Single-Patient (N-of-1) Trials:
 

A Pragmatic Clinical Decision
 

Methodology for Patient-Centered
 

Comparative Effectiveness Research
 

Naihua Duan, Ph.D.
 

Naihua.Duan@Columbia.Edu
 

Division of Biostatistics
 

Departments of Psychiatry
 

Columbia University
 

2013-09-05 SPT VA CIPRS Cyber Seminar 1 
Outline
 

• Overview of Single patient (n-of-1) trials (SPT) 

– Clinical question 

– Basic protocol 

– Indications 

– Pros and cons 

• IRB requirements 

• Design of SPT 

– Blocked randomization or counter-balance 

– Blinding vs open label 

– Physical washout vs. “analytic washout” 

– Standardize or adapt/personalize? 

• Statistical methods: borrow from strength 

• Methodological development needs 

• Explanatory trials vs. pragmatic trials 

• Discussions 

2013-09-05 SPT VA CIPRS Cyber Seminar 2 
Audience Poll #1 

• What is your primary role in the VA?
 

– Student, trainee, or fellow 

– Clinician 

– Investigator/researcher 

– Statistician, IT/data manager 

– Policy-maker/manager/administrator 

– Other 

2013-09-05 SPT VA CIPRS Cyber Seminar 3 
Audience Poll #2
 

• What is your experience with SPT? 

– Have never heard of SPT 

– Have heard of SPT vaguely 

– Have some idea what SPT is about 

– Familiar with SPT protocol 

– Have considered conducting SPT 

– Have participated in SPT practice and/or 

investigations 

2013-09-05 SPT VA CIPRS Cyber Seminar 4 
 

Single Patient (N-of-1) Trials:
 

Clinical Question
 

•	 Patient with chronic condition such as chronic pain
 

•	 Uncertainty Re: the comparative effectiveness of 

treatment options for this specific patient 

–	 Lack of existing research evidence 

– Potential for heterogeneity of treatment effects (Kravitz, 

Duan, Braslow 2004) 

•	 Possible solution: conduct patient-centered 

comparative effectiveness investigation within this 

specific patient to inform his/her clinical decision 

2013-09-05	 SPT VA CIPRS Cyber Seminar 5 
 

Single Patient (N-of-1) Trials:
 

Basic Protocol
 

•	 Within patient multiple cross-over trials 

•	 Assign time intervals (e.g., weeks) to alternate 

treatment options 

•	 Collect outcome measures over time 

•	 Compare outcomes under each treatment option
 

•	 Select treatment option with superior 

performance 

2013-09-05	 SPT VA CIPRS Cyber Seminar 6 
Go to Figure 1 in PDF 
 

2013-09-05 SPT VA CIPRS Cyber Seminar 7 
 

 

Single Patient (N-of-1) Trials:
 

Indications
 

•	 Duan, Kravitz, Schmid (2013), Table 1 

•	 Chronicity and stability 

–	 On-going treatment for chronic conditions 

–	 Stable treatment effects 

•	 Need for personalized knowledge 

–	 Lack of adequate evidence 

–	 Heterogeneity of treatment effects, one size might not fit all
 

•	 Quick effect onset and extinction 

–	 Quick onset of treatment effect 

–	 Negligible carry-over effect 

–	 No irreversible effects 

•	 Examples: fibromyalgia, chronic pain, attention deficit-hyperactivity disorder, 

insomnia, asthma, chemotherapy-associated nausea and vomiting, and allergic 

rhinitis 

2013-09-05	 SPT VA CIPRS Cyber Seminar 8 
 

Single Patient (N-of-1) Trials:
 

Pros and Cons
 

•	 Consistent with routine clinical practice 

–	 Pull, not push 

•	 Potential to improve outcomes for individual 

patients 

–	 Empirical evaluation is warranted 

•	 Infrastructure needs 

– Application of mobile health (mHealth) technology 

•	 Financial mechanism needs 

2013-09-05	 SPT VA CIPRS Cyber Seminar 9 
 

PREEMPT Study
 

•	 Personalized Research for Monitoring Pain 

Treatment (PREEMPT) 

•	 SPT using mHealth in Chronic Pain 

•	 NINR-funded 

•	 Infrastructure development (IT, Statistics) 

•	 RCT to compare patients randomized to SPT 

vs. usual care 

2013-09-05	 SPT VA CIPRS Cyber Seminar 10 
 

 

 

 

 

 

 

 

 

Literature (I)
 

•	 Duan N, Kravitz RL, Schmid CH. Single-Patient (N-of-1) Trials: A 

Pragmatic Clinical Decision Methodology for Patient-Centered 

Comparative Effectiveness Research. Journal of Clinical Epidemiology. 

2013 Aug;66(8 Suppl):S21-8. 

•	 Richard L. Kravitz and Naihua Duan (eds) and the DEcIDE Methods 

Center N-of-1 Guidance Panel (Naihua Duan, Ian Eslick, Nicole B. 

Gabler, Heather C. Kaplan, Richard L. Kravitz, Eric B. Larson, Wilson D. 

Pace, Christopher H. Schmid, Ida Sim, and Sunita Vohra). Design and 

Implementation of N-of-1 Trials: A User’s Guide. AHRQ Publication 

No. 13-EHC122-EF. Rockville, MD: Agency for Healthcare Research 

and Quality; October 2013. Will be available online at 

effectivehealthcare.ahrq.gov. 

