A physician often needs to guess as to what intervention will be the best fit for a patient. One of us was struck by an honest statement from a clinician that about 30% of what any physician does is arbitrary. Such a statement may cause some to feel angst, but it is not meant to be flippant. Given a myriad of variables affecting a patient’s response to intervention, each patient is likely different and even extreme in some relevant variable. Put more colloquially, “we are all normal until you get to know us.”

Individual variability manifests in clinical research as well as clinical care, though how it is viewed and managed in the two settings is different. In a clinical trial variability is viewed in aggregate, while for the general practitioner variability is often appreciated after treatment of a single, unique patient. In clinic, for example, a guess is often made that an antibiotic that works well in most will work well as an initial treatment for the patient at hand, though in most cases there is no baseline knowledge regarding the level of resistance in the patient’s infection, or the exact risk of an allergic response in a particular patient. Whatever the response ends up being it shapes the next steps in a patient’s care. This iterative path is different from the approach to handling individual variability in research of groups, where statistical analyses of aggregation are applied so that general tendencies can be appreciated. Most statistical approaches guide us to making inferences about a population based on data from a sample. Then, there is the non-linear process of applying generalities to individuals.

The Gap Between Our Question and Our Data

There is, of course, inherent misfitting in applying group data to individuals. Put succinctly by Dr. Jane Nikles of the University of Queensland:¹  

The fundamental problem facing the clinical research enterprise is this: what clinicians (and patients) want to know is not what clinical trials are equipped to say. Randomized controlled trials (RCTs) are all about the average patient; they yield average treatment effects. Doctors and patients want individual treatment effects; how a given patient will respond to Treatment A vs Treatment B. No amount of statistical subterfuge can make standard-issue, parallel group RCTs reveal precisely the results we want….

Sadly, trying to predict individual outcomes from aggregated data leads to mishaps, including unexpected allergic reactions, toxicity due to genetic variation, and negative interactions with other interventions given during polypharmacy, as examples. But perhaps one of the less considered downsides is that many individuals take medications that do not substantially help them.²    Estimates of efficacy rates across broad categories of drugs range from 30-80%,³    meaning that a significant proportion of patients take drugs that simply do not help them with their problems. Ideally, efficacy would be increased by pharmacogenetic testing, to identify those with variants who will not respond to a drug. However, at present, even as this testing becomes more common, it is still the case that only about 6.6% of the population has currently received pharmacogenetic testing.⁴    Thus, currently, many prescribers are still simply ill-informed about specific, impactful genetic variants a particular patient possesses. This makes the observation of the GlaxoSmithKline executive Allen Roses over 20 years ago still relevant today: “Drugs on the market work, but they don’t work in everybody.”⁵   

However, while genetic variation is important to predicting results, it is not the only variable of concern. For example, focusing on the effectiveness of statins for a moment, we can appreciate that genetic variation is one factor at least modestly related to lower efficacy,⁶    but it is not the only force at work. In fact, there is a good case to be made that in the course of prescriptive decision making, many patients with low 10-year risk of MI or ischemic risk are given a prescription for a statin not likely to help them.^7-9    Why is this? Clinicians face pressures. Factors such as time constraints, the relative ease of prescribing a statin vs delving into the details of lifestyle changes, narrowing focus exclusively on a change in cholesterol, and fear of not treating combine to fuel prescriptions for those with low risk.¹⁰    If it is so difficult to get to the right fit for a patient’s treatment even when there is plentiful group data that parses out risk and benefit, how much more difficult is this process when risk/benefit data is relatively scarce? 

Why N-of-1 Trials?

In addition to encouraging more pharmacogenetic testing, one of the best ways to individualize medicine may be to incorporate into clinical practice a study design that allows both patients and physicians to follow a process leading them to both conclude they have found the best-fitting treatment. An effective approach for achieving this alignment is known as an N-of-1 trial, a study in 1 patient with multiple crossovers between interventions. In describing this approach, its original proponents, Hogben and Sim, described the need for the tool, thusly:¹¹   

The now-current recipe for a clinical trial based on group comparison sets out a balance sheet in which individual variability with respect both to nature and to previous nurture does not appear as an explicit item in the final statement of the account; but such variability of response to treatment may be of paramount interest in practice. 

Where clinical trials of groups seek to average individual variability, N-of-1 trials embrace individual variability. Hogben and Sim argue that careful, successive observations in a single person ought not be viewed as experimentally inferior data because successive observations can, in fact, provide great clarity on the effect of an intervention. They stated:

A little reflection will suffice to discredit the assumption that inquiry limited to successive observations based on a single individual is indeed foreign to the pattern of a rigorously controlled experiment…. 

