Thomas R. Frieden, New England Journal Med 377;5 August 3, 2017

Main findings

This article doesn’t specifically discuss lymphoedema research but there is a lot of food for thought when considering research. It also includes a useful table that compares the strengths and weaknesses of various study designs. The key messages include:

• In large, well-designed trials, randomization evenly distributes known and unknown factors among control and intervention groups, reducing the potential for confounding. Despite their strengths, randomised Controlled trials (RCTs) have substantial limitations. Although they can have strong internal validity, RCTs sometimes lack external validity; generalizations of findings outside the study population may be invalid.
• RCTs usually do not have sufficient study periods or population sizes to assess duration of treatment effect.
• Selection of high-risk groups increases the likelihood of having adequate numbers of end points, but these groups may not be relevant to the broader target populations.
• There are increasingly high costs and time constraints of RCTs.
• RCTs often take years to plan, implement, and analyze reduce the ability of RCTs to keep pace with clinical innovations; new products and standards of care are often developed before earlier models complete evaluation.
• Many other data sources can provide valid evidence for clinical and public health action. Observational studies, including assessments of results from the implementation of new programs and policies, remain the foremost source, but other examples include analysis of aggregate clinical or epidemiologic data.
• Objections to observational studies include the potential for bias from unrecognized factors along with the belief that these studies overestimate treatment effects. Comparisons of validity between observational studies and RCTs have dispelled many misperceptions.
• Large observational studies, with longer follow up, can be tailored to minimize bias in a manner analogous to the way bias is minimized in RCTs.
• No study design is flawless, and conflicting findings can emerge from all types of studies. The following examples show the importance of recognizing the strengths and limitations in all data sources and finding ways to obtain the most useful data for health decision making.
• Elevating RCTs at the expense of other potentially highly valuable sources of data is counterproductive. A better approach is to clarify the health outcome being sought and determine whether existing data are available that can be rigorously and objectively evaluated, independently of or in comparison with data from RCTs, or whether new studies (RCT or otherwise) are needed.
• For example, although an RCT may show the benefit of a drug, large observational studies can be conducted to refine dosages and identify rare adverse events.
• There is no single, best approach to the study of health interventions; clinical and public health decisions are almost always made with imperfect data.
• The goal must be actionable data — data that are sufficient for clinical and public health action that have been derived openly and objectively and that enable us to say, “Here’s what we recommend and why.”