Define n 1 trial

The success of an n-of-1 trial largely depends on the collaboration and commitment of both clinician and patient. Clinicians must explain the process to their patients, collaborate with them in developing outcome measures most appropriate to the individual, monitor patients at regular intervals throughout the trial, evaluate and explain what the results of the trial mean, and work with patients to determine the course of treatment based on trial findings. Patients participating in n-of-1 trials must be involved in selecting therapies for evaluation, recording processes and outcomes including nonadherence to treatment protocols , and sharing in treatment decisionmaking.

As the centerpiece of patient-centered care, patient engagement has been shown to improve health outcomes among patients with chronic illness.

With appropriate infrastructure support, n-of-1 trials can be used by individual practicing clinicians in their daily care of individual patients. While the naturalistic application of n-of-1 trials may involve a single patient-clinician pair, over time there may emerge a multitude of such pairs. At the same time, research studies may use n-of-1 trials to examine decision support, quality improvement, and implementation of improved clinical and organizational procedures.

As a research study design, n-of-1 trials are uniquely capable of informing clinical decisions for individual patients. Therefore, the research goal to produce generalizable knowledge that can be applied to future patients is compatible with the clinical goal of serving the needs of the individual patients participating in these trials. The same is often not true for other research study designs, such as the usual parallel group randomized controlled trials [RCTs], in which patients contribute to the research but usually do not benefit directly in terms of their own clinical decisionmaking.

This special feature of n-of-1 trials may facilitate the recruitment and retention of patients and clinicians in research studies. Beyond their potential for promoting patient-centered care, n-of-1 trials may have additional pragmatic value. With escalating drug costs, health care systems are struggling to provide cost-effective therapies.

N-of-1 trials offer an objective way of determining individual response to therapy: if two therapeutic options are shown to have equivalent effectiveness in a given individual, the less costly option could be chosen. This approach to comparative effectiveness could apply to different classes of medications, as well as formal assessment of the bioequivalence of generic and proprietary pharmaceuticals. Considering that n-of-1 trials are particularly suited to chronic conditions, the savings to the health care system could be substantial.

N-of-1 trials are indicated whenever there is substantial uncertainty regarding the comparative effectiveness of treatments being considered for an individual patient.

Uncertainty can result from a general lack of evidence as when no relevant parallel group RCTs have been conducted , when the existing evidence is in conflict, or when the evidence is of questionable relevance to the patient at hand. N-of-1 trials are applicable to chronic, stable, or slowly progressive conditions that are either symptomatic or for which a valid biomarker has been identified.

Acute conditions offer no opportunity for multiple crossovers. Rapidly progressive conditions or those prone to sudden, catastrophic outcomes such as stroke or death are not amenable to the deliberate experimentation of n-of-1 trials. Asymptomatic conditions make outcomes assessment difficult, unless a valid biomarker exists. Some patient groups e. For practical reasons, treatments to be assessed in n-of-1 trials should have relatively rapid onset and washout i.

Treatments with a very slow onset of action e. The major design elements of n-of-1 trials are balanced sequence assignment, blinding, and systematic outcomes measurement. Before introducing these elements, we offer a description of standard clinical practice. In ordinary practice, the clinician prescribes treatment and asks that the patient return for followup. At the followup encounter, the clinician asks the patient if he or she is improving. If the patient responds positively, the treatment is continued.

If not, the clinician and patient discuss alternative strategies such as a dose increase, switching to a different treatment, or augmenting with a second treatment. This process continues until both agree that a satisfactory outcome has been achieved, until intolerable side effects occur, or until no further progress seems possible. Although treatments are administered in sequence, there is no systematic repetition of prior treatments replication , and the treatment assignment sequence is based on physician and patient discretion not randomized or balanced.

Neither clinician nor patient is blinded. Typically, there is no systematic assessment of outcomes. As a result, it is easy for both patient and clinician to be misled about the true effects of a particular therapy. Take for example Mr. J, who presents to Dr. Alveolus with a nagging dry cough of 2 months duration that is worse at night.

After ruling out drug effects and infection, Dr. Alveolus posits perennial vasomotor rhinitis with postnasal drip as the cause of Mr. Alveolus increases the diphenhydramine dose to 50 mg, but the patient retreats to the lower dose after 3 days because of intolerable morning drowsiness with the higher dose.

