It may seem counterintuitive that collecting too much data is a common pitfall in clinical study design. After all, clinical studies are labor-intensive, time-consuming, and expensive, so why wouldn’t you try to get everything you can out of your study? In this blog post, we discuss why there is such a thing as too much data and share tips on how to avoid the perils of a “Franken-study.”
Perils of collecting too much data
Designing a clinical study is exciting and it is tempting to gather everything you can about your product during a clinical study, especially if it represents the first major use of the device in its intended patient population. However, if you are not purposeful in selecting which aspects to evaluate, you may end up with a mismatched accumulation of random datapoints — the dreaded “Franken-study.” Below are a few perils of collecting too much data.
- Diluted purpose: By studying everything, you risk learning nothing. A study without a defined focus does not generate compelling results that you can share with your stakeholders.
- Overly complicated protocol: If the study purpose, endpoints, and required assessments are hard to follow, your study will face risks of increased protocol deviations, missing data, and data entry errors.
- Increased workload for sites: Every study data point requires staff to perform an assessment on a subject and a research coordinator or investigator to record the results. This research team is essential to your study success – you don’t want them burning out from “collection fatigue.”
- Increased costs: The case report forms and the database (EDC) will be more complicated and expensive. Study monitors will need to invest more time monitoring “nice-to-have” data, the site will need to invest more time to resolve data discrepancies, and the data management and statistics teams will need to analyze a potential mountain of data. This work and the associated costs add up.
- Delayed results: More complicated studies may present enrollment or retention challenges that will cause your study to take longer. An involved data analysis and reporting process also takes time, especially if your extraneous data collection requires long-term follow-up and extensive monitoring. The longer your results are delayed, the longer it will take to get your product on the market, in the hands of physicians, and most importantly, into a position to help patients.
Understand the study purpose and key stakeholders
Instead of getting “all the data you can,” stay focused on what your key stakeholders need to know about the safe and effective use of your product. In other words, the first step in clinical study design is to consider the purpose your clinical data will serve and the audience – or stakeholders – who will care about the data.
Clinical studies help address your product development design validation requirements and provide data for regulators and end users to assess its safety and effectiveness. The data also allows your company to make specific claims in the product labeling and marketing materials. Other key stakeholders include reimbursement payors and, for start-up companies, financial investors. To zero in on the key data to demonstrate the safety and efficacy of your device, start by evaluating the benefit your product provides. Does it extend life, minimize disability or reduce disease symptoms? Does it help to avoid riskier or more invasive treatments? Does it improve the quality of patients’ lives or enhance their ability to function normally? If so, these are among the most important data to collect. For more insights, don’t forget to refer to your risk assessment documents, competitor clinical data, available society guidelines for the clinical field, and the opinions of your medical advisors.
Define focused study endpoints
With clear understanding of your study purpose and stakeholders, it’s time to define focused study endpoints. Most studies require at least one primary endpoint and several secondary endpoints that evaluate safety and effectiveness measures and represent critical information about the benefits and risks of your product.
Further evaluations might be needed but a long list of “additional,” “tertiary,” or “ancillary” endpoints could be evidence that you’re looking for too much. For a more comprehensive discussion of defining endpoints, see our blog post Five Key Qualities of Study Endpoints.
If your team struggles with how to handle additional “nice-to-have” endpoints, consider whether you can evaluate them in a pre-clinical study, human factors study, or a post-market clinical study. Carefully weigh the potential benefit of adding more endpoints against the potential risks noted above.
Avoid the pitfall of collecting too much data
While it seems counterintuitive — and maybe that’s why this is a common pitfall — the cost is high for those who try to collect too much data and instead overcomplicate their clinical program. When you carefully consider the purpose of your study and the stakeholders for your clinical data, you can accurately scope the work and develop a clinical study design that meets your business objectives: a timely, cost-effective study that gathers meaningful data to support your market authorization and market adoption.