2013-09-05	 SPT VA CIPRS Cyber Seminar 11 
 

 

 

 

 

 

 

 

 

Literature (II)
 

•	 Gabler NB, Duan N, Vohra S, Kravitz RL. N-of-1 trials in the medical literature: a 

systematic review. Med Care. 2011 Aug;49(8):761-8. 

•	 Larson EB. N-of-1 trials: A new future? J Gen Intern Med. 2010 Sep;25(9):891-2. 

•	 Zucker DR, Ruthazer R, Schmid CH. Individual (N-of-1) trials can be combined to 

give population comparative treatment effect estimates: methodologic 

considerations. Journal of Clinical Epidemiology, 2010. 63(12): p. 1312-1323. 

•	 Kravitz RL, Paterniti DA, Hay MC, Subramanian S, Dean DE, Weisner T, Vohra S, 

Duan N. Marketing therapeutic precision: Potential facilitators and barriers to 

adoption of n-of-1 trials. Contemp Clin Trials. 2009 Sep;30(5):436-45. 

•	 Kravitz RL, Duan N, Niedzinski EJ, Hay MC, Subramanian SK, Weisner TS. What 

ever happened to N-of-1 trials? Insiders' perspectives and a look to the future. 

Milbank Q. 2008 Dec;86(4):533-55. 

•	 Zucker DR, et al. Lessons learned combining N-of-1 trials to assess fibromyalgia 

therapies. Journal of Rheumatology, 2006. 33(10): p. 2069-2077. 

•	 Zucker DR, et al. Combining single patient (N-of-1) trials to estimate population 

treatment effects and to evaluate individual patient responses to treatment. Journal of Clinical Epidemiology, 1997. 50(4): p. 401-410. 

2013-09-05	 SPT VA CIPRS Cyber Seminar 12 
 

 

Audience Poll #3
 

•	 Are you likely to participate in SPT practice 

and/or investigations in the next ten years? 

–	 Not at all 

–	 Somewhat possible 

–	 Maybe, 50/50 

–	 Likely 

–	 Certainly or almost certainly 

2013-09-05	 SPT VA CIPRS Cyber Seminar 13 
 

IRB Requirements for SPT
 

•	 Human subjects research or quality improvement?
 

•	 Does intention to publish render a quality 

improvement program subject to regulations for 

human subjects research? 

2013-09-05	 SPT VA CIPRS Cyber Seminar 14 
 

 

 

 

 

IRB Requirements for SPT:
 

Does SPT Aim to Produce Generalizable
 

Knowledge?
 

•	 Research means a systematic investigation, including 

research development, testing and evaluation, designed to 

develop or contribute to generalizable knowledge. (45 CFR 

46.102(d)) 

•	 Primary objective for SPT is often to produce specific 

knowledge for individual patient, not to produce 

generalizable knowledge 

– Self-contained population for production and consumption of 

specific knowledge, not to export generalizable knowledge from 

trial population to consumption population 

– Generalizable knowledge might result from SPTs as a by-product, 

but not as the primary objective 

2013-09-05	 SPT VA CIPRS Cyber Seminar 15 
 

 

 

 

 

 

 

 

 

IRB Requirements for SPT:
 

SPT for QI
 

•	 Application of SPT for QI might not be subject to 

regulations for human subjects research 

•	 “Protecting human subjects during research activities is 

critical and has been at the forefront of HHS activities for 

decades. In addition, HHS is committed to taking every 

appropriate opportunity to measure and improve the 

quality of care for patients. These two important goals 

typically do not intersect, since most quality improvement 

efforts are not research subject to the HHS protection of 

human subjects regulations.” 

(http://answers.hhs.gov/ohrp/questions/7281) 

2013-09-05	 SPT VA CIPRS Cyber Seminar 16 
 

 

 

IRB Requirements for SPT:
 

Publications from SPT
 

•	 ‘...the intent to publish is an insufficient criterion for 

determining whether a quality improvement 

activity involves research... Planning to publish an 

account of a quality improvement project does not 

necessarily mean that the project fits the definition 

of research... 

(http://answers.hhs.gov/ohrp/questions/7286) 

2013-09-05	 SPT VA CIPRS Cyber Seminar 17 
 

 

 

 

Design of SPT:
 

Randomization or Counter-Balance
 

•	 Objective: balance between treatment conditions in terms 

of potential confounding factors such as time trend 

–	 AAABBB very bad 

–	 ABABAB not so good 

•	 Blocked randomization vs. simple randomization 

–	 Small block size OK, no concerns about selection bias 

•	 Counter-balance (maybe with restricted randomization) 

might achieve better balance 

– ABBA or BAAB provides better protection against linear time trend 

than ABAB or BABA 

2013-09-05	 SPT VA CIPRS Cyber Seminar 18 
 

 

 

 

 

Design of SPT:
 

Blinding or Open Label?
 

•	 Blinding (when feasible) is often important for 

parallel group trials that aim to produce 

generalizable knowledge for future patients 

– Expectancy among trial participants might not generalize 

to future patients 

•	 Concern might not apply to SPT that aims to inform 

future treatment decision for the patient 

undergoing trial 

– Expectancy might persist from trial period into
 

“consumption period”
 

2013-09-05	 SPT VA CIPRS Cyber Seminar 19 
 

 

 

Design of SPT:
 

Physical Washout or “Analytic Washout”
 

•	 Washout period often inserted between treatment periods 

to eliminate/reduce carryover effect 

•	 Does not address time required for onset of new treatment 

effect 

•	 Might prolong transition between treatment period 

•	 Problematic for comparative effectiveness investigations 

with active treatments 

•	 “Analytic washout” models outcome trajectory, attempting 

to project long term treatment effect, without physical 

washout 

– Requires frequent outcome measurements, say, daily within 

weeklong treatment period 

2013-09-05	 SPT VA CIPRS Cyber Seminar 20 
 

Design of SPT:
 

Standardize or adapt/personalize?
 

