SAP.
-
9
Tables, Listings & Figures
Analysis datasets are turned into the outputs used for reporting.
-
10
Interpretation
Statistical findings are translated into clinically meaningful conclusions.
-
11
Clinical Study Report
Results and conclusions are documented in the final report of the study.
Statistical programming creates the analysis datasets, tables, listings, and figures that turn collected trial data into reportable results, using tools such as SAS, R, or Python.
Results interpretation translates statistical findings into clinically meaningful conclusions, so that trial outcomes can support informed decisions in drug development.
Frequently Asked Questions
Biostatistics is the application of statistical methods to the design, analysis, interpretation, and reporting of clinical research data, helping researchers quantify uncertainty and draw scientifically valid conclusions.
The SAP defines the statistical methods and analyses before the study results are examined, so the analysis is planned in advance rather than shaped by the results.
It determines how many participants are needed to detect a clinically meaningful treatment effect.
Survival analysis examines time-to-event outcomes, such as overall survival or progression-free survival.
An interim analysis evaluates accumulating trial data before the planned end of a study, when appropriate.
Analysis datasets, tables, listings, and figures are created using tools such as SAS, R, or Python.