Clinical Biometrics Expertise Delivers Results
Clinical Biometrics encompasses the use of biostatistics, statistical programming, data management, and data standards. The timely and proficient implementation of biometric tools empowers the generation of an efficient study design, data collection and management, and outcome analysis.
The process of clinical drug development is complex and time-consuming. A recent analysis of clinical trials conducted between 2011 and 2020 demonstrated that the overall likelihood of approval of Phase 1 drug development candidates was 7.9%. Moreover, the mean duration until regulatory approval for Phase 1 drug candidates was 10.5 years.
There are many possible reasons for the failure of drug development programs. Some of these reasons may be related to biostatistical aspects, such as a flawed study design, an underpowered clinical study, an inappropriately selected statistical endpoint, or failure to choose participants representative of the targeted population.
During clinical drug development, a vast amount of clinical data is collected, necessitating advanced methods for clinical trial data management and statistical analysis. Biometrics is a science that uses statistical methods to collect and analyze biological or health-related data.
The Role of Biometrics in Clinical Trials
Clinical biostatistics plays a crucial role in the early stages of clinical trial design and study protocol development. First, strategies, such as randomization and masking, should be developed to control the numerous sources of variance and reduce potential bias.
A power analysis and sample size calculations are also conducted during these early stages to ensure that the clinical research study has a sufficient sample size to detect existing differences. Finally, a thorough statistical analysis plan should be developed, predetermining the statistical analysis of primary and secondary variables and other data, including safety and efficacy analyses and interim analyses.
Refining the Role of Biometrics
Clinical Data Management
A key component and encompasses collecting, cleaning, and managing clinical trial data. Its goal is to collect and process complete and reliable, high-quality data and to minimize the number of errors and missing data. In addition, clinical data management should comply with the relevant regulatory standards. The process starts with reviewing the developed study documents and identifying strategies for the manner and frequency of data collection. A Data Management Plan (DMP) is generated that serves as a roadmap for handling the collected data and delineates the planned activities.
A Case Report Form (CRF) is designed, tracked, and annotated as an initial step in translating protocol-specific activities into generated data. Using the CRF, the collected data are entered, medically coded, validated, and managed for discrepancies.
Finally, after the process is completed, the database is locked. The development of software applications is also important for data management because it helps to identify and resolve data discrepancies and facilitates the maintenance of all necessary documentation for an audit.
Setting Unified Standards for Data Collection and Management
Complying with unified standards for the collection and management of data facilitates the transfer and integration of data within an organization, between different organizations, and with regulatory agencies. The Clinical Data Interchange Standards Consortium (CDISC) is a multidisciplinary non-profit organization that has established data standards for the acquisition, analysis, exchange, submission, and archival of clinical research data and metadata.
Thus, it optimizes study planning and helps enhance the quality and interoperability of drug development research. Moreover, the CDISC standards have been routinely implemented in submissions to the United States Food and Drug Administration (FDA), Japanese Pharmaceuticals and Medical Devices Agency (PMDA), and other regulatory agencies.
In-depth Statistical Analysis
The applied methods for statistical analysis depend on the type of the selected statistical endpoints, including continuous, binary, and time-to-event outcomes. They also take into consideration missing data, patient noncompliance, and loss of follow-up. Descriptive statistical methods (accompanied by confidence intervals where applicable) are often used to delineate safety and efficacy aspects.
In addition, p-values may be utilized to examine specific differences of interest. Statistical modeling approaches may also be employed to integrate information regarding pharmacological parameters, the extent of protocol compliance, and other biologically based data to gain insight into the efficacy and safety of drug candidates. All underlying assumptions and limitations of such statistical models should be clearly defined.4
Implementation of Clinical Biometric Monitoring Devices
Clinical biometric monitoring devices serve as biosensors that gather objective biological data and utilize algorithms to transform these data into defined measures or functional outcomes. Their use in clinical studies is expanding, as they permit the collection of real-time data with high frequency. As a result, a high quantity of biometric data is collected, and the assessment times and sample sizes required for clinical trials may be reduced.