– Incorporate user preference? 

– Selection and weighting of outcomes 

– Selection of criteria and format for reporting
 

– CER or PCOR 

2013-09-05 SPT VA CIPRS Cyber Seminar 21 
 

 

 

 

 

 

Statistical Methods:
 

Borrow from Strength
 

•	 Individual’s own SPT data most informative about his/her 

future treatment decisions 

•	 Caveat: individual SPT usually of limited duration, with 

limited precision in estimated treatment effects 

•	 Empirical Bayes methods can be used to “borrow from 

strength”, combining index patient’s own data with 

aggregate data from other patients with similar conditions, 

to provide more precise treatment effect estimates 

–	 Shrinkage estimator 

2013-09-05	 SPT VA CIPRS Cyber Seminar 22 
 

 

Methodological Development Needs 

•	 Analytic strategies to deal with onset and 

carryover effects 

•	 Effective ways to summarize SPT findings for 

end-users (patients and their providers) 

•	 Use of sequential stopping rules 

•	 Use of responsive-adaptive designs with 

skewed randomization to incorporate partial 

information available 

2013-09-05	 SPT VA CIPRS Cyber Seminar 23 
 

 

Explanatory vs. Pragmatic Trials
 

•	 Schwartz D, Lellouch J. Explanatory and pragmatic 

attitudes in therapeutical trials. J Chronic Dis. 1967 

Aug;20(8):637-48. Republished: J Clin Epidemiol. 

2009 May;62(5):499-505. [Example] 

•	 Tunis SR, Stryer DB, Clancy CM. Practical clinical 

trials: increasing the value of clinical research for 

decision making in clinical and health policy. 

JAMA. 2003 Sep 24;290(12):1624-32. 

2013-09-05	 SPT VA CIPRS Cyber Seminar 24 
2013-09-05 SPT VA CIPRS Cyber Seminar 25 
2013-09-05 SPT VA CIPRS Cyber Seminar 26 
2013-09-05 SPT VA CIPRS Cyber Seminar 27 
 

 

Distinct Research Questions
 

Neither Right Or Wrong
 

•	 Explanatory: lab condition, control for contextual 

factors – “everything being equal...” 

–	 Inform development of new treatments 

•	 Pragmatic/practical: naturalistic condition, 

incorporate contextual factors – treatment bundle 

–	 Inform clinical and policy decision-making 

–	 Usual focus for SPT 

•	 Scientific research vs. QI/engineering investigation
 

2013-09-05	 SPT VA CIPRS Cyber Seminar 28 
Discussions
 

Single-patient (n-of-1) trials can be a useful tool for 

treatment decisions for on-going treatment for 

chronic conditions consistent with indications 

discussed in Duan, Kravitz, and Schmid (2013), Table 

1. 

Effectiveness of SPTs in improving long term patient 

outcomes needs to be established empirically in 

studies such as PREEMPT. 

Broad implementation of SPTs requires solution of
 

infrastructure needs and implementation issues.
 

•	 

•	 

• 

2013-09-05	 SPT VA CIPRS Cyber Seminar 29 
 

 

Audience Poll #4
 

•	 Are you likely to participate in SPT practice 

and/or investigations in the next ten years? 

–	 Not at all 

–	 Somewhat possible 

–	 Maybe, 50/50 

–	 Likely 

–	 Certainly or almost certainly 

2013-09-05	 SPT VA CIPRS Cyber Seminar 30 
Thank you!
 

•	 Questions? 

•	 Naihua Duan, Ph.D. 

Naihua.Duan@Columbia.Edu 

2013-09-05	 SPT VA CIPRS Cyber Seminar 31 
 

 

Is There an Attention Bias? (I)
 

• Medication vs. Therapy 

– More intensive contact with clinician among therapy 

patients 

• Explanatory trial 

– Difference in attention intensity considered a
 

confounding factor
 

• There is an attention bias 

– Control/equalize all contextual factors 

• Impose non-therapeutic clinician contact among 

medication patients? 

2013-09-05 SPT VA CIPRS Cyber Seminar 32 
 

Is There an Attention Bias? (II)
 

• Pragmatic trial 

– Difference in attention intensity is natural for the 

way each treatment is delivered 

– Compare treatment bundles, not isolated treatment 

ingredients 

• “Medication + low attention” vs.
 

