For clinicians, this means we must resist the temptation to generalize too rigidly. Situated choice reminds us that every patient’s disease is an improvised performance, not a scripted play. It humbles us away from notions of predestination. Yet situated choice also inspires us to meet our patients where they are—in their environment, with their biology and in their particular chapter of life.
Tuning in to Statistical Noise in Clinical Trials
Zooming out of the basic science labs, we see the implications of situated choice in clinical research. Although clinical trials are the backbone of evidence-based medicine, they still feel a little dissonant in rheumatology. Our diseases are inherently heterogeneous, slow moving and filled with outliers. And yet our trials depend on averages—mean DAS28 scores, pooled ACR response rates and hazard ratios that assume or impose a homogeneity that simply isn’t there.
Because of this, we are inclined to dismiss outliers as statistical noise, but sometimes that noise carries a tune worth paying attention to. In every clinical trial, there’s someone who has a miraculous response—and someone who gets dramatically worse. We rarely investigate these cases with the same vigor that we celebrate P values and confidence intervals. But Bob and John’s case suggest that maybe we should.
Sometimes, those very outliers help us identify biomarkers, stratify subgroups and fine-tune our therapies. For example, early hints of JAK inhibitors’ utility in many autoimmune conditions came from noticing who responded—often unexpectedly well— to medications for other indications.4
Rather than viewing variability as a nuisance to be averaged out, we might see it as an invitation. What might we learn if we followed up every surprising responder—or non-responder—with the same rigor we devote to standardized endpoints? What if we treated clinical trials not as blunt instruments for population-wide answers, but as portals into the messy reality of immunologic individuality?
Variability as a Loyal Friend
To researchers and clinicians alike, variability can feel like the enemy. Even outside of twins, one patient flares despite biologic therapy; another feels great on hydroxychloroquine alone. One may report prohibitive side effects to methotrexate, while another asks if they can stop because they feel as if they are cured. As clinicians, we yearn for predictability, whether it is a sensitive and specific lab test that forecasts disease trajectory, guidelines that don’t just classify but wholesale diagnose a condition early and reliably, or a treatment algorithm that navigates the dazzling array of immunotherapies. But these are utopian thoughts.