Hogben and Sim then go on to describe the “self-controlled” and “self-recorded clinical trial” where a focus on careful, oft-repeated observations is made to provide “one-to-one correspondence of stimulus and response” as conclusive of an outcome. They give a hypothetical illustration in their article. To paraphrase, let’s suppose one conducts 13 trials with substances not expected to have a particular effect and 13 trials of a substance suspected to be active, in a non-regular (“chaotic”) pattern of administration that keeps the subject from correctly guessing what is to be administered. If each of the 13 trials with the substance suspected to be active shows a response and the other substances do not, then clearly the substance suspected to be active is indeed active in this patient. The focus of Hogben and Sim is on drawing informed conclusions from self-controlled experimentation of “individuals with observable peculiarities,” rather than application of statistical analysis to data viewed in aggregation. Concerned with approaches that emphasize group data rather than careful observation of the individual, they state: “It is indeed a mischievous delusion that statistical recipes can remedy incompetent observation.”

Interestingly, while Hogben and Sim felt it important to keep the order of interventions chaotic, they also were not proponents of randomization of the interventions, arguing that a pattern of intentional disorder could be helpful if it made it easier to see a delayed effect or lessen the chances of misinterpreting a normal rhythm. Perhaps an example of what they meant could be making sure that an intervention thought to be active would at some point be followed by enough non-effective interventions in series to allow a benefit with delayed onset to manifest.

Building on Hogben and Sim’s work, Guyatt in 1986 went on to describe “randomized trials in individual patients” in which a single patient received a series of a pair of treatments (active and placebo or active and another active) with the order of administration being randomized.¹²    This adds structure different from the “chaotic” pattern called for by Hogben and Sim, utilizing the element of randomization to help keep the patient guessing about what they are given. In contrast to many study designs of today it is the order of interventions that is randomized, rather than subjects themselves being randomized to groups. Guyatt provided an example of the application of the method elucidating the fact that the particular patient with asthma in their N-of-1 trial experienced worsening symptoms each time theophylline was included in his treatment regimen. This setting was an ideal one for what are now commonly referred to as N-of-1 trials, as the design is most efficient in the context of chronic diseases that are stable and with interventions that have short-lasting effects.¹³    While the use of randomization to help keep the patient from anticipating which intervention they are receiving may often make sense, we wonder, too, if there may be instances where some form of “chaotic” or “intentional disorder” in the pattern of administration should still play a role. For example, if randomization produces a sequence for administration of stress-affecting interventions given multiple times a day that happens to direct that one intervention be predominantly studied at the peak of morning cortisol secretion while another intervention would rarely be studied at that time, it seems to make sense to minimize that disparity. 

Further Advantages

Where it can be applied efficiently, we see multiple advantages in the N-of-1 study design, in addition to the fact that the most beneficial outcome for an individual patient is often elucidated. N-of-1 trials avoid a major difficulty associated with simple randomization in clinical trials of small to medium-size groups: that simple randomization in small to medium size studies often creates imbalance between groups.¹⁴    Additionally, with the order of cross-over interventions randomized in a single patient, inter-person variability is removed. Moreover, where desired, it is possible to aggregate data from N-of-1 trials done with the same protocol to estimate the average effect of an intervention in a population.¹⁵    Additionally, the study design is well suited to the available and widely used technology of today where data can often be collected in the real world, frequently via phone apps or continuously via wearable technology. 

Placebos and N-of-1 Trials

Placebo use in clinical trials may be considered unethical when effective interventions already exist (and are denied to the patient in lieu of placebo) or when not receiving treatment could lead to harm.¹⁶    N-of-1 trials with placebo controls are not immune from these pitfalls, even if the single subject is crossed over to active intervention at least part of the time. As placebo use could still result in phases of the trial where benefits of more proven interventions are withheld or where safety of the subject is threatened by lack of timely intervention, we favor the use of multiple active comparisons in N-of-1 trials, rather than use of placebos.

Enhancing Efficacy of N-of-1 Trials

Efficiency of an N-of-1 trial design may be maximized when its data is analyzed with a Bayesian approach that allows for continuous treatment effect estimation (continuously updating an analysis of treatment effect whenever new data becomes available), which helps detect a clear effect as quickly as possible. In a recent analysis, Defelippe¹⁷     used such an approach in both a simulated N-of-1 trial of a patient with epilepsy and the re-analysis of N-of-1 trials (in patients treated for epilepsy and one treated for headache). The benefits of continuous outcome estimation could be very large. If the statistical technique had been used in an N-of-1 trial of a patient with frontal lobe epilepsy treated with a nicotine patch, for example, the nicotine patch could have been considered definitely effective a month sooner than following the pre-specified time periods of the study. 

