Clinical Trial Biostatistics

In clinical research, the importance of accurate data analysis cannot be overstated. Biostatistics – the application of statistical methods to biological and medical data – plays a crucial role in uncovering meaningful insights that drive evidence-based decision-making. By harnessing the power of data, biostatisticians can make informed conclusions and ultimately improve patient outcomes.

Biostatistics serves as a powerful tool for hypothesis testing, sample size determination and the evaluation of treatment efficacy in clinical research. By employing rigorous statistical methods, biostatisticians can minimize bias and maximize the validity of their findings, ensuring that their conclusions are both reliable and generalizable.

The function of biostatisticians in clinical trial management goes far beyond analyzing data at the end of a study. Instead, they should be involved from the start to maximize the chances for a new drug’s market approval. Through the use of statistical techniques, these researchers can identify patterns, trends and associations within the data to inform critical decisions throughout the trial.

Biostatisticians advise on study design, calculate the appropriate sample size, and validate that the enrolled patients are correctly randomized. They also help define endpoints, provide definitions for data analysis, analyze complex datasets, and create tables, listings and figures for the clinical study report.


Biostatisticians play a crucial role in the preparation of the clinical trial protocol, ensuring that a study is appropriately designed to address research questions and minimize bias. They determine the required sample size and sampling technique and develop strategies for randomization. They need to understand the scientific question underlying the study to give input to suitable parameters – such as enrolling the right number of patients – that can be measured with statistical relevance.

The clinical study protocol also defines the objectives and endpoints to be assessed during the clinical trial. By employing robust study designs, biostatisticians can enhance the validity and reliability of the findings, providing a solid foundation for subsequent data analysis.

The role of biostatistics in clinical research

In clinical research, data analysis serves as the backbone of evidence-based practice, driving the development of new treatments and interventions. Through careful examination of data, biostatisticians can detect outliers and uncover hidden patterns that may have significant implications for patient care and outcomes.

By applying statistical techniques and employing advanced analytical tools, these researchers can extract insights from vast amounts of data that can directly impact clinical trials in the following ways:

Study design development Biostatistics can be used to dictate the methods and direction of the research to be undertaken.
Consolidating evidence A large amount of data can be sorted efficiently using biostatistics tools.
Data analysis Not only does the data need to be organized, but it also needs to be analyzed to draw insights that could prove or disprove a particular medical hypothesis.
Interpretation With their field-specific knowledge, biostatisticians can assist in interpreting the clinical trial results.

Sample size determination and randomization

Sample size determination involves calculating the number of participants or observations needed for a study to achieve sufficient statistical power and precision in estimating outcomes. It is crucial to ensure that the sample size is appropriate to detect meaningful effects or differences, minimize the risk of type I (false-positive) and type II (false-negative) errors and provide reliable results. Various factors, such as the research question, study design, effect size, variability and statistical methods, influence the sample size calculation.

Randomization is a critical component of rigorous research design to minimize selection bias and increase the validity and generalizability of study findings. It involves the random assignment of participants or observations to different study groups or treatments.

Study design considerations

According to the U.S. National Institutes of Health, fundamental issues to consider in clinical trial design include:

  • Clearly defining the research question
  • Minimizing variation
  • Randomization and stratification, blinding, and use of placebos/shams if warranted
  • Selecting a control group
  • Selection of the target population and entry criteria
  • Selection of endpoints
  • Sample size
  • Planning for interim analyses (statistical evaluations of data collected during an ongoing study)

As defined by the Journal of the American Medical Association, randomized clinical trials evaluate medical interventions under ideal conditions among highly selected populations, while observational studies examine such effects in “real world” settings.

Today, clinical researchers may apply innovative design approaches, such as adaptive designs, which allow modifications to the trial’s statistical procedures and/or trial design after its initiation while maintaining data validity and integrity. Other design approaches include:

Basket design Tests a targeted therapy on the molecular profiles of a broad spectrum of diseases
Umbrella design Evaluates multiple targeted therapies in a single disease setting
Platform design Evaluates multiple interventions simultaneously against a common control group

Caption: Clinical trial designs; Credit: ResearchGate

Real-world data and biostatistics

Over the past decade, the number of health care datasets and novel data sources available to researchers has increased exponentially, from electronic medical records, insurance claims and registries to patient handheld monitoring devices. Biostatisticians have access to a wealth of real-world evidence (RWE) to investigate a product’s value, safety and effectiveness. 

One of the main challenges in incorporating real-world evidence is the lack of standardization in data collection and analysis. RWE is collected from various sources and in different formats, making it difficult to compare and analyze the data. Another challenge is the need for data privacy and security. Patient data must be protected to ensure patient confidentiality and comply with regulations. 

Despite these challenges, there are many opportunities for incorporating RWE into clinical trials. It can supply a more diverse patient population, as it includes patients who may have been overlooked in traditional clinical trials. This can lead to a better understanding of how a drug works in different patient populations. RWE can also offer insights into the long-term safety and effectiveness of a drug, which are not always possible in traditional clinical trials. 


One common biostatistical approach is descriptive analysis, which involves summarizing and presenting real-world data using measures such as means, proportions and rates. This provides a snapshot of the population under study and helps pinpoint patterns and trends. Another approach is comparative effectiveness research, which compares the outcomes of different treatment options in real-world settings. Multiple imputation techniques and sensitivity analyses are used to account for missing data, while propensity score weighting, instrumental variable analysis and adjustment methods address confounding biases.

