N-of-1 Trials: Conceptual Underpinnings, Clinical Applicability, and Methodological Considerations

The primary goal of evidence-based medicine is to go beyond average effects observed in the general population and determine treatment strategies tailored to each individual's clinical condition and biological characteristics. While traditional randomized controlled trials (RCTs) with parallel groups draw general conclusions from large sample groups, N-of-1 trials aim to reveal the impact of clinical heterogeneity and individual differences, providing in-depth insights for personalized treatment decisions. This article comprehensively examines the methodological framework, statistical foundations, clinical advantages, and integration strategies of N-of-1 trials.

This article is based on insights from this book. You can access a more in-depth read here.

A. Definition and Conceptual Framework of N-of-1 Trials

N-of-1 trials are specialized clinical studies designed to evaluate treatment effectiveness in a single patient. These trials follow a crossover design, often randomized and blinded, making them a valuable tool in personalized medicine. They belong to the broader category of Single Case Designs, which are widely used in psychology, education, and social sciences. By systematically alternating between treatments, N-of-1 trials allow for a more precise assessment of how an intervention affects an individual.

Key Features of N-of-1 Trials:

• Personalized Approach: Conducted on a single patient, tailoring the study to individual needs.

• Crossover Design: Involves switching between an active treatment and a placebo or between different treatments.

• Randomization & Blinding: Often includes random assignment and blinding to reduce bias.

• Withdrawal-Reversal Method: Uses structured treatment alternation to analyze effectiveness more accurately.

• Application in Medicine: Helps optimize treatment plans by assessing how well a therapy works for a specific patient.

B. Historical Process and Evolution

N-of-1 studies were first introduced to clinicians by Hogben and Sim in 1953, but the movement gained real momentum in the 1980s under the leadership of Gordon Guyatt. Many pioneers established N-of-1 trial units in academic centers, but these were often abandoned when funding ran out. Nevertheless, some units remain active, and in the last 30 years, more than 2,000 patients have participated in such studies, with over 90% preferring treatments aligned with the trial results.

Unlike parallel-group studies, N-of-1 trials use the crossover method to address the issue of interaction between the patient and the treatment. This allows for more precise measurements in cases where individual characteristics affect whether one treatment is superior to another and helps control for treatment effects over time.

C. The Importance of N-of-1 Trials in the Era of Patient-Centered Care

N-of-1 trials are personalized clinical research studies based on collaboration between the individual patient and the physician. Their success depends on the physician explaining the process to the patient, establishing appropriate outcome measures, regularly monitoring the patient, evaluating the results, and shaping the treatment process together with the patient. Meanwhile, the patient is responsible for selecting treatment options, recording outcomes, and actively participating in decision-making.

These individualized trials enhance patient engagement in chronic diseases, leading to better health outcomes. Patients who complete N-of-1 trials report a better understanding of their health conditions and greater control over treatment decisions.

Beyond supporting patient-centered care, this method also has the potential to increase cost-effectiveness in healthcare. By comparing individual efficacy across different treatment options, more cost-effective therapeutic alternatives can be identified. Its application, especially in chronic diseases, could result in significant savings within the healthcare system.

Furthermore, N-of-1 trials facilitate learning from individual patient experiences, improving clinical decision-making and contributing to medical research. Unlike other study designs, they both prioritize individual patient benefits and contribute to the generation of generalizable scientific knowledge.

D. Applicability, Limitations, and Contraindications of N-of-1 Trials

N-of-1 trials are recommended when there is significant uncertainty about which treatment is more effective for an individual patient. This uncertainty may arise due to a lack of sufficient scientific evidence, conflicting results from existing studies, or low applicability of general findings to the specific patient. Additionally, N-of-1 trials can be useful when heterogeneous treatment effects (HTE) are observed among patients.

These trials are typically suitable for chronic, stable, or slowly progressing diseases. Acute illnesses, rapidly progressing conditions, or situations with a risk of sudden death (e.g., stroke or heart attack) are not appropriate for this method. For asymptomatic diseases, N-of-1 trials can only be conducted if a reliable biomarker is available (e.g., blood pressure, LDL cholesterol, sedimentation rate).

