Real-world evidence studies are becoming increasingly popular in pharmaceutical development. But to ensure such studies are feasible and of high scientific quality, a well-written study protocol is essential. Let’s take a closer look at how to write a successful study protocol for real-world evidence studies:
Study protocols are particularly important in real-world evidence studies
Clinical trials typically involve randomizing trial participants into different groups: the treatment and the control arms, and many clinical trials have the control arm receiving a placebo. Randomization is done to ensure that the group receiving treatment does not differ in any important respect from the control group. These trials may also be double-blinded.
However, unlike such clinical trials, RWE studies are neither randomized nor double-blind — and no one receives a placebo. Instead, they make use of real-world data — data and information gathered from real-world sources, such as electronic health records, claims databases, patient registries, and other healthcare data sources. As a result, it becomes more difficult to establish causal links. Researchers must themselves, via the registers or other RWD, identify patients who could have been prescribed these medicines by clinicians, but were not. In short, a control group is created from the same patient population as the treatment group for the sake of an apt comparison.
Gathering study protocol content
Writing a study protocol requires gathering puzzle pieces from a variety of areas to see the whole picture and must include all scientific questions and methods for answering those questions. It is also a tool to communicate the study-implementation plan to all parties involved, and the researcher, the clinical expert, the statistician, and the programmer must all be able to understand it. Plenty of attention to detail and a thorough understanding of the research topic are required. The content of the protocol will vary from study to study, but usually includes information on the following:
The background of the disease area and the rationale behind the study.
The research objectives.
The inclusion and exclusion criteria for the patient population.
Study timeframe – study start and follow-up period.
Clearly defined outcomes/endpoints.
The medicines to be studied.
The data sources to be used.
A clear analysis plan must also be included, describing the data-management and statistical models to be used, as well as the epidemiological methods to be used to address bias.
Challenges to planning an RWE study
There are many potential challenges when planning an RWE study, such as:
Accounting for bias: this is, for instance, to avoid confounding, for example when a control group happens to have a higher average age than the treatment group and the outcome is a stroke, so the control group has a greater risk of stroke due to their higher average age.
Clearly defining outcomes: e.g., the diagnoses or laboratory values needed to define the disease outcomes; in some cases, algorithms need to be developed.
Having a plan for complying with data protection and ethics laws: this could include issues such as secure storage of sensitive data.
Clearly mapping relevant and available RWD: what variables are needed in the study, and can these be found in registers and/or patient records?
To overcome these challenges, investigators should carefully consider the epidemiological methods to be used, clearly define the outcomes, ensure compliance with data protection and ethics laws, and have a good overview of data availability in the registries.
Interested in learning more? Cytel’s Real-World and Advanced Analytics team will be at ISPOR Europe 2023 at Booth #C3-049. Click below to book a meeting with our experts:
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