MAKING THE CONNECTION BETWEEN CENTRALIZED BIOMETRICS & ADAPTIVE TRIAL DESIGN

Flexible study designs are gaining popularity in clinical research as sponsors search for ways to

reduce study timelines and costs by making modifications along the way. Implementing Adaptive

Trial Design has been one flexible design that has proven successful for many clinical trials with

20-30% of trials now using this methodology. However the degree of success of this design

approach doesn’t just depend on the capabilities of your biostatistician. It can also depend on your

outsourcing model.

CROS NT looks at the connections between a centralized biometrics approach and adaptive trial design success.

In an adaptive approach, at each stage and in each phase of the drug or device development cycle,

the probability of success is quantified and the study team is full informed in order to evaluate risks

and benefits associated with each decision. This begins with the protocol where scenario planning

acts as a critical “stress test” key tool for demonstrating the value of adaptive designs.

Ways of maximizing efficiency through adaptive designs are:

– Early stopping (futility, early rejection)

– Change in treatment allocation ratios

– Alterations of hypothesis (non-inferiority vs. superiority)

– Use of different test statistics

– Sample size re-assessment

– Dropping/adding treatment arms

– Select special populations (inclusion/exclusion criteria)

– Combnation of trial phases (adaptive seamless design)

These are generally statistical concerns, therefore where does the centralized biometrics

outsourcing model fit in?

It is generally agreed that the best time to involve a biostatistician in a clinical trial is at the very

begninning, This allows the biostatistician to understand the study design and make suggestions on

hypothesis testing and analysis and also ensures continuity throughout the study team. If one study

team – including the biostatistician – is assigned to trial design, data analysis and medical

communications from the start, common data standards can be applied throughout the drug

development process. This is a centralized approach to data collection and analysis.

As planning is an important step in preparing and implementing an adaptive design approach, there

should be a good communication plan defined between the CRO and sponsor. Agreeing on a

statistical analysis plan, methodologies and programming formats can reduce the number of

reviews, therefore saving time and costs.

In order for statisticians to make “go/no-go” decisions, they need access to real time data as soon as

it is collected. This can be achieved through a centralized approach when all data are stored in a

central warehouse and/or archive which avoids having to keep track fo multiple repositories.

The biostatistician really does collaborate with the rest of the study team, includnig Data Managers,

Statistical Programmers and Medical Writers. Regarding Data Management, the biostatistician can

assist with CRF development and dataset specifications. Working with statistical programmers,

methodological statisticians ensure data formatting is correct and select data to be pooled. These

interactions all contribute to the biostatistician’s statistical analysis that lead to adaptations in a trial

such as the decision to recalculate the sample size, alter study endpoints or even terminate a study.

When the study data is dispersed among various sources, it becomes more time consuming for the

biostatistician to gather the data needed and calls in question the quality of the data being pooled.

Some studies have already suggested that the implications of adaptive designs could save Sponsors

between $100-200 million USD per year. Those savings, coupled with the considerable 30-40%

cost savings of a centralized biometrics approach, due mostly to global libraries and achieved

efficiencies through one project team, can result in significant cost savings for trial Sponsors.