From a practical perspective, treatments considered for N-of-1 trials should have a rapid onset of action and reversible effects. Treatments that take a long time to show efficacy or remain in the body for extended periods may test the patience of both patients and physicians. Additionally, complex treatment protocols requiring frequent dose adjustments are not well-suited for N-of-1 trials.

E. Key Design Elements of N-of-1 Trials

E.1. Standard Clinical Practice and Its Shortcomings

In traditional clinical practice, a doctor prescribes a treatment and asks the patient to return after a certain period. If the patient reports improvement, the treatment continues; if not, dosage adjustments, alternative medications, or additional treatments are considered. However, this process is not systematic:

• Treatments are not assigned randomly or in a balanced sequence,

• There is no repetition of previous treatments as a control,

• Blinding is not applied,

• Outcomes are not assessed regularly and objectively.

This inconsistent approach can lead both doctors and patients to misinterpret treatment effects.

E.2. Differences and Advantages of N-of-1 Trials

Compared to standard clinical practice, N-of-1 trials offer a more systematic and objective approach. For example, consider a patient with a persistent chronic cough. A doctor may prescribe two different antihistamines (diphenhydramine and cetirizine). In a standard approach, the patient may never be certain which medication is truly more effective over time.

However, if an N-of-1 trial is conducted:

• The two medications are administered in a balanced sequence for a predetermined duration (e.g., 7-day periods),

• Blinding is ensured, meaning the medications are provided in identical capsules,

• The patient’s symptoms (e.g., cough severity on a 1-5 scale) are systematically recorded,

• Side effects (e.g., drowsiness) are also monitored, allowing the patient and doctor to make a joint decision.

This method helps determine whether a treatment is genuinely effective and assists in selecting the best-individualized option. N-of-1 trials enable both the patient and the physician to follow a data-driven decision-making process.

F. Fundamental Design Principles in N-of-1 Trials

N-of-1 trials provide a systematic approach to identifying the best treatment option at an individual patient level. To ensure the success of these trials, the following key design principles should be considered:

Figure 1. Scheme for a prototypical n-of-1 trial

F.1. Balanced Sequence Assignment

In conventional parallel-group RCTs, randomization aims to achieve a balanced distribution among patient groups. In N-of-1 trials, the primary goal is to prevent treatment effects from being influenced by time-dependent confounders.

For instance, if the treatment sequence is assigned as AAAABBBB, natural variations during the treatment period (e.g., seasonal effects, spontaneous improvement, or worsening of the disease) can affect the results. Instead, designs such as:

• ABABABAB or ABBAABBA provides a more balanced distribution across treatment periods,

• ABBABAAB and similar "bidirectionally balanced" designs offer better protection against both linear and non-linear time trends.

These strategies make it more difficult for the patient or external factors to predict the treatment sequence, leading to more reliable results.

Example Scenario:

A patient may prefer to take diphenhydramine (a sedating antihistamine) on weekends when they sleep more. However, this preference could create an unconscious bias, as their sleep patterns may mask the drug's effect. If the medication is assigned in a randomized sequence, both the patient and doctor can evaluate the treatment effect more accurately.

F.2. Replication and Statistical Power

In an N-of-1 trial, the patient must be exposed to both treatments. The simplest design follows an AB or BA sequence, where one treatment (A) is given first, followed by the other (B). However, single comparisons fail to account for:

• Natural symptom fluctuations (e.g., spontaneous improvement or worsening of the disease),

• External influences like stress, diet, or exercise,

• The statistical power is gained through repeated measurements.

Therefore, repeated designs such as ABAB, ABBA, or ABBAABBA are preferred. Repetitions enhance reliability in N-of-1 trials, functioning similarly to increasing sample size in parallel-group RCTs.

Example Scenario:

If a patient takes Drug A for one week and Drug B the next, short-term external factors (e.g., experiencing a stressful work environment during one of those weeks) could distort the results. However, by repeating the cycle multiple times, the influence of short-term variables is balanced out, making the true treatment difference clearer.

F.3. Washout and Run-In Periods

The effects of a treatment may persist even after administration has stopped. Therefore, when testing two different treatments sequentially, it is essential to ensure that the effects of the previous treatment have completely worn off.