Additionally, biostatisticians use regression analysis to model relationships between variables, allowing for the identification of factors influencing outcomes. Survival analysis is employed to assess time-to-event outcomes, such as disease progression or patient survival. These approaches help researchers understand the impact of interventions and patient characteristics on outcomes.

The role of biostatisticians in clinical trial biostatistics groups

Biostatisticians are integral members of clinical trial biostatistics groups, employing their knowledge and expertise to ensure the validity, reliability and ethical conduct of clinical trials. These highly trained professionals are responsible for designing the statistical aspects of the trial, analyzing and interpreting the collected data and providing overall statistical guidance.

A primary function of biostatisticians is to collaborate with other members of the clinical trial team to develop a statistical analysis plan. This plan outlines in detail how the collected data will be used, analyzed and displayed (e.g., in tables, listings, and figures) to answer the research questions posed in the trial.

During the trial, biostatisticians monitor the data collected for quality and integrity. They analyze the data using appropriate statistical techniques to evaluate the efficacy and safety of the tested interventions and interpret the results of the trial, helping to draw meaningful conclusions and communicate them effectively to the broader scientific community.

In addition to their technical expertise, biostatisticians provide input on the ethical and regulatory aspects of clinical trials. They ensure that the trial design and data collection procedures adhere to ethical guidelines and regulatory requirements, safeguarding the rights and well-being of study participants and supplying quality control throughout the entire process.

Biostatistics in public health research

Biostatisticians support public health studies by examining complex and large-scale health datasets to provide reliable, robust and balanced information for the assessment of the risk-benefit profile of new medicines or to inform public health policies.

One of the primary uses of biostatistics in public health research is in the design and analysis of epidemiological studies. As with smaller clinical trials, biostatisticians help determine the appropriate sample size, study design and statistical methods to ensure the validity and reliability of the study findings. In this case, the researchers analyze data collected from surveys, existing clinical trials or observational studies, using various statistical techniques such as regression analysis, survival analysis or Bayesian methods (a general statistical paradigm that answers research questions about unknown parameters using probability statements).

Biostatistics also contributes to a better understanding of the distribution and determinants of diseases within populations. Biostatisticians use statistical modeling to assist in quantifying the burden of diseases, estimating disease prevalence and incidence rates and investigating patterns of disease transmission.

In another example, biostatisticians can analyze environmental data, such as air quality measurements or water contamination levels, and assess their impact on public health. By employing advanced statistical techniques, such as spatial analysis or time-series analysis, researchers can identify associations and patterns between environmental exposures and health outcomes. Overall, the expertise of biostatisticians helps to confirm that public health research is based on sound statistical principles, leading to evidence-based decisions and interventions that can improve population health.


In clinical trials, biostatistics and data analysis help make decisions regarding the safety of the drug, population density and type of treatment needed. Biostatisticians’ contributions to study strategy, planning, design, methodology, data analysis, programming and interpretation are invaluable in advancing clinical research and improving patient outcomes. They provide expert analysis and strategies for protocols, endpoint capture and reporting. They support sample size calculations, statistical power and estimands (a systematic description of the treatment effect to be quantified). Biostatisticians also need to have regulatory knowledge that will provide support for regulatory interactions, avoid risks, help streamline the entire clinical research process and maximize the likelihood of success from the outset.

Through biostatistical techniques, researchers can identify patterns, trends and associations within the data, providing valuable insights that inform critical decisions towards improving clinical research study design and interpretation.

Bayesian analysis is a general statistical paradigm that answers research questions about unknown parameters using probability statements. It enables defensible and accurate quantification, as well as incorporation of prior evidence and utilities, to make statements about population parameters, future data and expected net benefits. Examples include:

  • Prediction: Forecasting, in event-driven trials, of time-to-event endpoint events such as mortality or progression-free survival, by accounting for uncertainties in statistical parameters as well as in future data. This aids in determining probabilities of success, predictive powers and pre-posterior chances of conclusive evidence.
  • Decision Analysis: End-of-Phase-II go/no-go decision making based on risk-adjusted net present value. Formal comparisons of candidate study designs (e.g., whether to add a futility analysis, optimal timing of interim analyses, or how to adjust a sample size) are given with respect to impacts on risk-adjusted net present values of investigational products.

Inferential statistics allow researchers to draw conclusions and make inferences about a population based on sample data. Techniques such as hypothesis testing, confidence intervals and analysis of variance are commonly employed to determine whether observed differences or associations are statistically significant. By applying inferential statistics, researchers can make informed decisions and generalize their findings to the broader population, ensuring the validity and applicability of their research.

Interim analysis specified in the protocol is performed for the data monitoring committee (DMC) and/or client monitoring of subject safety alone, or for monitoring both safety and efficacy. For safety monitoring, the DMC and/or the client may use statistical or non-statistical guidance for continuing or stopping the study. Efficacy monitoring in confirmatory studies (Phase III trials), on the other hand, is based on statistically derived stopping guidelines to protect the study’s type I error rate, power or both. Details of the stopping rules, either for futility, efficacy or sample size re-estimation, are provided in the study statistical analysis plan and may also be included in the DMC charter.

Robust biostatistics support

In the past five years, a team of nearly 900 dedicated biostatistics and programming staff from the PPD clinical research business of Thermo Fisher Scientific has supported nearly 1,200 studies spanning every phase of clinical trials. With the flexibility of a functional service partnership (FSP) model, we support biopharmaceutical, biotech and medical device organizations with comprehensive biostatistics services.

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