Which brings me to a recent review in the New England Journal of Medicine by Laura Mauri and Ralph D’Agostino titled “Challenges in the Design and Interpretation of Noninferiority Trials.” This review is very well written, perhaps require-reading-for-epidemiology-students well written. In infection control, it is important to recognize that most de-implementation studies are really non-inferiority trials. For example, when we discontinue contact precautions, we are really suggesting that “stopping contact precautions” is non-inferior to continuing contact precautions in preventing multidrug resistant organism transmission, of course ignoring that compliance with contact precautions is probably so poor that they are basically the same intervention!
In the contact precautions example, we would be testing whether stopping contact precautions “is not worse than the control (continuing contact precautions) by an acceptably small amount, with a given degree of confidence.” The null hypothesis would be that discontinuing contact precautions leads to higher transmission of MDRO (i.e. is worse) and rejection of the null hypothesis is used to support the claim that discontinuing CP is noninferior.
Further discussion about the design and analysis of these trials is way beyond the scope of a humble blog post; however, the authors include nice descriptions of methods for deriving noninferiority margins, the “constancy assumption” and statistical analysis approaches. But their sixth and seventh components of noninferiority trials are worth mentioning from an infection control standpoint:
6) Adequate ascertainment of outcomes: The authors write that ”incomplete or inaccurate ascertainment of outcomes, as a result of loss to follow-up, treatment crossover or nonadherence, or outcomes that are difficult to measure or subjective, may cause the treatments being compared to falsely appear similar.” I would suggest that studies that seek to de-implement contact precautions that do not include admission/discharge surveillance cultures seeking to detect transmission events fail this criteria.
7) Issues with “Intention-to-Treat” in noninferiority designs: In a superiority studies (typical randomized, controlled trials), intention-to-treat analysis, where anyone who receives the treatment is included even if they get one dose, is the gold standard. The authors write: ”In a noninferiority study, however, if some patients did not receive the full course of the assigned treatment, an intention-to-treat analysis may produce a bias toward a false positive conclusion of noninferiority by narrowing the difference between the treatments. In some instances, a per-protocol analysis, which excludes patients who did not meet the inclusion criteria or did not receive the randomized, per-protocol assignment, may be preferable in a noninferiority trial. However, a per-protocol analysis may include fewer participants and introduce postrandomization bias. In general, both the intention-to-treat and per-protocol data sets are important. We suggest analyzing both sets and examining the results for consistency.”
Just some things to think about as we read the coming wave of de-implementation studies in infection control, including diagnostic stewardship.
Eli N. Perencevich, MD, ACP Member, is an infectious disease physician and epidemiologist in Iowa City, Iowa, who studies methods to halt the spread of resistant bacteria in our hospitals (including novel ways to get everyone to wash their hands). This post originally appeared at the blog Controversies in Hospital Infection Prevention.
