Understanding Biometrics in Clinical Trials: The Ultimate Guide (2024)
Clinical trials are essential for bringing new drugs and medical devices to market but are also complex and expensive. Biometrics, the application of statistics and mathematics to biological and medical data, can play a vital role in maximizing clinical trial success.
According to the National Institutes of Health (NIH), even though numerous effective strategies are used, a staggering 90% of clinical drug development efforts fail.
In this article, we discuss the key ways biometrics can contribute to the success of clinical trials in terms of design, conduct, patient safety, and analysis.
What is biometrics for clinical trials?
Biometrics in the context of clinical trials refers to the application of statistical and data management techniques to ensure the integrity, accuracy, and regulatory compliance of clinical trial data. It encompasses three key components:
Biostatistics
Biostatistics in clinical trials is the application of statistics and mathematics to biological and medical data to improve the design, conduct, and analysis of clinical trials. Biostatistics ensures that clinical trials of different phases are scientifically proper, efficient, and ethical.
Biostatisticians play a significant role in all aspects of clinical trials, from designing the study to analyzing the data. They work with clinical researchers to develop a study protocol that outlines the study design, methods, and statistical analysis plan (SAP). Biostatisticians also help monitor the study’s progress and make adjustments as needed. Here is a part of their functionality:
Sample Size Calculation and Justification:
Biostatisticians determine the ideal number of participants in a trial to achieve statistical significance, ensuring the results are reliable and meaningful.
Randomization Schedule Development:
Biostatisticians create randomized schedules, a pivotal aspect of clinical trial design, to provide unbiased subject assignment, thereby enhancing trial validity.
eCRF/CRF Development and Review:
Biostats develop and review case report forms (CRFs), which capture patient data during the trial, ensuring data collection aligns with statistical analysis needs.
Statistical Analysis Plan (SAP) Preparation:
They prepare the SAP, a comprehensive document outlining statistical methods, endpoints, data handling, and reporting to ensure standardized and robust data analysis.
Reviewers Guidelines (SDRG and ADRG) Development:
They create the guidelines regulators use to review trial data, helping ensure data quality and consistency.
Result Interpretation and Assistance in the Clinical Study Report Development:
Biostatisticians contribute to result interpretation and collaborate on the Clinical Study Report (CSR), providing a statistical perspective to support the trial’s findings.
Data Management
Data management in clinical trials is a crucial function that involves the systematic collection, validation, and organization of patient data to ensure its accuracy, completeness, and regulatory compliance. Data managers create the foundation upon which reliable clinical research is built.
Data managers are responsible for meticulously managing data from its initial collection to its final presentation, generating meaningful insights and evidence-based healthcare advancements. Data management in clinical trials consists of 3 core phases:
Study Startup:
This stage involves protocol review to align data requirements with study objectives, followed by the meticulous creation of CRFs for accurate patient data collection. A comprehensive data management plan ensures data validity and reliability throughout the study. Simultaneously, databases are designed, complete with eCRF specifications and programming. Data validation plans, edit/SAS check programming, dataset extraction, and eCRF completion guidelines are developed.
Conduct Phase:
The conduct phase involves data entry and validation, ensuring the accuracy and integrity of collected information through both single and double data entry methods. Data cleaning identifies and rectifies discrepancies, while query management maintains data consistency. Serious adverse event (SAE) reconciliation and external data integration are managed to verify and reconcile data from various sources. Data Transfer Agreements (DTAs) are established for secure data sharing, while medical coding is performed using WHODrug and MedDRA dictionaries for standardized terminology.
Database Lock:
Database finalization encompasses query resolution, the retrieval of eCRF eSignatures, and verification of serious adverse event (SAE) reconciliation. Approval of medical coding ensures standardized terminology. Comprehensive documentation is finalized for regulatory compliance. Data export yields CDASH-compliant deliverables, and finally, the database is securely locked and archived, preserving data for future reference and regulatory purposes.
SAS Programming
SAS programming in clinical trials involves using SAS (Statistical Analysis System) software to manage, analyze, and report on data collected during clinical research studies. SAS is a powerful and versatile software suite widely employed in the field of clinical research due to its ability to handle large volumes of data, perform complex statistical analyses, and generate detailed reports. Critical aspects of SAS programming in clinical trials include:
Sample Size Estimation SAS Codes Development:
This involves writing SAS programs to calculate the sample size needed for a clinical trial. It ensures that the study will have sufficient statistical power to detect meaningful effects or differences between groups while minimizing the risk of errors.
CRF Annotation:
CRF annotation involves adding metadata and variable labels to the CRFs, making it easier to understand and analyze the data. This ensures that data collected on CRFs is adequately documented and facilitates later data analysis.
Preparation and Finalization of SAS Programs for SDTM/ADaM Datasets and TFLs Dry-Run Stage and Production Stage:
SAS programmers are responsible for writing and testing SAS programs to transform raw clinical trial data into standard formats like SDTM (Study Data Tabulation Model) and ADaM (Analysis Data Model). They also develop programs for generating tables, listings, and figures (TFLs) at both the dry-run and production stages. TFLs provide visual representations of the study results.
Datasets Validation According to CDISC:
Validation involves checking datasets to ensure they conform to CDISC (Clinical Data Interchange Standards Consortium) standards, which are widely accepted in clinical research for data consistency and compliance with regulatory requirements. This step aims to identify and correct discrepancies and errors in the data.
Define.xml Development:
Define.xml is an XML file that provides metadata and data definitions for regulatory submission. SAS programmers contribute to its development, describing the structure and attributes of datasets used in a clinical trial. Define.xml enhances data transparency and regulatory acceptance.