“Therapy + high attention”
 

– There is no attention bias 

– Incorporate/optimize all contextual factors 

2013-09-05 SPT VA CIPRS Cyber Seminar 33 
 

Figure 1. Scheme for a prototypical SPT 

A B B A B A A 

B 

Trial Periods 

(Balanced or Randomized) 

Baseline Measurements Repeated Outcome Measurements 

Trial Design Feedback for Decision 

Making 

Statistical
 

Analysis 
Journal of Clinical Epidemiology 66 (2013) S21eS28

Single-patient (n-of-1) trials: a pragmatic clinical decision methodology
 

for patient-centered comparative effectiveness research
 

Naihua Duana,

*, Richard L. Kravitzb

, Christopher H. Schmidc
 

a

Division of Biostatistics, Department of Psychiatry, Columbia University, 1051 Riverside Drive, Unit 48, New York, NY 10032, USA
 b

Division of General Medicine, Department of Internal Medicine, University of California, Davis, 4150 V. Street, Suite 2400 PSSB, Sacramento, CA 95817, USA
 c

Center for Evidence-Based Medicine, Department of Biostatistics, School of Public Health, Brown University, 121 S Main Street, Box G-S121-8, Providence,
 

RI 02912, USA
 

Accepted 22 April 2013

Abstract

Objective: To raise awareness among clinicians and epidemiologists that single-patient (n-of-1) trials are potentially useful for informing personalized treatment decisions for patients with chronic conditions.

Study Design and Setting: We reviewed the clinical and statistical literature on methods and applications of single-patient trials and

then critically evaluated the needs for further methodological developments.

Results: Existing literature reports application of 2,154 single-patient trials in 108 studies for diverse clinical conditions; various recent

commentaries advocate for wider application of such trials in clinical decision making. Preliminary evidence from several recent pilot acceptability studies suggests that single-patient trials have the potential for widespread acceptance by patients and clinicians as an effective

modality for increasing the therapeutic precision. Bayesian and adaptive statistical methods hold promise for increasing the informational

yield of single-patient trials while reducing participant burden, but are not widely used. Personalized applications of single-patient trials can

be enhanced through further development and application of methodologies on adaptive trial design, stopping rules, network meta-analysis,

washout methods, and methods for communicating trial findings to patients and clinicians.

Conclusions: Single-patient trials may be poised to emerge as an important part of the methodological armamentarium for comparative

effectiveness research and patient-centered outcomes research. By permitting direct estimation of individual treatment effects, they can facilitate finely graded individualized care, enhance therapeutic precision, improve patient outcomes, and reduce costs. � 2013 Elsevier Inc.

All rights reserved.

Keywords: Adaptive trial; Bayesian method; Borrow from strength; Carryover effect; Crossover trial; Sequential trial; Washout

1. Introduction

A variety of critiques have been raised on the current

paradigm for producing clinical knowledge. The core criticisms are that: (1) it is practically impossible to conduct

standard parallel-group randomized controlled trials

(RCTs) to address all clinically important questions, even

those restricted to comparative effectiveness of drugs and

devices [1,2]; (2) clinical evidence generated in those RCTs

has poor generalizability and therefore limited applicability

to real patients seen in ordinary practices [3,4]; and (3)

treatments shown to be safe and effective on average may

Conflict of interest: The authors have no conflicts of interest related to

the content of this article.

This study was presented at the Fourth DEcIDE Symposium on Comparative Effectiveness Research Methods: From Efficacy to Effectiveness;

June 12e13, 2012; Rockville, MD.

* Corresponding author. Tel.: þ212-543-5589.
 

E-mail address: Naihua.Duan@Columbia.Edu (N. Duan).
 

deliver an uneven mix of risks and benefits to individual patients, a problem known as heterogeneity of treatment effects (HTE; [4e10]). Furthermore, practitioners usually

have little leverage over the choice of research topics or

participation in the generation and interpretation of evidence. This represents an important missed opportunity to

facilitate the development of a learning health care system

‘‘to generate and apply the best evidence for the collaborative health care choices of each patient and provider; to

drive the process of discovery as a natural outgrowth of patient care; and to ensure innovation, quality, safety, and

value in health care’’ [11].

Single-patient trials, also known as n-of-1 trials and

individual-patient trials, have the potential to address these

critiques. Single-patient trials are multiple-period crossover

experiments comparing two or more treatments within individual patients. Unlike parallel-group RCTs, single-patient

trials can be used to estimate individual treatment effects

directly. This allows single-patient trials to identify the best

0895-4356/$ - see front matter � 2013 Elsevier Inc. All rights reserved.

http://dx.doi.org/10.1016/j.jclinepi.2013.04.006
S22	 N. Duan et al. / Journal of Clinical Epidemiology 66 (2013) S21eS28

What is new?

•	 Single-patient (n-of-1) trials have struggled to gain

acceptance among researchers, clinicians, and patients. The comparative effectiveness research

and patient-centered outcomes research movements create an opportunity for such trials to serve

as a clinical decision tool to inform personalized

treatment decisions for patients with chronic

conditions.

•	 Preliminary evidence from several recent pilot acceptability studies suggests that single-patient trials may have broader appeal among patients and

clinicians than previously suspected. Bayesian

and adaptive statistical methods hold promise for

increasing the informational yield of single-patient

trials while reducing participant burden, but are not

widely used.

•	 Personalized applications of single-patient trials

can be enhanced through further development

and application of methodologies on adaptive trial

design, stopping rules, network meta-analysis,

washout methods, and methods for communicating

trial findings to patients and clinicians.

treatment for each individual patient [8,12], thereby serving

as a promising clinical decision tool for individual patients

[13] in the spirit of patient-centered outcomes research

(PCOR).

Gabler et al. [14] reviewed single-patient trials reported

in the medical literature during 1985e2010, and identified

100 articles that reported on 108 studies enrolling a total of

2,154 patients. The studies addressed diverse clinical conditions, including neuropsychiatric (36%), musculoskeletal

(21%), and pulmonary (13%). Examples of conditions to

which single-patient trials have been applied successfully

include fibromyalgia, chronic pain, attention-deficit hyperactivity disorder, insomnia, asthma, chemotherapyassociated nausea and vomiting, and allergic rhinitis.