• Washout Period: The period required for the effects of the first treatment to subside. If the second treatment starts too soon, residual effects from the first treatment could interfere with the results (carryover effect).

• Run-In Period: A phase where the patient is exposed to a specific treatment for a set duration to assess their responsiveness. This phase can help identify "responders" and "non-responders."

However, a washout period is not always necessary:

• If the drug has a short half-life, a washout period may not be needed.

• For long-acting medications (e.g., corticosteroids), an adequate washout period should be implemented to prevent carryover effects.

Example Scenario:

Aspirin provides pain relief within hours but increases bleeding risk for up to seven days. If a patient switches to another medication without a washout period, the increased bleeding risk may be mistakenly attributed to the new drug rather than the lingering effects of aspirin.

F.4. Blinding and Bias Prevention

In parallel-group RCTs, it is a fundamental principle to ensure that patients, doctors, and outcome assessors (triple blinding) are unaware of the treatment being administered. This helps prevent placebo effects and cognitive biases.

In N-of-1 trials, however, the primary goal is to determine the most suitable treatment for an individual, so blinding is not always mandatory. Nevertheless, whenever possible, blinding should be preferred because:

• A patient's expectations can influence the outcomes,

• Doctors may consciously or unconsciously favor the treatment they believe to be more effective,

• It helps distinguish the true drug effect from the placebo effect.

If medications are prepared in identical-looking capsules and assigned in a randomized sequence, the patient remains unaware of which drug they are receiving, allowing for a more objective evaluation.

Example Scenario:

A patient may perceive a particular medication as more effective simply due to their belief in its efficacy (placebo effect). If the medications are provided in similar-looking capsules, the patient will not know which one they are taking, leading to a more accurate measurement of the actual effect.

However, blinding is not always feasible:

• In behavioral therapies (e.g., cognitive therapy methods), the patient will inherently know which intervention they are receiving, making blinding impossible.

• In many community pharmacies, it may not be practical to prepare medications in specially designed capsules.

G. Systematic Outcome Assessment

Systematic patient monitoring is one of the most crucial design elements in N-of-1 trials. Research has shown that regular and meticulous health tracking leads to better treatment planning and improved patient outcomes. For instance, home blood pressure monitoring can enhance hypertension management, and targeted treatment approaches based on PHQ-9 scores have yielded successful results in depression treatment.

Systematic outcome assessment involves two fundamental questions:

• What data should be collected?

• How should this data be collected?

G.1. What Data Should Be Collected?

In N-of-1 trials, patients, doctors, and researchers must first determine which health indicators will be monitored. These indicators can include both disease-specific measures and general health metrics.

Disease-specific measures:

• Chronic back pain → Pain severity

• Inflammatory bowel disease → Frequency of diarrhea

• Depression → Mood fluctuations

General health indicators:

• Quality of life

• Daily functionality

• Fatigue level

Different stakeholders may have varying priorities:

• Patient → May prioritize minimizing pain or fatigue.

• Doctor → May focus on improving daily functionality.

• Health authorities → May aim to reduce medication misuse.

However, since N-of-1 trials are patient-centered, the patient's preferences should take precedence. Nonetheless, the clinical expertise of doctors plays a critical role, and their input is necessary for treatment decisions.

• Once measurement criteria are selected, the appropriate scales should be determined.

• Previously developed, validated, and reliable scales are preferred.

• However, in some cases, standard scales may not fully capture a patient’s specific condition.

• In such instances, customized but scientifically less-established new scales may be used.

• MYMOP (Measure Your Medical Outcome Profile) is an example of a scale designed for personalized measurement.

G.2. How Should Data Be Collected?

Traditional methods:

 ✔ Surveys 

✔ Diaries (diary tracking) 

✔ Medical records 

✔ Healthcare data

With technological advancements, new data collection methods are increasingly being utilized:

• Real-time data entry via mobile devices (Ecological Momentary Assessment - EMA)

  • Allows patients to record symptoms daily, hourly, or weekly.

  • Provides higher patient adherence compared to paper diaries.

• Activity tracking using motion sensors and GPS

  • Actigraphy (movement tracking): Automatically measures the patient’s physical activity.