He returns complaining of the same symptoms 2 weeks later; the doctor prescribes cetirizine 10 mg a nonsedating antihistamine. Alveolus asks. While this typical clinical scenario involves some effort to learn from experience, the approach is rather haphazard and can be improved upon. What if Mr. J and Dr. Alveolus were to acknowledge their uncertainty and elect to embark on an n-of-1 trial of diphenhydramine versus cetirizine for treatment of chronic cough presumed due to perennial rhinitis?

They might agree:. Their design schematized in Figure 1—1, modified from Zucker et al. In parallel group RCTs, randomization serves to maximize the likelihood of equivalence between treatment groups in terms of both known and unknown prognostic factors. In n-of-1 trials, the aim is to achieve balance in the assignment of treatments over time so that treatment effect estimates are unbiased by time-dependent confounders.

Randomization of treatment periods is one way of achieving such balance, but there are others. For example, Mr. J might have decided to take diphenhydramine on weekends and cetirizine on weekdays. He might then be less prone to notice daytime sleepiness from diphenhydramine because he tends to sleep in on weekends. This would bias his assessment. Randomization along with blinding makes it more difficult to guess which treatment has been assigned. For a patient interested in selecting the treatment likely to work best for him or her in the long term, the simplest n-of-1 trial design is exposure to one treatment followed by the other AB or BA.

This simple design allows for direct comparison of treatments A and B and protects against several forms of systematic error e.

In this way, repetition is to n-of-1 trials what sample size is to parallel group RCTs. The importance of a washout period separating active treatment periods in n-of-1 trials has been fiercely debated. Carryover effects resulting from insufficient washout will often tend to reduce observed differences between treatments for placebo- controlled trials.

However, more complex interactions are possible. For example, if the benefits of a particular treatment wash out quickly but the risks of adverse treatment-related harm persist think aspirin, which reduces pain over a matter of hours but increases risk of bleeding for up to 7 days , the likelihood of detecting net benefit will depend on the order in which the treatments are administered.

Similar issues also apply to slow onset of the new treatment. A possible downside of a washout period is that the patient is forced to spend some time completely off treatment, which might be undesirable for patients who already receive some benefit from both treatments.

For practical purposes, washout periods may not be necessary when treatment effects e. 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.

Further discussion is offered in Chapter 4 Statistics. Some n-of-1 investigators have advocated for the use of run-in periods. In parallel group RCTs, a run-in period is a specified period of time after enrollment and prior to randomization that is allotted to further measure a participant's eligibility and commitment to a study.

In parallel group RCTs, blinding of patients, clinicians, and outcomes assessors "triple blinding" is considered good research practice.

These trials aim to generate generalizable knowledge about the effects of treatment in a population. In drug and device trials, the consensus is that it is critical to separate the biological activity of the treatment from nonspecific placebo effects.

For a broader view, see Benedetti et al. Patients and clinicians participating in n-of-1 trials are likely interested in the net benefits of treatment overall , including both specific and nonspecific effects.

Therefore blinding may be less critical in this context. Nevertheless, expert opinion tends to favor blinding in n-of-1 trials whenever feasible. However, just as in parallel group randomized trials, blinding is not always feasible.

For example, in trials of behavioral interventions e. Furthermore, 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.

Evidence is accumulating that careful, systematic monitoring of clinical progress supports better treatment planning and leads to better outcomes.

There are two issues to consider: 1 what data to collect and 2 how to collect them. In designing an n-of-1 trial, participants patients, clinicians, investigators must first select outcome domains specific symptoms, specific dimensions of health status, etc.

In so doing, they must balance a number of competing interests. For most chronic conditions, there are numerous potentially relevant outcomes.

These may be condition specific e. Clinicians, patients, and service administrators may assign different priorities to different domains. For example, in chronic musculoskeletal pain, the patient may prioritize control of pain intensity or fatigue, the clinician may prioritize daily functioning, and Drug Enforcement Agency officials may prioritize minimizing opportunities for misuse of opiates.

The primary purpose of most n-of-1 trials is to assist with individual treatment decisions. Therefore patient preferences are paramount. However, as prescribers of treatment, clinicians are essential partners, and their buy-in is essential. 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.