The strengths and limitations for single-patient trials are

reviewed in Table 1. To summarize, single-patient trials are

suitable for evaluating long-term treatments for chronic conditions, with stable treatment response, quick onset of treatment effect, and modest or negligible carryover effects. The

presence of HTE renders the evidence for individual treatment effects from single-patient trials especially informative

[8]. Chronicity of the condition and stability of the treatment

response provide opportunities for the initial investment in

a single-patient trial to pay off through improvements in

long-term patient outcomes. The lack of existing evidence

creates a need for the evidence produced in single-patient trials. Slow-onset and/or carryover effects can compromise the

validity of single-patient trials. Successful single-patient trials need to either ascertain that slow-onset and/or carryover

effects are absent or negligible, or account for these transient

effects with an appropriate washout period and/or an analytic

strategy to untangle these effects from the true long-term

treatment effect. At the same time, single-patient trials are

notsuitable for conditionsthat are acute or unrelentingly progressive (e.g., acute leukemia, where the clinician and patient

might have only one chance to get it right); treatments causing permanent or only slowly reversible effects (e.g., surgery); and preventive treatments targeting conditions

associated with uncommon, catastrophic outcomes(e.g., prevention of stroke in atrial fibrillation).

This article discusses how single-patient trials address the

core critiques of evidence-based medicine (including its new

guises, comparative effectivenessresearch [CER] and PCOR).

We begin by reviewing the methodology of single-patient trials and their potential acceptability as a mainstream clinical

decision tool. We then discuss applying Bayesian methods

to combine single-patient trials across patients to produce estimates for individual treatment effects that are more stable

than those generated by individual single-patient trials alone

[15e18]. Finally, we discuss the methodological developments that could enhance the utility of single-patient trials.

2.	 Overview of methodology

Single-patient trials are multiple-period crossover trials

conducted within individual patients to evaluate the comparative effectiveness of two or more treatments for each

specific patient. As the methodology for single-patient trials has been discussed in detail elsewhere [13,15e23], we

provide below only a brief overview. New directions for

single-patient trial methodology are discussed in the

‘‘Methodological developments’’ section below.

We focus on the comparison of two treatments in the rest

of this article; the extension to more than two treatments is

relatively straightforward. Other types of single-subject designs, such as multiple baseline designs commonly used in

social sciences research, differ in key features from singlepatient trials and are not included in this discussion.

The unit of treatment assignment is a prespecified time

period, say 1 week, during which the patient receives either

treatment A or B. The duration of the treatment period is

selected to allow each treatment an adequate time to manifest its effect. A washout period might be used between the

two treatment periods to eliminate or reduce the carryover

effect of the treatment used in the previous time period.

Treatment assignment is usually randomized and blocked

to ensure good balancewith respect to possible period effects.

For example, within each block of two time periods, both

treatments are used in one time period, so that the treatment

assignments are randomized to either AB or BA in each

block. The number of crossovers is usually prespecified, although some variation is often allowed to accommodate
Table 1. Indications and contraindications for single-patient trials (SPTs)

Feature Description Indication Co

Heterogeneity of treatment Treatment effect varies across patients; With HTE, evidence based on specific patient is Homogeneity of treatment 

effects (HTE, [8]) one size does not fit all essential to personalize treatment decisions for reduction of blood gl

(e.g., serotonin reuptake inhibitors for

treatment of depression)

Chronicity Long-term treatment for chronic Chronicity allows knowledge gleaned from Acute conditions (e.g., infl

condition single-patient trials to inform future treatment One-time treatment with lo

Stability Stable treatment effecta

decisions (e.g., gastroesophageal reflux disease)

Stability ensures that knowledge gleaned from Lack of stability (e.g., in a

single-patient trials informs future treatment vitamin K fluctuates widdecisions may be unstable relative 

Effect onset and carryover Transition periods between two Negligible or modest duration for onset and Long duration of onset and

treatment periods may be needed carryover (e.g., short-acting psychostimulants medications)

for the effect of previous treatment for ADHD) allows single-patient trials to

to extinguish, and the effect of provide valid knowledge about long-term

new treatment to commence and treatment effect, especially when accompanied

stabilize. Insufficient length of with appropriate washout or analytic strategies

either might confound estimation to untangle slow onset and carryover effects

of long-term treatment effect from long-term treatment effect

Lack of adequate evidence Existing clinical evidence not Lack of adequate evidence creates the need Adequate evidence: there i

adequate to inform treatment for evidence to be gleaned from single-patient single-patient trials (e.g.

decision for individual patients trials (e.g., effectiveness of prophylactic reductase inhibitors [sta

antibiotics in spinal cord injury patients with risk in individuals with e

frequent urinary tract infections)

Abbreviation: ADHD, attention-deficit hyperactivity disorder.

a The assumption of stable treatment effect is weaker than the assumption of stable treatment outcome under both treatments. With the assumption of s

treatment outcome to manifest a time trend, say, a gradual deterioration over time, as long as the trajectories are parallel for the two treatments, so that 

remains constant (stable). In other words, this assumption amounts to a requirement that treatment effect and time trend are additive, that is, there is n
S24 N. Duan et al. / Journal of Clinical Epidemiology 66 (2013) S21eS28

adverse events, in ways similar to the variations allowed in

standard, parallel-group RCTs.