  • Location tracking (GPS): Analyzes how environmental factors affect patient behavior.

• Physiological measurements using biometric devices

  • Heart rate, blood pressure, and blood glucose levels can be directly transmitted to mobile devices.

  • Additional data such as sweating (galvanic skin response), EEG activity, and voice tone changes can provide insights into stress levels and mood.

Although evidence regarding the reliability and validity of these emerging technologies is still evolving, increasing research supports their effective use in N-of-1 trials.

H. Statistical Analysis and Personalized Treatment Decisions in N-of-1 Trials

The data collected in N-of-1 trials should be presented in a format that is understandable and actionable for both patients and doctors. Studies show that some trials use t-tests or simple statistical methods, while others rely on graphical comparisons to evaluate results.

H.1. Presentation of Data: Separate or Composite?

• Results can be presented individually (e.g., stair-climbing ability, nighttime sleep quality, and emergency visits for an asthma patient), or

• As a composite measure (e.g., an Asthma Improvement Index).

Separate presentation preserves clinical details but can be complex.Composite measures simplify decision-making but may obscure important details.

H.2. Graphs vs. Statistical Analysis

• Graphical analysis provides a quick understanding of results but can sometimes be misleading.

• Statistical analysis determines whether the results are due to chance.

• The best approach is to use both statistical tests and graphical methods together.

H.3. Using Additional Data for Decision-Making

• If a large number of similar N-of-1 trials exist, data can be pooled for more reliable conclusions.

• However, if data is limited or if the patient has a unique condition, only the results of the specific trial should be considered.

These approaches help patients and doctors determine the best-personalized treatment option.

I. Opportunities and Challenges in N-of-1 Trials

N-of-1 trials not only enhance individualized treatment precision but also offer three key advantages:

I.1. Identifying Ineffective Treatments

• Can help reduce unnecessary medication use (polypharmacy).

• Minimizes side effects, improving patient safety.

• Encourages the selection of more cost-effective alternatives.

I.2. Increasing Patient Involvement in Treatment

• Patients become more actively engaged in monitoring their health outcomes.

• Encourages patients to develop a scientific mindset and critically assess treatments.

• Studies show that increased patient engagement leads to better health outcomes.

I.3. Bridging the Gap Between Clinical Research and Everyday Practice

• Treatment efficacy can be better assessed using real-world patient data.

• Makes clinical research more patient-focused and applicable.

• Contributes to continuous learning in healthcare systems.

I.4. Challenges:

• N-of-1 trials must be beneficial and feasible for patients, doctors, and healthcare institutions.

• Ethics committees must recognize these trials as an extension of standard clinical care.

• User-friendly statistical tools should be developed to simplify analysis processes.

• Health informatics systems should support the integration of N-of-1 trials into clinical practice.

• Doctors and patients need adequate training and support to use this method effectively.

Addressing these opportunities and challenges can lead to the widespread adoption of N-of-1 trials in healthcare systems.

J. Conclusion

N-of-1 trials serve as a cornerstone of personalized medicine, providing a robust methodology to understand and optimize individual patient responses to treatment. Compared to traditional randomized controlled trials, they offer more precise and meaningful insights at the individual patient level, thereby enhancing clinical decision-making processes.

This approach not only strengthens patient-centered care but also has the potential to improve cost-effectiveness in healthcare and prevent unnecessary medication use. N-of-1 trials are particularly effective in managing chronic and variable-response diseases, encouraging patients to take a more informed and active role in their treatment journey. However, overcoming ethical, methodological, and practical challenges is essential for broader implementation.

In the future, the integration of N-of-1 trials into clinical practice will be further advanced through health information systems, mobile technologies, and sophisticated statistical analysis methods. To facilitate this, increasing education and awareness among both clinicians and patients is crucial, along with incorporating this methodology into clinical decision support systems. As a result, N-of-1 trials have the potential to become one of the most significant transformations in modern medicine, empowering individualized treatment decisions.

References

• Nikles, J., & Mitchell, G. (Eds.). (2015). The essential guide to N-of-1 trials in health (No. 11566). Dordrecht: Springer Netherlands.