N-of-1 or single subject clinical trials consider an individual patient as the sole unit of observation in a study investigating the efficacy or side-effect profiles of different interventions. The ultimate goal of an n-of-1 trial is to determine the optimal or best intervention for an individual patient using objective data-driven criteria. Such trials can leverage study design and statistical techniques associated with standard population-based clinical trials, including randomization, washout and crossover periods, as well as placebo controls. Despite their obvious appeal and wide use in educational settings, n-of-1 trials have been used sparingly in medical and general clinical settings. We briefly review the history, motivation and design of n-of-1 trials and emphasize the great utility of modern wireless medical monitoring devices in their execution. We ultimately argue that n-of-1 trials demand serious attention among the health research and clinical care communities given the contemporary focus on individualized medicine.
Keywords: clinical equipoise, early-phase trials, individualized medicine, n-of-1, remote phenotyping, single patient trial, treatment repositioning, wireless health
There is a growing acceptance that the development of medical interventions that work ubiquitously (or under most circumstances) for the majority of common chronic conditions is exceptionally difficult and all too often has proved to be fruitless [1,2]. This recognition has led to the notion that the clinical practice of medicine should acknowledge and embrace the unique characteristics of individual patients, particularly at the genetic level, and seek to individualize patient care [3–5]. In addition, there has been a great deal of emphasis on obtaining and evaluating objective criteria for claims that certain interventions work better than others. For example, initiatives to facilitate and promote ‘evidence-based’ medicine [6,7] and ‘comparative effectiveness’ research [8] have been proposed by many government and research agencies. In fact, these beliefs are so strong that legislation to promote research and practices aimed at personalizing medicine has been introduced in at least the USA [9], and the US NIH has initiated large-scale programs to facilitate comparative effectiveness research. Such initiatives have even become a rallying cry for reinvigorating the troubled US healthcare system [10–13].
The interest in evidence-based, as well as individualized medicine, has led to some very notable discoveries. For example, for individualized medicine, genetic data have been exploited to identify therapies appropriate for an individual and has led to changes in drug oversight policy and the way certain drugs have been labeled. For example, many cancer therapeutic responses have been demonstrated to be influenced by very specific tumor genetic profiles, which has led to the obvious notion that before one administers the relevant compounds, a patient’s tumor should be screened for the presence of specific genetic profiles [14]. In fact, the drug cetuximab (Erbitux®), used to treat colorectal cancer, is rendered ineffective in the presence of a specific mutation in the KRAS protein in the tumor [15]. In response, the US FDA relabeled the drug to indicate a need for genetic profiling before administering the drug. There are many other instances in which connections between the presence of genetic variations and noncancer drug effectiveness or side-effect profiles have been made that have led to FDA relabeling, such as warfarin, carbamazepine and clopidogrel [16,17]. At present, approximately 10% of labels for FDA-approved drugs contain pharmacogenomic information. In addition, the FDA is actively involved in creating a streamlined review approach to diagnostic companion tests with therapeutics where n-of-1 trials could play a role in facilitating the approval process [18].
As compelling as these studies and consequent drug administration policy changes are, they do not necessarily indicate a shift towards true individualized medicine since they only reflect attempts to fractionate or stratify the larger population into smaller groups likely and not likely to benefit from specific treatments [19]. Hence, they do not involve a true consideration of all the nuances and characteristics individual patients may have that would dictate – or be most compatible with – therapies tailored specifically to those patient characteristics. Obviously, as more insights or connections between various factors and drug responses are revealed, the more likely clinical care can be specifically directed to the unique combinations of factors that define an individual patient’s clinical presentation. Until that time, however, for many clinical conditions, a physician is faced with the dilemma of true ‘clinical equipoise’ in which the best course of therapy is unknown a priori simply because connections between individual patient characteristics, such as genetic profile, and likely response to particular therapeutic agents, have not been identified. Many physicians recognize that the practice of medicine is individualized medicine but not in a systematic manner across every patient, physician and health institution. N-of-1 trials, which focus on the objective determination of the optimal therapy for a single individual, can possibly improve outcomes by preserving some homogeneity while stratifying care among patients.
