In the quest for clinical success, we all strive for evidence packages of the highest quality. If the clinical data is strong, then a promising new therapy is more likely to obtain approval from key stakeholders, such as regulators and payers . As a result, you’ll get the chance to develop a therapy that will help many patients (and you will likely gain returns on your investments). As we discuss in our new eBook on data strategy planning, a carefully planned data strategy can help mitigate risks to your programs and enable you to successfully achieve your goals.
Discover how to plan a data strategy that enhances your clinical programs and enables new therapies to reach patients in our new eBook.
In the high-stakes environment of clinical development, it is never too early to start protecting your valuable data assets with a first-rate strategy. So, keep reading to learn which planning approach to use, who should be involved, when it is best to start, and why it is well worth going to all the effort.
What is the optimal approach to planning your data strategy?
It can be far better to form a program-wide strategy rather than planning on a trial-by-trial basis. Focusing just on individual trials when planning a data strategy can be very risky. Instead, considering the entire duration of the program can better protect the quality of your clinical data.
Why is this? Well, it is mainly because of changes to the traditional Phase 1—2—3—4 trajectory of clinical development. These four phases have been very siloed in the past but are gradually becoming more interlinked. This means that a data issue occurring during one phase can more easily affect data integrity at a later stage. As such, planning a program-wide strategy can help to predict potential issues and prepare solutions for them as and when they arise.
A complete data strategy can overcome various data handling issues ranging from bias (e.g., blinded outcome assessors) to missing data. Additionally, conducting simulations of trial designs based on program-level considerations can predict the likelihood of failure, so you can make appropriate alterations before issues occur.
Who is responsible for data strategy planning?
Specialists from clinical, biometrics and data management, regulatory, and project management functional groups are all key players in forming your clinical data strategy for the pre- and post-approval stages. It is valuable to foster close collaboration among these different departments and form communicative partnerships with external specialists, if needed.
For example, effective collaboration between biostatisticians and data managers can improve planning efficiency and enhance the quality of the data outputs overall. Additionally, a cross-departmental governance committee comprised of key stakeholders from each relevant department can encourage everyone to understand the impact of how the data will be sourced, collected, managed, and stored.
What is more, when planning for the post-approval stage, consultation with market access experts can ensure you generate suitable data to show payers and HTA agencies. Doing so helps ensure the approved therapy can be made accessible to patients once the trial phases are over. For instance, collecting real-world data on patients’ quality of life and competitive benchmarking through model-based meta-analysis (MBMA) can help to justify the cost-effectiveness of the therapy versus comparators, and its affordability to the target population.
When is the best time to start planning your data strategy?
For example, there are numerous logistical and administrative factors you’ll need to consider, such as implementing suitable electronic data capture (EDC) systems that enable data standardization processes and the integration of data from multiple vendors (which can be as many as 15 at a time). Furthermore, it is imperative to get the plan reviewed by all the relevant people so you can make any necessary amendments before the program starts. This helps to prevent any late-stage protocol amendments, which can be time-consuming and costly.
The clock will also be ticking while you make key decisions about the trial design. For instance, you could decide to use historical control data (‘historical borrowing’) to inform a control arm, which can increase power and reduce type I errors. Other points that will take time to consider include selecting appropriate randomization procedures and weighing up the pros and cons of using an adaptive trial design (instead of a traditional fixed design). Adaptive designs can make better use of resources but create statistical issues that need to be carefully managed to preserve the integrity of the data.
Why is it worth rethinking your approach to planning a data strategy?
There is no doubt that planning a top-notch clinical data strategy is a challenging process. As such, if you start planning too late, then it is likely you will not be able to address the numerous and complex considerations involved. This might mean you end up with a plan that could jeopardize the integrity of your evidence package and so reduce the chance that your new therapy will gain approval from regulators and payers.
As discussed in this blog, by implementing a program-wide plan you can:
Mitigate risk to your data assets
Boost your credibility with decision-makers
Minimize delays to the pipeline and expedite your therapy’s time to market
Overall, optimizing your data strategy is well worth the effort. It’s likely you will generate top-quality evidence packages that can strengthen your case for support from decision-makers, and help you make huge strides towards bringing effective new therapies to patients who really need them.
Download your free copy of the new eBook to learn more about how to revitalize your data strategy and drive the success of your clinical programs.