Biostatistical Consulting Services

Due to our biostatistical heritage, Quanticate has been an industry leader in biostatistical consulting for over 25 years. Having such a breadth of experience among our staff allows us to provide our clients the needed expertise for their projects.  From study start up through to the final analysis, from Phase I to submission, our statistical consultants are among the leaders in their respective areas enabling our clients to have the ability to choose expertise from a range of consultants to match their needs and reach their IND submission, publication or research goals.

We offer biostatistical consultancy in areas such as:

Clinical Study Design

An optimally designed trial can reduce costs and save time, either by requiring a smaller sample size or by stopping a trial earlier in time without continued wasted efforts. Our statistical consultancy team have been involved with a broad spectrum of clinical study designs across a wide range of therapeutic areas and can draw upon their experiences to support your clinical development programme. We can support with:

• Writing Statistical Sections of Complex Protocols including Master Protocols
• Conducting Simulations to Estimate and Optimise Study Sample Size
• Exploring use of Novel Designs across Drug Development Program
• Support in planning of Adaptive trial designs and Group Sequential Trials and related stopping rules for interim analyses
• Expert Statistical Support for Data Safety Monitoring Boards (DSMBs)
• Maximizing Study Efficiency through Best Statistical Practice including Bayesian Methods
• Efficient support for Biosimilars
• Specialist Expertise in Pharmacokinetic Analysis and Reporting
• Exploiting data sources from latest technologies such as mhealth apps, wearables and ePRO solutions

Alternative Trial Types

Our Statistical consultants are experienced in handling data and statistical methodologies that lie beyond the standard randomized controlled clinical trial (RCT), our consultants are highly skilled in appropriate use of other data sources including:

• Registry Studies
• Real World Evidence/Data
• Health Economics and Outcomes Research

Due to their observational nature, analysing these data requires specific methods and an understanding of Observational Studies to ensure the correct conclusions are drawn. Our consultant will be able to plan the correct analytical methods to deal with these data sources and ensure all potential source of bias are identified and properly managed.

Estimands

Fundamentally linked with trial design, the definition of a study estimand is a crucial part of study planning since it specifies the scientific question of interest. Our team can provide support in the definition of the correct study estimand by liaising with the clinical team and specify all estimands component consistently with the most recent Regulatory framework.

Handling and Prevention of Missing Data

Whether it is missing data in clinical trials due to poor patient engagement or from a lack of data in Observational studies, our biostatistical consulting offers a range of statistical methodologies to handle missing data including:

• Specification of Multiple Imputation Models (coupled with tipping-point analysis)
• Simulation studies to assess sensitivity (Sensitivity Analyses) of the main results to alternative scenarios (e.g. to assess impact of differential drop-out rates)
• Evaluation of impact of a given methodology in an equivalence/non-inferiority context

Risk Based Approaches & Regulatory Submissions

With constantly evolving guidance and various opportunities for improved clinical trial efficiency in the recent ICH E6 R2 Addendum, our statistical consultants understand the demand and practicality of embracing new technologies and methods and offer the following:

• Regional based regulatory support including an independent review of submission dossiers
• Data Quality Oversight through the creation of Statistical Monitoring Reports
• Development of protocols and SAPs for a risk based approach to monitoring

Support in the creation of a Risk Assessment Categorization Tool (RACT) to enable Centralized Statistical Monitoring