An intuitive way around this dilemma is to treat the individual patient as a study subject and objectively and empirically determine the best course of therapy. Such single subject or ‘n-of-1’ trials have great precedent in educational and behavioral settings, but have not been used to an appreciable degree within the medical and clinical communities; in fact, many such trials have been disparaged as ‘only anecdotal’ [20]. There are many reasons for this, not the least of which is cost, but n-of-1 studies are a promising way to advance individualized medicine and a method for gaining insights into comparative treatment effectiveness among a wide variety of patients. We review the design and conduct of n-of-1 studies and suggest that modern remote wireless medical devices may play a big role in their execution in the future. We also consider some of the drawbacks of such studies as well as areas for future research.
Do n-of-1 clinical trials have a role in clinical science?
Randomized controlled trials (RCTs) are considered the sine qua non of applied biomedical research. The objective evaluation of the benefits and problems associated with novel clinical interventions by directly comparing them with standard or sham (placebo) interventions allows claims to be made about the ultimate effectiveness and utility of those interventions. Although the amount of evidence one might need in order to motivate the pursuit of a clinical intervention in the absence of a clinical trial is arguable, the basic motivation and scientific foundation behind clinical trials are not in doubt, and few would argue that the positive results of a well-designed clinical trial could ever hurt the case for implementing or pursuing an intervention. The appropriateness of different designs for clinical trials, however, is highly debatable and a rich area of biostatistical research. For example, the appropriateness of certain kinds of adaptive designs, which minimize the amount of time a subject is on an inferior intervention, sequential designs that seek to reach a conclusion about an intervention prior to a fixed, prespecified lengthy data collection process, crossover designs that allow subjects to act as their own controls, and other strategies all come with challenges that need to be considered when vetting or testing particular interventions, especially for rare diseases and unique situations [21–23].
One issue that has been of immense historical and clinical importance in the design and conduct of clinical trials involves the generalizability of the results, especially if they suggest a novel intervention has utility. Addressing this issue is important because it obviously impacts on wider use, dissemination and marketing of an intervention after the completion of a successful clinical trial. Ensuring that a trial’s design and subject enrollment facilitates applicability of the results is not trivial given the tremendous heterogeneity of diseased populations. In this light, n-of-1 trials that focus exclusively on the objective, empirically determined optimal intervention for a single patient or subject clearly defy easy generalizability, but are compatible with the ultimate end point of clinical practice – the care of individual patients. In addition, clinical studies focusing on the treatment of single patients is, as noted previously, actually more consistent with the vision of individualized or personalized medicine than stratifying patients into groups more or less likely to benefit from a specific treatment on the basis of population-level association studies [24,25]. Finally, as discussed later, n-of-1 trials could be very efficient and less costly vehicles for motivating serious consideration about an intervention with respect to other patients, larger patient groups, or other clinical conditions.
N-of-1 trials have been pursued routinely in education and learning settings [26], often in behavioral and psychological assessment settings but, with the exception of studies of pain medications (Table 1) [27], rarely in medical settings (Table 2). The reasons for this are unclear but may have to do with the physician’s ability to effectively monitor relevant clinical end points easily and remotely, as well as the costs and time involved in both patient and physician conducting n-of-1 trials [28,29]. Although modern wireless health-monitoring devices may help overcome these problems, as discussed later. The ultimate benefits of n-of-1 trials may derive from the reality that interventions of whatever type rarely work in everyone. If comparable interventions have differing effects across groups of patients defined by certain characteristics, then it is highly likely that these interventions will show variation in efficacy between individuals, even within specific strata, as long as those strata are defined appropriately [30–32]. N-of-1 trials explore this variability in an objective way while simultaneously leading to an informed decision about the best way to treat an individual patient using his or her own data. Furthermore, with the rising cost of patient care (including drug costs and clinic visits), it is desirable to minimize clinic visits and patient time on a suboptimal treatment. Therefore, although outcomes must be shown on a case-by-case basis, it is possible that efficient n-of-1 trials will be comparatively more effective at identifying and minimizing the time on suboptimal interventions than standard care [33].