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. 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. 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.

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.

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 ].

Examples of individual and combined n-of-1 studies investigating the utility of an intervention in pain and discomfort related to a disease. Examples of individual and combined n-of-1 studies investigating the utility of an intervention in the treatment of a disease.

In light of issues surrounding the feasibility of specific types of clinical trial, there exists medical care settings, such as palliative care, that defy the successful completion of RCTs owing to substantial methodological barriers. Recruiting and retaining subjects along with maintaining distinct interventions are challenged by patient variation related to disease burden, complex needs and changing symptomology.

RCTs in palliative care fail because of the inability to recruit and retain sufficient numbers of subjects to achieve necessary sample size requirements [ 34 ]. As a result of the paucity of RCTs with relevant subset analyses, RCTs as a whole have failed to inform drug selection for an individual patient requiring palliative care.

Until this occurs, patients may suffer through extensive periods of suboptimal treatment [ 35 ]. N-of-1 trials have thus been proposed as an alternative method of gathering evidence to inform palliative care decision-making [ 27 ].

In addition, if there are many interventions that contribute to an apparent state of clinical equipoise, then leveraging insights into how individuals within populations might be stratified on the basis of genetic or clinical risk profile information from large-scale trials could lead to the study of a subset of all possible interventions in an n-of-1 trial involving a patient with a specific genetic or clinical risk profile.

As useful as n-of-1 trials are in many situations, they may not be possible or ideal for certain conditions owing to the nature of the symptoms and pathologies associated with a given condition, the clinical stability of the condition, as well as the clinical assessments necessary for conducting a trial.

This has been shown to be problematic in standard crossover trials as well [ 38 ]. An example is infectious conditions that progress or regress relatively rapidly. In this context, chronic conditions for which there are easily measurable clinical end points and where the drugs or interventions that are to be tested have a relatively short half-life are the most amenable to n-of-1 trials [ 39 ]. The design of n-of-1 trials is rooted in standard techniques and strategies used in the design of population-based clinical trials with a few caveats.

For example, simple crossover designs in which the order of the administration of two compounds, one perhaps being a placebo, is randomized across different subjects enrolled in n-of-1 studies have often been used. Thus, an ABAB design would involve a four-period crossover design [ 24 , 25 ]. The number and length of the crossover periods would be dictated by the nature of the outcome and interventions as well as the statistical power associated with the chosen number of observations or data collection points within each period given the likely differential effect of the interventions.

In addition, it is quite possible that for any n-of-1 design, not enough evidence favoring one intervention over another might occur. If both interventions did not achieve some reasonable target then the interventions might be seen as equally ineffective.

If they both achieve a target but equally well, then either intervention might be appropriate for future use. Obviously, increasing the length or sophistication of the trial may help resolve issues of ambiguity like this. There is a trade-off, as with any trial design; patient retention is jeopardized with a longer trial. The simple ABAB design raises at least four related design questions. First, should one randomize the sequence in which interventions are administered to a single patient such that they may not be alternating?

An argument for the use of randomized sequencing, as opposed to simply randomizing the intervention labeled A and B, could be made if the intention was to pursue many n-of-1 trials and then assess the results via combined or meta-analysis see later where order effects of the treatments might be of interest. A second question concerns the carryover effects of the interventions. Many drugs and behavioral interventions may linger in the system or influence the behavioral patterns and psyche of the patient once their administration is stopped, thereby influencing future interventions.

Such effects may confound the interpretation of the effectiveness of subsequent interventions. Sequence randomization and meta-analyses may help identify and assess such effects, but to really combat them in any one study, it is important to ensure that the treatment periods are sufficiently long and that statistical methods that appropriately accommodate or consider carryover effects are used to analyze the data.

A third question is directly related to the first two and it concerns the use of washout periods between administrations of interventions. Washout periods can be used to combat carryover effects, but their use may compromise patient safety since they may result in taking a patient off all treatments during the course of the trial although such an approach is no different in orientation from large trial randomization to a placebo arm, or to the use of washout periods in a population-based trial.

