Though the effectiveness of many treatments varies widely across individuals, treatments are rarely rigorously evaluated on a personal basis. Instead, pressed for time, physicians rely on trial and error testing. But protocols for personal experiments are simple, and their benefits are well documented.
“We spend so much effort on precision in diagnosis, yet we have very little precision about treatment,” observed Dr. Eric Larson, a professor of medicine at the University of Washington.
Experts say that, even for widely-studied drugs, “the trial and error approach to medicine is not cost efficient because several of the most prominent drugs only work on one-third to one-half of patients. Thus, many patients are subjected to the cost, inconvenience, side effects, and potential adverse reactions of taking medicine that will have little to no clinical benefit.”
Responding to this problem in the late 1980s, medical researchers at McMaster University in Canada determined that many treatments for chronic conditions could be successfully evaluated on a person-by-person basis. (You can read a great history of that work here.) They advocated applying routine research techniques — standardized reporting, randomized treatment blocks, blinding, placebos and statistical rigor — to individual testing.
For chronic conditions, single patient trials are “not only ethical but arguably obligatory to undertake,” one researcher argued in 2002.
Though clearly needed, single person experiments are still relatively underutilized. “The apparent simplicity of this study design has caused it to be enthusiastically touted in some research fields and yet overlooked, underutilized, misunderstood, or erroneously implemented in other fields, ” note Jean Slutsky and Scott R. Smith of the US Agency for Healthcare Research and Quality.
The agency’s user guide to personal experiments, published in 2014, is an invaluable resource best practices in single person experiments.
The user guide defines n-of-1 trials simply as “multiple crossover trials, usually randomized and often blinded, conducted in a single patient.” Best practices include:
- “Treatments to be assessed in n-of-1 trials should have relatively rapid onset and washout (i.e., few lasting carryover effects).”
- “Regimens requiring complex dose titration (e.g., loop diuretics in patients with comorbid congestive heart failure and chronic kidney disease) are not well suited for n-of-1 trials.”
- Blinding with the help of a compounding pharmacist or trusted friend can reduce placebo effects. Unfortunately, “even for drug trials, few community practitioners have access to a compounding pharmacy that can safely and securely prepare medications to be compared in matching capsules.”
- “One-time exposure to AB or BA offers limited protection against other forms of systematic error (particularly maturation and time-by-treatment interactions) and virtually no protection against random error. To defend against random error (the possibility that outcomes are affected by unmeasured, extraneous factors such as diet, social interactions, physical activity, stress, and the tendency of symptoms to wax and wane over time), the treatment sequences need to be repeated (ABAB, ABBA, ABABAB, ABBAABBA, etc.).
- “For practical purposes, washout periods may not be necessary when treatment effects (e.g., therapeutic half-lives) are short relative to the length of the treatment periods. Since treatment half-lives are often not well characterized and vary among individuals, the safest course may be to choose treatment lengths long enough to accommodate patients with longer than average treatment half-lives and to take frequent (e.g., daily) outcome measurements.”
- “In n-of-1 trials, systematic assessment of outcomes may well be the single most important design element. … In the systematic review by Gabler et al., approximately half of the trials reported using a t-test or other simple statistical criterion (44%), while 52 percent reported using a visual/graphical comparison alone. Of the 60 trials (56%) reporting on more than one individual, 26 trials (43%) reported on a pooled analysis. Of these, 23 percent used Bayesian methodology, while the rest used frequentist approaches to combining the data.”
As noted above, the benefits of n-of-1 trials include reducing the cost, inconvenience, side effects, and potential adverse reactions of taking medicine that has little to no clinical benefit. A protocol counters the influence of numerous biases and errors. In studies of n-of-1 trials for some chronic conditions, a personal treatment experiment resulted in a treatment change for up to two thirds of individuals.
The most important result, though, may be increasing the participants’ sense of playing an important role in their own treatment journey.
To learn more about creating your own n-of-1 test for your own symptoms, engage with the GuideBot below!