Clinical outcome is assessed repeatedly over time, at

least once within each treatment period. The outcomes obtained during time periods with treatment A are compared

with those obtained during treatment B to determine which

treatment leads to preferred outcomes for the patient.

The analysis of single-patient trials includes informal

evaluations such as visual inspection, simple statistical analysis such as the paired t-test, time series analysis to account

for possible serial correlation, and Bayesian methods

[13e18,20]. Among the studies reviewed by Gabler et al.

[14], a quarter used visual inspection but no statistical comparison; among those that used statistical comparison, 59%

used the t-test, 30% used nonparametric methods, 22% used

regression modeling, 32% used pooled analysis, and only 7%

used Bayesian methods (some used multiple methods).

On completion of the single-patient trial and the analysis, the patient and clinician meet to discuss the findings

and decide on the treatment going forward. This decision

must be based on a sound interpretation of the data obtained during the trial, including a careful assessment of

the uncertainty involved that avoids overinterpreting effects

that are driven by random noise in the data.

Single-patient trials primarily seek to produce specific

clinical knowledge for each individual patient to inform

the decision about which treatment to use in the long term.

It is also possible to combine data across individual patients

using Bayesian methods, to improve the clinical decision

for individual patients beyond what can be accomplished

using each patient’s own data alone, and as a byproduct

also to produce aggregate information that can be used to

inform treatment decisions for other patients not participating in the trials [15e18]. Further discussions of Bayesian

methods are given in the ‘‘Bayesian methods’’ section.

3. Acceptability

Several recent commentaries have advocated wider application of single-patient trials as a clinical decision tool

[24e28]. Successful implementation, however, depends on

the receptivity of stakeholders who control adoption of this

methodology, especially clinicians and patients. We review

in the following paragraphs several recent pilot acceptability

studies that provide encouraging insights into the appeal of

single-patient trials to these stakeholders.

Brookes et al. [29] conducted a pilot acceptability study

among nine patients with osteoarthritis of the knee participating in two series of single-patient trials. One series

(n 5 5) compared a standard knee support with a heatretaining support; the other series (n 5 4) compared a

nonsteroidal anti-inflammatory drug, diclofenac, with an

analgesic, paracetamol. Patients were randomized to three

pairs of crossovers, double blinded for the drug trials, and

open label for the knee support trials (cannot blind). Daily

diaries were taken to inform future treatment decisions.

Qualitative, semistructured interviews were conducted with

each patient at the start of the single-patient trial and again

at the completion/termination to solicit patients’ perspectives on trial participation, understanding of the protocol,

initial expectations, and experience of participation. The

authors concluded that the single-patient trial was an acceptable approach to individualize treatment decisions. Participants viewed the single-patient trial as a logical and

accurate method that provided a fair opportunity for them

to experience both treatments. They valued the personalized nature of the trial and its potential to offer improved

treatment.

Kravitz et al. [30] conducted a pilot acceptability study

among clinicians and patients to solicit potential facilitators

and barriersto the adoption ofsingle-patient trials. The study

conducted phone interviews with 21 physicians (9 internal

medicine, 5 family medicine, 4 rheumatology, and 3 pediatrics). They conducted focus groups,stratified by age, with 32

adult patients and parents of pediatric patients with at least

one chronic condition. Despite initial lack of familiarity with

the concept ofthe single-patient trial, both physicians and patients readily ‘‘grasped the fundamental logic and appreciated the potential benefits’’ of personalizing treatment

decisions and enhancing doctorepatient relationship.

Nikles et al. [31] interviewed 12 Australian stakeholders

recruited using purposive sampling through organizations

considered likely to have an interest in single-patient trials

to provide an optimally broad range of respondent categories

(consumers, doctors, government, and industry). Stakeholders supported wider implementation of single-patient

trials in a targeted fashion, with some caveats. They recognized the rationale behind single-patient trials to increase

cost-effectiveness of government spending, to improve community health outcomes, and to maximize the effectiveness

of individual treatment. They also recognized the potential

benefits of single-patient trials, including targeting of therapy, reducing unnecessary prescribing, and reducing health

care costs. Barriers recognized included constraints on doctors’time, doctors’ acceptance, drug companies’ acceptance,

patient willingness, and cost. The authors identified several

strategies for overcoming these barriers, namely (1) building

single-patient trials into standard clinical consultation, (2)

social marketing, (3) incentives such as payment for nurse

practitioner support, (4) ensuring that the trials are suitable

for real-world conditions and free for patients, (5) restricting

the duration of the trials, and (6) obtaining funding for an independent organization to conduct the trials.

4. Bayesian methods

In most practical applications of single-patient trials, the

number of crossovers that can be conducted is limited by resources and patient burden. Therefore, the comparative effectiveness estimated from the patient’s own single-patient
N. Duan et al. / Journal of Clinical Epidemiology 66 (2013) S21eS28 S25

trial data is often oflimited statistical precision.It istherefore

desirable to combine the index patient’s own data with the

data obtained from other patients who participated in similar

single-patient trials to enhance the statistical precision available for the index patient. Bayesian methods, sometimes

known as borrowing from strength [32], are useful for this

purpose [15e18]. We present in the following paragraphs

an overview of the Bayesian methods. Readers who are not

interested in statistical details are welcome to skip to the

‘‘Methodological developments’’ section.

