How to optimize your data strategy to drive success in clinical development
In clinical development, data is the vital ‘foundation’ that supports your programs. To successfully bring a promising new therapy to patients, the quality of your evidence must be strong enough to gain approval from key decision-makers, including regulators, payers, and health technology assessment (HTA) agencies.
So, how can you strengthen the quality of your clinical evidence package? A key solution is to optimize your data strategy. As we discuss in our new eBook on data strategy planning, making just a few small changes to your planning approach can strengthen your ‘foundation’ and generate various benefits that enhance and expedite clinical development.
Download our free eBook to find out how to optimize your data strategy to boost success in clinical development.
If you think your data strategy might need a kickstart, then read on to learn more about how certain tactical changes can enhance the quality of your evidence package and boost the success of your clinical programs.
How your data strategy supports your clinical programs
First, let’s go over the main reasons for planning a data strategy. Although the key aim is to protect the quality of your clinical data, it’s also vital for identifying and plugging information gaps (such as when assessing your Target Product Profile, or TPP). Just as importantly, a data strategy helps you to define your approach to data collection, storage, and analysis, such as selecting a suitable electronic data capture (EDC) system and implementing ways to standardize data transformation processes.
Another crucial aim of your data strategy is to choose your trial design. Although there are many different types of design you can use, some may be more suitable for your program than others. For example, some adaptive trial designs allow you to assign newly enrolled participants to a more promising treatment arm after interim analysis, whereas others change the recruitment eligibility criteria to enrol patients who are more likely to benefit. However, changing your parameters midstream in this way can create complex statistical issues, which a well-planned data strategy will prepare for and resolve when they arise.
Considering these complex considerations, planning a data strategy not only requires a deep level of expertise but also enough time to formulate an optimal plan well before your program starts. Like the foundations of a house, your data is the crucial ‘foundation’ supporting the ongoing success of clinical programs, and it must be strong enough to avoid later collapse. So, what can you do to plan your data strategy so that the evidence package is as rock-solid as it can be?
How to optimize a data strategy and strengthen your clinical data
There are small, easy to implement changes you can make to optimize your clinical data strategy. For example, starting to plan your strategy in advance of Phase 1 (ideally as you transition from non-clinical to clinical studies) will ensure you have a ‘roadmap’ in place that resolves issues as they arise—and prevents any nasty surprises!
It’s also advisable to avoid planning on a trial-by-trial basis. Instead, plan a data strategy that accounts for the entire duration of your clinical program (i.e., a ‘program-wide’ strategy). As the traditional Phase 1—2—3—4 trajectory of clinical development is now more connected, a program-wide strategy can help prevent a data issue encountered during one phase from having immediate downstream consequences. As well as facilitating risk management throughout your program, planning a program-wide strategy can have many other benefits, such as improving patient access and market positioning once the trial phases are over.
Following the industry’s best practices can help you plan an optimized data strategy that benefits not just the quality of your data package but also the efficiency and cost-effectiveness of your programs. For example, centralized data collection and analysis via a single EDC can reduce the time and costs involved in training personnel. Implementing such small changes can go a long way towards staying within budget and hitting major project milestones.
It may also be worth seeking guidance early on from external specialists, such as biostatisticians and strategic consultants, who have a track record of planning successful data strategies. Working with an experienced partner allows you to mitigate all potential risks to your project and deliver the full benefits of an optimized plan.
The value of a well-planned clinical data strategy
An optimized data strategy can benefit clinical programs and avoid the issues that can arise when planning begins too late or occurs only on a trial-by-trial basis. One major risk of overlooking a strategic data plan is that the quality of the evidence package suffers and reduces the likelihood that decision-makers will approve the new therapy. Consequently, it will be more difficult to achieve a business objective, such as licensing out the asset or selling the company post-Phase 2. Alternatively, it will be much harder to successfully progress the therapy to Phase 3.
Adopting key tactics to preserve data quality can mitigate these risks and enhance clinical development, for example streamlining data management processes and reducing costs. As a result, it’s more likely you’ll gain a return on investments and create opportunities to develop other promising new therapies. What’s more, the time and effort invested by participants in your clinical trials will be worthwhile. Even more importantly, new therapies will ultimately reach patients and improve their lives.
Read our new eBook to discover tactics and tips on improving your clinical data strategy to enhance the success of your clinical development.