The fourth question concerns the use of blinding, baseline periods and placebo controls. As with the use of washout periods, the establishment of a baseline and the use of placebos may compromise the patient if they are completely taken off treatments. The use of blinding is arguably essential for the success of such trials and should involve blinding of the patient as well as the evaluating physicians and clinical monitoring team.

The wavy dark and light lines reflect the SBP levels for individuals 1 and 2, respectively, during the trial. The design included a baseline period followed by four alternating periods in which two drugs, A and B, were administered with a washout period between drug administrations.

Individual 2 had better blood pressure control on drug B. A study by Yelland et al. A comparison of two treatments for osteoarthritsis, celecoxib and paracetamol, were assessed. The design of the trial was based on a double-blind, crossover comparison where a subject took either celecoxib or sustained-release paracetamol for three pairs of 2-week periods. The order of the drugs during each pairing was random. Both patients and physicians did not know the order of the drug regimens until after the study was completed and data comparing treatment response were pursued.

Analyzing data from n-of-1 trials has parallels to the analysis of traditional population-based crossover design clinical trials, again with a few caveats. The most obvious caveat has to do with the fact that the assessment of the effects of interindividual variation e. Another relates to the likelihood that more intensive data collection would be associated with n-of-1 trials rather than population-based trials. Thus, the large number of observations collected on a patient in an n-of-1 trial suggests that data analysis methods more in line with time-series analysis, which assume many observations rather than methods, such as simple repeated measures of analysis of variance and related techniques, designed for a relatively few observations, are appropriate.

The actual statistical methods that have been used in the analysis of n-of-1 trials range from visual inspection techniques for making clinical decisions [ 41 , 42 ] to sophisticated time-series analyses [ 42 , 43 ].

However, two very important phenomena need to be accommodated in the analysis of n-of-1 trial data, as mentioned previously. The first is serial correlation between the measures. Since the data are to be collected on a single individual with probable short intervals between the data collections, the observations collected at adjacent or near time points will exhibit strong correlations. These correlations need to be accommodated in relevant analyses.

For example, it has been shown that the use of standard t-tests comparing quantitative responses to two particular interventions collected over time in a crossover-based n-of-1 trial will lead to erroneous inferences owing to dependencies between the observations [ 44 ]. Therefore, methods that account for serial correlation in comparing the response to two or more treatments, such as certain time-series analyses, are necessary.

Even if washout periods are included in a study, it is quite likely that the influence of a prior intervention on the end points of interest will linger into the time during which a different intervention is employed. Accounting for carryover effects is not trivial as their lengths may vary from intervention to intervention and at different times in the study. More research into how to identify and accommodate carryover effects in n-of-1 trials is clearly needed. Therefore, monitoring and reporting methods should be as invisible and labor-free to the patient as possible.

Remote clinical phenotyping and wireless devices have enormous potential in this light [ 45 ]. In fact, there have been many innovations in wireless health monitoring that could be of great value in the implementation of n-of-1 clinical trials.

Table 3 provides a few examples. However, it is important to note that not all clinical conditions may be amenable to n-of-1 trials with wireless devices or at least current monitoring devices.

Examples of remote phenotypic monitoring devices for potential use in n-of-1 clinical trials. For further discussion of remote phenotypic monitoring devices please see [ 45 ]. Many of the available wireless health monitoring devices have not themselves been shown to be reliable in clinical settings, hence making their immediate use in n-of-1 clinical trials that focus on the data they produce premature. Despite this limitation, there is a great affinity between these devices and n-of-1 trials, and some may be ready for use.

For example, cell-phone-based mood, activity and pain-level diaries, although not completely invisible to a patient, could be used to assess the efficacy of antidepressants, anxiolytics, analgesics and other palliative interventions.

Cell phone diaries could also be used to record mild side effects of interventions, as well as compliance with an intervention, and hence complement symptom monitoring. Other simple monitoring devices that could be of immediate use in certain n-of-1 trial settings are actigraphs or movement monitors [ 46 ].

Activity monitors have become very sophisticated and could be used as adjuncts to other monitoring devices in an n-of-1 trial in which such monitoring may only be secondary to a primary set of measures. The activity level information could provide insight into compliance, secondary effects of the intervention, an important covariate or confounding factor, or an additional end point relevant to the intervention. For example, the quantity of rare cell types found in the blood, such as circulating endothelial cells and circulating tumor cells, and the expression levels of particular genes in these cells, may be indicative of the effectiveness of a treatment in eradicating pathologies or signs of pathologies [ 51 , 52 ].