For a blocked design with two time periods in each block,

the i-th patient’s individual treatment effect (di) can be estimated within each block asthe difference in the observed outcome between the two treatments being compared. The

block-specific individual treatment effect estimates can then

be averaged across blocks within the i-th patient to provide

a combined estimate for di. The Bayesian model usually assumes di to be drawn randomly from a normal distribution

with mean d0 denoting the average treatment effect (ATE)

for the population, and standard deviation t denoting the variation in the individual treatment effects across patients (the

HTE). The Bayesian framework requires placing prior distributions on these parameters that represent knowledge about

these parameters before the study, say, from clinical knowledge or previousstudies. When prior information is not available, noninformative prior distributions would be used. The

ATE, d0, and the individual treatment effects, di’s, are then estimated using the posterior distribution for each parameter,

usually using the posterior mean or median as the point estimate and the credibility interval based on the posterior distribution as the interval estimate.

The relationship between individual treatment effects and

the ATE depends on the balance between the between-patient

and within-patient variances [33]. When between-patient

variance is small compared with the within-patient variance

(i.e., little or no HTE), the individual treatment effects are

very similar and close to the ATE. Alternatively, if betweenpatient variance is large compared with the within-patient

variance (i.e., strong HTE), the individual treatment effects

would be estimated to be close to the patient’s observed treatment effect estimate with little or no borrowing from

strength. In this situation, the strength (population information) to be borrowed from does not provide strong statistical

information; therefore, the within-patient information dominates the between-patient information.

As a byproduct of the Bayesian method that aims to enhance clinical decisionsfor individual patients, the estimated

ATE and HTE can also be used to inform treatment decisions

for similar patients who did not participate in single-patient

trials.

Some research studies can be designed either as a standard

parallel-group RCT, or as an ensemble of single-patient trials. The parallel group design has more logistical simplicity,

with no need to manage the crossovers and washout. On the

other hand, single-patient trials might deliver greater power/

precision than parallel group designs with the same number

of patients, under the assumption that the carryover effect

is either negligible or controlled for appropriately. With

single-patient trials, the same patient serves as his/her own

control; therefore, the idiosyncrasies unique to each patient

are controlled for automatically. With parallel group designs,

these idiosyncrasies contribute additional uncertainty, resulting in lower power/precision.

Related discussions on the precision for single-patient

trials and traditional crossover trials are given in section

2.2.5 and Table 3 in the study of Zucker et al. [18]. They

show that for a study with M patients and N paired-time periods, study precision is M/(t2 þ 2 s2

/N ), thus providing

a way to calculate the tradeoff in sample size between the

patients and time periods.

5. Methodological developments

As the single-patient trial receives wider attention in the

CER/PCOR framework, further developments in its methodology, as discussed in the following paragraphs, can enhance the utility of single-patient trials in clinical practice.

Although awashout period between treatment periodsis often used to guard against carryover effects, this strategy has

limitations. Patients and clinicians might be dissatisfied with

the withholding of active treatments during the washout, especially for comparative effectiveness single-patient trials with

an active control. The lack of active treatments might also result in suboptimal patient outcomes during the single-patient

trial. Furthermore, washout does not mitigate slow onset of

the new treatment. Rather, the onset is deferred until after

the end of the washout period, stretching the duration of the

transient effects between the two treatment periods.Therefore,

when designing a single-patient trial, there is a need to balance

the benefits of washout(such asmitigating the potential bias of

estimated treatment effects) against its limitations.

Hogben and Sim [5,6] used an innovative methodology

to address the carryover effect without a washout period,

taking daily measurements in each 3-day time period, but

downweighting the earlier measures that are more susceptible to carryover effect. Zucker et al. [17] used a similar approach by analyzing only a single measurement at the end

of each treatment period. Further analytic strategies can be

used to model outcome trajectories during each treatment

period, to untangle long-term treatment effects from transient effects owing to carryover of prior treatment and/or

slow onset of new treatment. The ability for analytic strategies to deal with both carryover and slow onset is an important advantage over the usual strategy of a washout

period. Further development and evaluation of these analytic methods are warranted.

Further investigation is warranted on effective ways to

summarize findingsfrom each single-patient trial for presentation to the patient and clinician. As there is diversity in the

design and analysis for single-patient trials, there is also diversity in the decision process, in particular, how the findings
S26 N. Duan et al. / Journal of Clinical Epidemiology 66 (2013) S21eS28

from the trial are presented. A variety of methods (such as visual inspection) used in the existing single-patient trial literature might be ineffective and vulnerable to overinterpreting

the data when the observed treatment effect might be driven

largely by random noise in the data.

As a patient-centered methodology, single-patient trials

should prioritize incorporating user (patient and clinician)

preferences, both in the design and analysis of the trial,

and in the decision phase. A key consideration is the quantitative sophistication of the users. Some users might prefer

to base their treatment decisions on the estimated treatment

effect size and associated confidence interval for each outcome of interest. Others might prefer to know the posterior

probability or odds for how the two treatments compare in

terms of achieving prespecified goals, such as reducing the

level of pain to a specific level, which takes uncertainty into

consideration. However, such advanced feedback that recognizes the level of uncertainty in the information may

be too difficult for many to grasp. Graphical presentations

that show the trajectories of treatment responses over time

for the two treatments are probably informative and comprehensible to all. Some patients may just prefer the pictures and may rely on their clinician to interpret the data.

