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How Routinely Collected Data for Randomized Trials Provide Long-term Randomized Real-World Evidence.

Randomized clinical trials often have characteristics that make them not useful for health care decision making.1 Having too short follow-up periods is one of them. Such trials leave it open if shortterm benefits remain over longer periods and they often do not provide information on long-term safety or rare or late-occurring events. While more patient-relevant outcomes, such as stroke, disability, or mortality would often be desirable, combined end points or surrogate outcomes are typically the only feasible option within limited follow-up periods; however, they come with other problems and assumptions. Feasibility of clinical trials is often determined by costs and available resources. But, even with unlimited funds longer follow-up requires participants to longer bear the nuisance of trial-related data collection procedures and follow-up visits, participants may increasingly lose motivation and wish to return to their routine care, and the number of dropouts and associated risks of bias can increase. But even beyond missing data, collecting research-specific data creates artificial settings and decreases the generalizability and applicability of results. All this may jeopardize the usefulness of trials for decision making and may be perceived as inherent limitation of randomized trials. These issues are often used to promote nonrandomized, observational real-world studies despite all their well-known limitations.2 Fitzpatrick and colleagues3 report how routinely collected data from administrative records or registries are used to extend follow-up of randomized trials. They provide insights on long-term effects of treatments. They collected more than 100 examples of randomized evaluations of drugs, vaccinations, surgical procedures, and screening procedures over periods of up to 55 years. Many of these extensions confirmed the earlier effects but also revealed beneficial or harmful effects on mortality, cancer, or cardiovascular events that were previously unknown. Using routinely collected data for randomized trials has been described as disruptive technology.4 Indeed, randomized real-world evidence transforms clinical research, and the collection by Fitzpatrick and colleagues3 gives a plethora of examples of what is possible. In contrast to the inherent and too often ignored limitations of nonrandomized real-world evidence,2,5 many of the perceived limitations of randomized trials are actually manmade and can be overcome using routine data.6 Collection of data in usual care avoids artificial follow-up procedures by using existing infrastructures, costs decrease, and feasibility increases.7 Very long time periods can be covered and more patient-important outcomes and long-term safety can be evaluated.3 However, such promises come with novel challenges and may create novel problems.6 Issues of data quality and completeness are clearly among the most urgent topics, but this should not hinder investigators from pursuing this path. Reasonable data quality is a challenge for any type of realworld evidence, but substantially less for randomized trials as they do not require complex statistical models based on accurate measurements of all potential confounders. A specific beauty of outcome information from routine data is the formally blinded outcome ascertainment since those who collect the data in routine care are typically independent from the initial research enterprise. This may offset some other shortcomings, for example, misclassification biases.6 While some patients leaving the data infrastructure may still require active follow-up, randomized trials embedded in existing data structures, such as registry-based trials, have been realized with perfect follow-up and 0 dropouts.7 + Related article