Today’s clinical trials are much more complex than those of the past. Unlike conventional trials, many of today’s studies are adaptive, using real-time biostatistics to collect and analyze data on an ongoing basis and adjust the trial as necessary (e.g., based on response rates). This shift has made biostatistics skill sets even more essential for clinical development teams.
Modern clinical trials have multiple clinical sites, more treatment arms, and adaptive designs. Addressing these challenges in a productive manner requires increasing sophistication of biostatistical programming and clinical programming capabilities.
To tackle these challenges, clinical development teams need access to clinical programming capabilities, particularly those that provide more sophisticated biostatistical programming. Modern clinical trial design requires estimating treatment effects and sample size requirements, managing protocol deviations, conducting frequent interim analyses, identifying when a target is met or if it needs to be re-estimated and calculating optimal post hoc analyses.
Unexpected safety findings, early signals of efficacy or changes in subject characteristics may require additional programming. A clinical study team must be able to adapt its plan rapidly and decide on a new path forward. The ability to handle frequent midcourse corrections efficiently is an essential capability in modern clinical development.
The Adaptive Trial
Many new trials today are designed to adapt as they go, using data that is collected throughout the trial duration. This involves multiple types of data such as clinical measurements, biomarkers, image analysis, lab results, electronic medical records and EHRs. While adaptive trials present major challenges in terms of scientific design, data management and biostatistics interpretation, it also offers unprecedented opportunities to accelerate therapies through early proof-of-concept.
Adaptive trials pose a significant technical challenge for clinical programming. Data must be captured, cleaned, verified, transformed, processed and structured to enable downstream data mining. It must then be integrated with other relevant sources of information to enable effective monitoring of patient status and response to therapy. Critical data must be stored in a central location so that it can be accessed from any endpoint. In addition, data integrity needs to be maintained across all endpoints while ensuring that they are HIPAA compliant.
Clinical Programming and Biostats Role in Trial Design
Biostats and clinical programming roles are changing dramatically in adaptive trial design. Biostats’ usual roles of producing analysis with pre-specified statistical objectives (power and sample size) aren’t as relevant in these types of trials. Rather, biostats and clinical programming should work with clinical development teams to identify outcomes that are most important for patients, understand potential drivers of variability, think about how to use adaptive trial methods that might lead to unexpected findings, and continuously revise trial designs as data become available.
As biostats and clinical programming roles are changing in adaptive trial design, we can anticipate several outcomes that will affect how we work together with other members of the clinical development team. For example, biostats may still have tasks like analyzing data to determine statistics, but clinical programming may be involved more closely in specifying what statistical tools to use at different points in an adaptive trial.
Collaboration with your clinical programming team will be essential in determining how to design an adaptive trial. This means that biostats should work closely with other members of your team during study planning meetings to determine which outcomes are most important, how you will assess those outcomes, and what statistical objectives you need to meet at various points during your trial.
Data Quality in an Adaptive Trial Environment
Adaptive trials present particularly daunting data quality challenges because of their design. An adaptive trial can also mean more frequent changes to protocols, objectives, measures, and other protocol design elements that make it harder to ensure data are recorded consistently across sites.
Data Reviews: Early and Often
Interim data reviews must be frequent and should happen as early as possible. Analysis can become more time-consuming and complex as clinical trials grow, making it easy to overlook problems with data quality. Regular reviews ensure that issues are caught early on.
Data are collected at several points during clinical development, including before a trial begins, in interim data collection periods, and at various stages of patient enrollment. All of these time points require data reviews to ensure that any potential problems can be caught early. In addition to saving time later on, performing earlier reviews allows for cost savings because additional analyses may not be needed as new information is collected.
Conducting clinical trials is a complicated affair that relies on expertise in multiple areas, including biostatistics and clinical programming. With adaptive trials, companies need to rely more heavily on biostats capabilities early in the process to meet their complex data requirements. In order to take advantage of these new opportunities and better position yourself as a company developing new drugs or bringing them to market, it’s important to focus on modernizing your biostats infrastructure.
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