Looking at their analyses collectively, Defelippe estimates that length of the trials they examined could have been reduced from 9.5% to 35% prespecified lengths. In addition to easing the burden on the patient, which can be significant in a N-of-1 trial, the potential of continuous outcome estimation may lead to substantial reductions in research costs. Furthermore, Defelippe’s team combines their continuously updated evidence about the probability of an intervention’s effectiveness with the context of what constitutes a minimal clinically important difference. This allows one to see the probability that a treatment is effective in an individual, as soon as possible, in the context of what is clinically relevant. This is in contrast to the common path of needing to do power calculations to guess at pre-determined sample sizes needed to detect significant results in a group. With this approach, the study is complete as soon as the patient has clear benefits. In addition, the Bayesian approach provides uncertainty levels that can be intuitively interpreted by clinicians. 

Should Elements of N-of-1 Trials be More Commonly Used?

Though the N-of-1 trial design is seen as most applicable in stable, chronic conditions where interventions are studied that have short effects on symptoms that can easily be measured many times, we wonder if some elements of the tool might also find application in clinical practice even when observations in chronic conditions are limited. For example, at the beginning of this article we highlighted the challenges clinicians face in deciding who should get a statin (terrain that is again shifting with updated guidelines based on longer term risk).¹⁸ What if in a case where there is lack of clarity as to whether or not a statin should be prescribed, instead of pursuing a typical therapeutic trial (simply trying a diet and/or a statin and then measuring changes in cholesterol vs baseline), the physician and willing patient decided to implement elements of an N-of-1 trial where intervention of Mediterranean Diet plus statins or Mediterranean diet alone were given for eight weeks, with an eight-week washout period between the interventions, and data beyond cholesterol (such systolic blood pressure, eGFR, and BMI) were collected for an assessment of risk at the conclusion of each of the three periods? There would be limited data, of course, and this alone may be enough to draw warranted criticism from experts in the design of N-of-1 trials. But would not the limited data in the outlined approach be more informative than a therapeutic trial of a statin where change in cholesterol is the only outcome? And if, as one possible outcome, the patient and the treatment team see evidence of greater improvement with both interventions combined, they will have learned something valuable to inform the care of the individual patient.

Closing Thoughts

Things change. Perhaps it is time to incorporate aspects of, and where possible complete protocols of, N-of-1 trials into clinical practice. We see this approach as empowering to both patients receiving, and physicians wanting to provide, individualized care. There are still cases where group data will be the best option to drive decision making—we cannot think of how an N-of-1 trial could ethically or practically be done in the treatment of an MI, for example. However, in cases where either data from a group or data from an individual could be used to inform treatment decisions for the individual, data from the individual has the greatest applicability.

Incorporating N-of-1 trials into clinical practice would significantly shift the burden of clinical research from professional research groups to individual clinicians. This can only happen if clinicians are willing and able to spend time with patients exploring treatment options, meaning that for many practices N-of-1 trials would need to be widely covered by insurance. Yet, if a series of office visits in an N-of-1 trial has a high probability of elucidating the best course of treatment, is that really more expensive than patients participating in multiple therapeutic trials, often with multiple providers, in order to try and find effective treatment? With further work, we may find that planned N-of-1 trials, with costs approved by an insurance provider, might lower overall costs in the long-run. Additionally, regulators need to allow that what happens in clinical practice to get to the answer of what is best for a patient is still clinical care, not a form of research generally needing outside regulatory approval. If the long-term implication is that there will be many trials of the smallest size possible that allow both patient and physician to clearly discern the best intervention for a patient through careful observation, this seems a world well worth exploring.

References

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18. Writing Committee Members; Blumenthal RS, Morris PB, Gaudino M, Johnson HM, Anderson TS, Bittner VA, Blankstein R, Brewer LC, Cho L, de Ferranti SD, Gianos E, Gluckman TJ, Gradney KF, Isiadinso I, Lloyd-Jones DM, Marrs JC, Martin SS, McLain KH, Mehta LS, Mora S, Mulugeta WM, Natarajan P, Navar AM, Orringer CE, Polonsky TS, Reynolds HR, Saseen JJ, Shapiro MD, Soffer DE, Tynes SA, Villavaso CD, Virani SS, Wilkins JT. 2026 ACC/AHA/AACVPR/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Dyslipidemia: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2026 Mar 13. doi: 10.1161/CIR.0000000000001423. Epub ahead of print. PMID: 41824552.