It is widely acknowledged among drug developers that one of their most important assets is the data generated during clinical trials. Hence, it is no surprise that many companies plan and execute a strategy to protect the quality of the clinical data they produce. It is, however, easy to underestimate just how much time and expertise you need to address the numerous and complex considerations involved in the planning process.
Unlock top tactics and tips on how to plan a rock-solid data strategy to minimize risk and boost clinical success in our latest eBook.
If you are keen to find out how to optimize your clinical data strategy, read on to discover five of the top tips outlined in our eBook from specialists working in the Strategic Consulting, Clinical Research Services, and Data Management teams. Their global reach ensures top insights from every corner of the world.
Tip 1. Plan to overcome phase-specific and program-wide challenges
As discussed in our previous blogs of this series, it can be valuable to plan a data strategy that considers the entire duration of your program (i.e., a ‘program-wide’ approach). Adopting such an approach can ensure continual improvement throughout the program, which will eventually help you gain support from decision-makers.
Another benefit of a program-wide approach is that it can consistently protect the quality of your data by identifying and mitigating potential risks across the development pathway. As the next steps in your program will be clearly signposted, you will be able to immediately resolve issues as they arise. This will help you to minimize costly delays to your timeline and avoid the risk of having to re-run trials, or even terminate your program.
However, within the framework of a program-wide data strategy, it is also worth considering the unique challenges that can arise during the different phases of clinical development. For example, logistical and administrative issues commonly occur during Phase 1 studies, and knowledge gaps typically need to be filled in Phase 2 trials to strengthen the evidence package. As such, a data strategy can greatly benefit from having solutions in place that effectively overcome these phase-specific challenges.
Tip 2. Start planning as early as possible
Our experts recommend that you begin planning your data strategy and trial design as early as possible, prior to starting Phase 1 and ideally as you transition from pre-clinical to clinical studies. Planning early can be highly beneficial. It will give you enough time to optimize your approach to data collection, handling, and analysis, and consult a variety of internal and external specialists on all aspects of your data strategy. For example, getting the plan reviewed by all relevant people before the program starts will allow you to make any necessary amends before starting the trial; and this in turn will avoid the temptation of investing time and money in a potentially flawed approach. Forward planning can also help you to identify potential issues and resolve them quickly, before they occur.
From an early stage, it’s vital that everyone is working towards the same goal: getting a therapy approved that will help patients, be trusted by doctors, and be commercially successful. To this end, it can help to form a governance committee consisting of key stakeholders from all relevant departments (i.e., clinical, biometrics and data management, regulatory, and project management functional groups). Additionally, setting up a data monitoring committee well before starting Phase 1 can help to independently monitor patient safety and treatment efficacy data throughout the entire program.
Tip 3. Precisely define your approach to data collection and analysis
It is vital to put plans in place that enable you to collect the right data, at the right time, in the right amount, and in the right way. For example, it is better to collect only the data you need at each step so you can make informed decisions and address any regulatory queries without having to invest any more resources than necessary. In later phases, you might consider collecting real-world data on patient quality of life, which is becoming increasingly important in health technology assessment (HTA) studies.
Moreover, it can be useful to define a consistent approach to data handling, such as how to control bias and minimize incomplete data collection. Biostatisticians can also help establish appropriate data analysis procedures so that you can produce actionable insights and make optimal decisions both for the project and the well-being of patients.
Tip 4. Carefully choose and manage your trial design
One key consideration you will need to address when planning your data strategy is deciding which trial design you will use. A specialist team can look at the existing data to inform the trial design, and then help you manage any issues that may arise.
Consider the following example: Our expert team recommends that you consider an adaptive trial design, as it can increase the efficiency, cost-effectiveness, and flexibility of trials while maintaining statistical rigor. However, an adaptive trial design involves changing the parameters of the study midstream, which can create complex statistical problems. It can, therefore, help to have experienced biostatisticians on hand throughout the program to maintain the trial’s integrity and guide you on how to interpret and report the results.
When planning your trial design, it can also be valuable to conduct a clinical trial simulation (CTS) based on program-level considerations to identify the probability of downstream success. These computer-based bio-simulation strategies are helping to drive better decisions in clinical development at various phases, including how to optimize trial designs.
Tip 5. Foster communicative partnerships
It can be extremely useful to have closely aligned biostatistics and data management departments working effectively together. For example, when statisticians and other stakeholders are designing the study, data managers can add value by reviewing the protocol drafts and advising what is possible in terms of data collection. Additionally, a collaborative approach can also help to identify and address data issues proactively and in real-time to minimize delays while improving data quality.
If you decide to work with an external partner, our experts advise you to choose one that can smoothly integrate into your team and workflows to minimize delays and logistical issues. Approaching it as a partnership can also be hugely beneficial for your clinical programs. For example, establishing your goals and expectations from the very early stages and maintaining clear communication throughout can ensure seamless project management.
Why go to the effort of optimizing your clinical data strategy?
There is no doubt that clinical development is a risky business, with 80-90% of compounds in clinical trials failing due to toxicity or lack of efficacy despite substantial investments in research and development. In this high-stakes environment, there is no room for error. Like the foundation of a house, the data you generate needs to be solid enough to convince decision-makers to approve your new therapy—otherwise, your programs will come crashing down and promising medicines will fail to reach patients.
A well-planned strategy can reinforce data quality to minimize risk to your programs, but this can be a complex and challenging process. Following our tips and other best practices can help your data stand strong through all phases of development to ensure patients get the treatments they need.
Download our new eBook for more tactics and tips on planning a data strategy that strengthens the quality of your evidence to drive successful clinical development.