If multiple n-of-1 trials investigating the same sets of interventions are initiated, then it is possible to pursue joint or meta-analytic studies of the data generated from those trials Table 2. Such analyses can explore trends in the data that may shed light on the characteristics of patients found to respond to one particular intervention, side-effect profiles, and overt carryover effects and other confounders that could be accommodated in future trials.

A number of statistical approaches to the combined or meta-analysis of multiple n-of-1 trials have been proposed for this purpose [ 36 , 37 ].

A recent paper by Zucker et al. Of the possible motivations for combining the results of n-of-1 trials, two stand out. The first involves the assessment of the utility of n-of-1 trials in improving healthcare. Guyatt et al. Larson et al. The authors ultimately concluded that an n-of-1 trial service is feasible, the trial costs were comparable to other conventional services and clinicians appeared to gain confidence and precision from them [ 54 ].

Finally, Mahon et al. Interestingly, they found n-of-1 trials led to less theophylline use without adverse effects on exercise capacity or quality of life in patients with irreversible chronic airflow limitation [ 55 ].

The authors concluded that there was clinically important bias towards unnecessary treatment during open prescription of theophylline for irreversible chronic airflow limitation that can be mitigated through the use of objective criteria associated with n-of-1 trial designs.

Of note, in the context of combining n-of-1 trials in order to assess their utility and feasibility, is the experience of Nikles et al. This service was designed for patients with attention deficit and hyperactivity disorder for which individual variations in intervention responses are common. Essentially, patients were referred to the service by a physician, a trial was initiated after referral, data analyzed and reports were sent back to the prescribing physician.

Two additional studies have examined the feasibility of n-of-1 trials from a cost perspective [ 57 , 58 ]. Both studies observed, as one might expect, that the operational costs of n-of-1 trials are not trivial relative to standard care and when high-cost interventions are used to contrast with other interventions, knowingly putting a patient on the intervention for prespecified periods without a favorable response is problematic from a care perspective.

However, this criticism is true of all clinical trials, and ways of mitigating this problem via adaptive and sequential designs, for example, have been proposed [ 59 ]. The second important motivation for combining n-of-1 trial data and results concerns the identification of common characteristics among patients who are ultimately found to respond best to a particular intervention. For example, it might be that patients who are found to respond best to a certain intervention share genotypic, biomarker, clinical or demographic characteristics.

Knowledge of these characteristics would help inform a physician as to the use of a particular intervention for future patients without having to resort to an n-of-1 trial. Obviously, the degree to which these characteristics are reliably predictive of response is incredibly important in this context.

The notion that one could analyze the results of multiple n-of-1 trials to search for patterns associated with response to an intervention contrasts with the approaches to individualized medicine that leverage the results from large population-based trials for this purpose. In the traditional approach, a large-scale population-based trial is pursued and individuals are identified that ultimately responded to an intervention.

Some characteristic e. This characteristic is then used to inform use of the intervention in the future. This approach essentially casts a wide net initially by studying a large number of patients in a unified manner, then winnows things down to what might work best in an individual patient over time and through additional studies of the subjects in the large trial. The combined n-of-1 trials approach achieves the same goal: a number of n-of-1 trials are pursued and the best interventions for each patient are recorded.

Characteristics of the patients are noted and contrasted in order to identify distinguishing features among those who did best on a specific intervention. If such a characteristic is found, it is used to inform the use of that intervention in the future.

This approach essentially starts out in a small and focused manner, and then works its way towards insights that would immediately benefit a much larger group of patients. See examples below for using aggregated N of 1 trials. N of 1 clinical trials could involve some complicated statistical analyses. See the discussions below:. N of 1 clinical trial design is rarely discussed in statistical conferences, perhaps because of the perception that not too much statistics is involved in the analysis of N of 1 study data.

One of the key questions is that the N of 1 study design is only applicable in certain situations — it depends on the disease characteristics, treatment short washout period , endpoint quick measurements. Posted by Web blog from Dr.