CONTEXT: When feasible, randomized, blinded single-patient (n-of-1) trials are uniquely capable of establishing the best treatment in an individual patient. Despite early enthusiasm, by the turn of the twenty-first century, few academic centers were conducting n-of-1 trials on a regular basis. METHODS: The authors reviewed the literature and conducted in-depth telephone interviews with leaders in the n-of-1 trial movement. FINDINGS: N-of-1 trials can improve care by increasing therapeutic precision. However, they have not been widely adopted, in part because physicians do not sufficiently value the reduction in uncertainty they yield weighed against the inconvenience they impose. Limited evidence suggests that patients may be receptive to n-of-1 trials once they understand the benefits. CONCLUSIONS: N-of-1 trials offer a unique opportunity to individualize clinical care and enrich clinical research. While ongoing changes in drug discovery, manufacture, and marketing may ultimately spur pharmaceutical makers and health care payers to support n-of-1 trials, at present the most promising resuscitation strategy is stripping n-of-1 trials to their essentials and marketing them directly to patients. In order to optimize statistical inference from these trials, empirical Bayes methods can be used to combine individual patient data with aggregate data from comparable patients.
CONTEXT: When feasible, randomized, blinded single-patient (n-of-1) trials are uniquely capable of establishing the best treatment in an individual patient. Despite early enthusiasm, by the turn of the twenty-first century, few academic centers were conducting n-of-1 trials on a regular basis. METHODS: The authors reviewed the literature and conducted in-depth telephone interviews with leaders in the n-of-1 trial movement. FINDINGS: N-of-1 trials can improve care by increasing therapeutic precision. However, they have not been widely adopted, in part because physicians do not sufficiently value the reduction in uncertainty they yield weighed against the inconvenience they impose. Limited evidence suggests that patients may be receptive to n-of-1 trials once they understand the benefits. CONCLUSIONS: N-of-1 trials offer a unique opportunity to individualize clinical care and enrich clinical research. While ongoing changes in drug discovery, manufacture, and marketing may ultimately spur pharmaceutical makers and health care payers to support n-of-1 trials, at present the most promising resuscitation strategy is stripping n-of-1 trials to their essentials and marketing them directly to patients. In order to optimize statistical inference from these trials, empirical Bayes methods can be used to combine individual patient data with aggregate data from comparable patients.
Authors: Richard L Kravitz; Christopher H Schmid; Maria Marois; Barth Wilsey; Deborah Ward; Ron D Hays; Naihua Duan; Youdan Wang; Scott MacDonald; Anthony Jerant; Joseph L Servadio; David Haddad; Ida Sim Journal: JAMA Intern Med Date: 2018-10-01 Impact factor: 21.873
Authors: Paul A Scuffham; Jane Nikles; Geoffrey K Mitchell; Michael J Yelland; Norma Vine; Christopher J Poulos; Peter I Pillans; Guy Bashford; Chris del Mar; Philip J Schluter; Paul Glasziou Journal: J Gen Intern Med Date: 2010-04-13 Impact factor: 5.128
Authors: Joyce P Samuel; Jon E Tyson; Charles Green; Cynthia S Bell; Claudia Pedroza; Don Molony; Joshua Samuels Journal: Pediatrics Date: 2019-03-06 Impact factor: 7.124
Authors: Ian M Kronish; Ying Kuen Cheung; Daichi Shimbo; Jacob Julian; Benjamin Gallagher; Faith Parsons; Karina W Davidson Journal: J Gen Intern Med Date: 2019-03-11 Impact factor: 5.128
Authors: Santosh Kumar; Wendy J Nilsen; Amy Abernethy; Audie Atienza; Kevin Patrick; Misha Pavel; William T Riley; Albert Shar; Bonnie Spring; Donna Spruijt-Metz; Donald Hedeker; Vasant Honavar; Richard Kravitz; R Craig Lefebvre; David C Mohr; Susan A Murphy; Charlene Quinn; Vladimir Shusterman; Dallas Swendeman Journal: Am J Prev Med Date: 2013-08 Impact factor: 5.043