Evaluating Surrogate Endpoints

Defining more sensitive endpoint measures to reduce study time and expense, and enhance outcomes.

Improving Endpoint Evaluation

There is increasing interest in the pharmaceutical industry to find other more sensitive endpoints than those accepted currently by regulators. We have helped to design and analyse studies into imaging modalities to compare new endpoints with traditional measures. Some of the endpoints that are accepted by regulators are insensitive, in that they show little change in response to treatment or they take a long time to change. As a result, studies performed using these endpoints can be prolonged, large and expensive.

 

Data derived from a range of imaging modalities have been evaluated as potential measures of response. However, in addition to showing early changes in response to treatment these also need to show some correlation with the accepted endpoints. We have performed numerous studies to help the evaluation of these novel endpoints.

Our Approaches

Collaboration

We work closely with our clients to obtain an agreement on what they want to achieve and to understand the characteristics of the novel imaging data. We create a statistical analysis plan with mock tables and figures to ensure that we deliver analyses that meet the needs of the client. Throughout the process, we are in constant contact with the client.

Data standards

Due to the novelty and complexity of the data, we need to make sure that we received data necessary to perform the analysis, including contextual information and meta-data. We design dataset standards with the help of the client to enable seemless transfer of the data when it was available.

Performing the analysis as per the SAP

The analysis plan describes numerous approaches to characterise the new endpoints including association between new and traditional measures, estimations of treatment effects in treatment groups, assessment of potential lags between the imaging data and traditional measures over time. We also perform further exploratory analysis on the potential use of imaging data as tool for selecting patients most likely to respond to treatment.

Reporting and visualisation

Rather than produce large numbers of tables, we utilise visualisation tools and graphics to enable the interpretation of large amounts of information. This includes the generation of multi-panel graphs, clustering approaches as well as time profile plots of individual patient data. In one example, our approach resulted in the identification of a new endpoint that showed significant treatment effect over 12 weeks compared to 52 weeks for the traditional endpoint.

Our Experience

Collaboration

We work hard to understand the needs of customers so that we can efficiently deliver the best results. We have set up document templates that allow us to capture the scope of work, to ensure that we are aligned with the customer and to confirm that we are agreed on the project requirements. We also work across multiple functions including statistics, clinical, bioinformatics, computational biology, genetics. With all interactions, we adopt a collaborative approach whereby we know that the best solutions are those that have been developed together.

Application of novel data platforms

We have worked with a broad range of different data platforms for the evaluation of surrogate markers covering protein arrays, gene expression arrays, imaging modalities, metabolomics platforms. Our experience with high-dimensional data allows us to implement practical solutions with novel data types.

Exploratory analysis and visualisation methods

We recognise that some studies require alternative approaches to analysis and reporting of results. Where there is a large amount of information to be presented, the use of graphics allows that information to be put in proper context. We have broad experience in use of multi-dimensional data displays and the development of bespoke graphical displays.

Exploratory versus regulatory statistics

We have seen many times the problems caused by poor decision-making as a result of over-interpretation of exploratory analysis leading to wasted time and money. We understand where exploratory analyses can fit into the discovery and development process. We work with clients to place exploratory analysis into proper context and to make them aware of the potential risks. Our focus on helping our clients to conduct the best science and enabling studies to generate and test hypotheses.