Integration of Scientific Advances with Clinical Biometrics
Advances in different fields of medical research (such as pharmacometrics, high-throughput analyses, and machine learning and artificial intelligence) have expanded the amount and type of data that may be acquired in clinical trials. Their integration with biometrics further empowers the generation of and insight into clinical trial data. The synergistic use of pharmacometric physiology-based drug and disease models and clinical biometrics promotes drug research and development.
In addition, the collection of a vast amount of -omics data and machine learning approaches may be used to stratify study populations and gain insight into subgroups of study participants based on their individual characteristics. However, there are still unclarities regarding the selection of optimal molecular profiling methods, standardized biological thresholds, and appropriate phase 1 clinical trial designs to stratify patient or volunteer populations.
Clinical Biometrics Expertise and Services Critical for Success
BioPharma Services, Inc. has expertise in all areas of clinical biometrics, including biostatistics and clinical trial data management. In addition to full-service biometric support for our clients’ clinical trials, we also provide stand-alone statistical analysis, programming, and data management for studies conducted at other organizations.
Full Compliance With All CDISC Data Formats
BioPharma Services complies fully with CDISC standards throughout all clinical trial stages. Our team designs the CRF in accordance with the CDASH standards, enabling easy dataflow into the Study Data Tabulation Model (SDTM) for data tabulation and CDISC Analysis Data Model (ADaM) for statistical analysis of the data collected during the trial.
A Proficient Team of Statisticians and Programmers
BioPharma Services has recruited and established a talented team of biostatisticians and programmers with expertise in statistics, mathematics, and epidemiology. Our biometric team includes three Ph.D. graduates, whereas all other team members have master’s degrees. Our biostatisticians and programmers’ diverse areas of expertise ensure that we can competently manage all biometric aspects of our clients’ clinical trial programs.
Full Suite of Clinical Biometric Services
We offer comprehensive clinical biometric solutions for client clinical trial programs. Our experienced in-house biostatisticians design robust clinical trials with minimized potential for bias and prepare sample size calculations and randomization schemes. They also generate detailed statistical analysis plans.
The clinical trial data management team captures, cleans, and verifies the data. SAS programming and validation are performed. Subsequently, data sets, tables, listings, and figures are generated, and in-depth statistical analyses are performed. Analyzed data is included in the clinical study reports and, if needed, in submissions to regulatory agencies.
Integration of Biometric and Regulatory Expertise
BioPharma’s clinical biometric experts work closely with our regulatory scientists to ensure that all aspects of statistical analysis and data management are conducted in compliance with the requirements of the relevant regulatory agencies and that the output reports are presented in compatible formats.
Niche Expertise in Phase 1 Clinical Trials and Human Abuse Potential (HAP) Studies
In addition to being a full-service CRO, BioPharma Services has niche expertise in Phase 1 clinical trials and human abuse potential (HAP) studies, including all clinical biometric aspects of these study types. Thus, our team has completed over 2,000 early-phase clinical trials, including their full biometric support. Our HAP specialists are well-recognized experts in the field whose experience dates back to the first HAP studies conducted according to the principles of GCP in the early 2000s.
Focusing on Each Client’s Drug Development Program Needs
Our biometric team works closely with our clients to offer personalized solutions best suited to their drug development programs. In addition, we perform all steps of the statistical analysis planning and performance and data management within a strict timeline and with the highest possible quality.
Why Choose BioPharma Services for your Clinical Biometrics
BioPharma Services is a full-service CRO that offers comprehensive clinical biometric solutions, both as backend statistical support for clinical trials performed at our organization and as stand-alone biometric services for externally conducted studies. Complete the form below to schedule a discovery call with a member of our team to learn how BioPharma Services Inc. can be your trusted clinical trial partner.
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