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Clinical Research

Biometry: Clinical Data Management & Statistics

Harmonize your data to support your clinical endpoints​

As technology advances, the challenges surrounding biometric data are becoming increasingly complex, such as data security and privacy, data integration and standardization, and maintaining the utmost quality expectations from all stakeholders.  ​

At VCLS, we are dedicated to overcoming these challenges and providing innovative solutions specifically designed for each unique client. Whether it is data integration, analytics, security, or data validation, our expertise in biometry empowers us to deliver client-oriented solutions and meaningful outputs. ​​

What we offer

  • ​Database Design & Maintenance: We design and maintain secure and compliant databases to store all clinical trial data.​​
  • Data Standardization: We help standardize data formats and terminologies to ensure consistency across trials, making it easier to compare results.
  • Data Collection and Entry: We assist in designing data collection forms, ensuring they meet regulatory standard and manage the process of entering data into an electronic database.​
  • ​Data Validation & Quality Control: We employ rigorous quality control processes to ensure the accuracy and integrity of the data collected during clinical trials. ​
  • Data Cleaning: We perform data cleaning activities, identifying and resolving inconsistencies or errors in the data, which is crucial for accurate statistical analysis.
  • Randomization and Blinding: We assist in the randomization process and ensure proper blinding techniques are in place to reduce bias in the study.
  • Statistical Programming: We analyze and visualize data, helping in the interpretation of results.
  • Statistical Analysis: Our in-house biostatisticians conduct sophisticated statistical analyses of the clinical trial data. This includes descriptive statistics, inferential statistics, and modeling to draw meaningful conclusions.
  • Clinical Study Reports: We contribute to the creation of clinical study reports, summarizing the trial results in a format suitable for regulatory submissions.
  • Regulatory Compliance: We ensure that all data management and statistical activities adhere to regulatory guidelines. ​

Why work with us?

Expertise and Experience: 

We have a team of experienced professionals, including biostatisticians and data managers, who specialize in clinical research. Their expertise ensures high-quality data management and statistical analysis​

Data Quality Assurance: 

We apply rigorous data quality control and cleaning processes, which lead to more reliable and accurate results, reducing the risk of costly delays or regulatory issues.​

Confidentiality and Data Security: 

We prioritize the security and confidentiality of clinical trial data, implementing robust data protection measures.                                                                                                                                                                     

​Comprehensive Service Offerings: 

We offer end-to-end solutions, including data management, statistical analysis, and regulatory support, simplifying the clinical trial process for sponsors.​

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F.A.Q.

What information do I need to calculate the sample size for my clinical study?

To calculate sample size, several parameters must be considered. The most important are the nature of the primary endpoint, expected difference between 2 treatment arms and chosen test (superiority, equivalence or non-inferiority).

If the primary endpoint of the study is a quantitative variable (e.g. quality of life score), you need to know the expected variance of this variable, as well as the expected difference in efficacy between the two study groups (test and control groups). If the study’s primary endpoint is a binary variable (e.g. survival), you need to know the expected frequency of occurrence of this event in both groups. Based on this information, the study statistician can calculate the sample size.

The desired power for the study (probability that the study will be conclusive) and the alpha risk (risk of wrongly concluding the treatment is effective) influence the sample size as well and are usually assumed at least 80% for power and 5% for alpha risk. Choice to perform an intermediate analysis may induce an alpha risk inflation allocated to the final analysis, consequently leading to increase in sample size. On the other hand, a superiority design will increase the sample size compared to non-inferiority or equivalence design.

It is recommended to involve a statistician as early as the synopsis stage. Statistical input is crucial to design a clinical study that will generate robust evidence of efficacy and safety.

At this stage, the statistician calculates the sample size and advise on study design (randomization, stratification, statistical analysis planning). When writing the clinical protocol and especially statistical analysis plan (SAP), the statistician indicates the analyses to be carried out and corresponding methods.

Prior to the inclusion period, the statistician draws up the randomization list to be imported into the eCRF. During the inclusion period, the statistician is responsible for statistical programming, so that results can be released as soon as possible after the last patient visit.

Finally, once data is collected the statistician is responsible for the analysis and preparation of the statistical report.