Thus, the single-patient trial needs to train patient and clinician to optimize the decision-making process while accommodating user preferences and maintaining scientific

rigor.

Mostsingle-patient trials are focused on prespecified treatments, such as two specific drugs to be compared. Although

this highly structured design facilitates the implementation

and interpretation of the trials (such as Bayesian methods for

combining trial data across patients), a more flexible frameworkmight be desirable in clinical applications, allowing each

patient and their provider to choose the specific treatments of

particular interest to them. With a flexible framework, it is

more challenging to combine data across patients for the sake

of borrowing from strength. A possible remedy is the use of

networkmeta-analysis[34], to integrate direct(using trialsthat

compared the specific treatments,say, Avs.B, directly) and indirect comparisons (using trials that compared A vs. B indirectly, say, through trials that compared A vs. C and trials

that compared C vs. B), to maximize information available

for the specific comparison of interest.

Essentially all single-patient trials are designed with

a fixed 1:1 randomization ratio with a prespecified number

of time periods. Alternatively, a response-adaptive design

such as the play-the-winner design [35,36] could be used

to allow the randomization ratio to adapt to the interim data

obtained, so as to minimize patient exposure to the inferior

treatment. The application of response-adaptive designs to

single-patient trials calls for new methodological development in the multilevel framework (time periods nested

within patients) to incorporate interim data from the index

patient themselves and additional data from other similar

patients. In applying a play-the-winner design, trial experience from other similar patients can be informative, but

interim data from the index patient themselves should be

weighted more heavily as being more directly relevant.

The common practice of prespecifying the number of

crossovers might not always accommodate the needs of

individual patients [20]. A more flexible strategy not prespecifying the number of crossovers can help accommodate the HTE across patients, for example, a sequential

stopping rule [37] can be used to terminate the singlepatient trial when a prespecified level of acceptable

uncertainty has been reached. For patients who manifest

dramatic differences between the two treatments, early

stopping is indicated. For example, in a study by Guyatt

et al. [19], a decision was made to terminate the trial after

four time periods (two pairs of crossovers) because the

unblinded data already convinced the patient and the clinician that one of the treatments was superior. Some patients might prefer more flexibility to allow for early

termination when appropriate. Some might prefer the

simplicity of a standard design with a fixed number of

crossovers. Some patients’ responses might fluctuate substantially, suggesting the need for more crossovers. Some

patients might generate more consistent results and could

benefit from early termination. Further research on adaptive trial design [38] and sequential stopping rules [37] for

application to single-patient trials may facilitate individualization of the single-patient trial to enhance its scientific

validity and user acceptability.

6. Summary and conclusions

After several decades of wandering in the wilderness

[12], single-patient trials may be poised to emerge as an important part of the CER and PCOR methodological armamentarium. These trials render a number of benefits in

clinical care. By permitting direct estimation of individual

treatment effects, they can facilitate finely graded individualized care, enhance therapeutic precision, improve patient

outcomes, and avoid unnecessary costs. New applications

of Bayesian, adaptive, and sequential statistical methods

hold promise for increasing the informational yield of singlepatient trials while reducing participant burden.

Single-patient trials can deliver benefits that extend beyond patients participating in the trials. The aggregation

of trial findings through Bayesian methods can inform

treatment decisions for patients unaffiliated with the trials.

In addition, by linking scientific methods to chronic disease

management, single-patient trials encourage clinicians and

patients to actively participate in real clinical learning communities. Supported by the right system-level infrastructure, clinicians can use single-patient trials to generate

data that are not only scientifically valid but also immediately available and directly relevant to their patients. In this

way, clinicians can obtain early, actionable feedback on

their practices. It is therefore conceivable that single-patient

trials might serve as a vehicle to facilitate the
N. Duan et al. / Journal of Clinical Epidemiology 66 (2013) S21eS28	 S27

transformation of clinical organizations into learning health

care systems [11].

The way forward is not free of obstacles. Participation in

single-patient trials requires time and effort, and although

some patients and clinicians will be enthusiastic to participate, others will not be. The application of single-patient

trials is restricted to chronic and symptomatic conditions

with stable treatment outcomes. There is an ongoing tension among precision (longer trials are better), convenience

(shorter are better), and clinical applicability (when a trial

is too long, the results of earlier periods may no longer apply if the patient’s condition has evolved).

Preliminary evidence from several recent pilot acceptability studies suggests that single-patient trials may be

widely acceptable to patients and clinicians as an effective

modality for increasing therapeutic precision. Because

single-patient trials are akin to informal therapeutic trialand-error procedures commonly used in clinical practice,

clinicians may be amenable to upgrade their practice to incorporate this formal scientific procedure if the necessary

infrastructure support is available. Even if just a small proportion of patients participate, the absolute number of eligible patients with chronic conditions who could benefit might

be very large, making way for a prominent role for singlepatient trials in clinical research and practice. If singlepatient trials can succeed in luring only a fraction of eligible

clinicians and patients into the scientific enterprise, the opportunity for transforming care through creation of highfunctioning knowledge organizations is immense.

Acknowledgments

N.D.’s work on this article was supported in part by National Institutes of Health (NIH) Grant 7 P30 MH090322.

R.L.K.’s work on this article was supported in part by NIH

Grants 5 R01 MH079387, 5 R01 CA140419, 1 R01

NR013938, and UL1TR000002. The authorsthank Ms. Kristen Greenlee for assistance with text processing.

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