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J Clin Epidemiol. 2018 Oct;102:12-22. doi: 10.1016/j.jclinepi.2018.05.020. Epub 2018 May 30.

Patient preferences for personalized (N-of-1) trials: a conjoint analysis.

Author information

1
Columbia University Medical Center, New York, NY, USA. Electronic address: nm2562@cumc.columbia.edu.
2
RTI International, Research Triangle Park, NC, USA.
3
Columbia University Medical Center, New York, NY, USA.
4
Ipsos Group, Mahwah, NJ, USA.
5
Columbia University School of Social Work, New York, NY, USA.
6
Department of Psychiatry, Columbia University College of Physicians & Surgeons and NY State Psychiatric Institute, New York, NY, USA.
7
Columbia University School of Nursing and NewYork Presbyterian Hospital, New York, NY, USA.
8
Columbia University, Irving Institute for Clinical and Translational Science and Hunter College, New York, NY, USA.
9
Department of Internal Medicine and UC Center Sacramento, Sacramento, CA, USA.
10
Patient Stakeholder.
11
RTI International, University of Pittsburgh and University of North Carolina, Chapel Hill, NC, USA.
12
Department of Psychology, University of Colorado Denver, Denver, CO, USA.
13
Department of Medicine, Columbia University, New York, NY, USA; Department of Epidemiology, Columbia University, New York, NY, USA.

Abstract

OBJECTIVE:

Despite their promise for increasing treatment precision, Personalized Trials (i.e., N-of-1 trials) have not been widely adopted. We aimed to ascertain patient preferences for Personalized Trials.

STUDY DESIGN AND SETTING:

We recruited 501 adults with ≥2 common chronic conditions from Harris Poll Online. We used Sawtooth Software to generate 45 plausible Personalized Trial designs comprising combinations of eight key attributes (treatment selection, treatment type, clinician involvement, blinding, time commitment, self-monitoring frequency, duration, and cost) at different levels. Conditional logistic regression was used to assess relative importance of different attributes using a random utility maximization model.

RESULTS:

Overall, participants preferred Personalized Trials with no costs vs. $100 cost (utility difference 1.52 [standard error 0.07], P < 0.001) and with less vs. more time commitment/day (0.16 [0.07], P < 0.015) but did not hold preferences for the other six attributes. In subgroup analyses, participants ≥65 years, white, and with income ≤$50,000 were more averse to costs than their counterparts (P all <0.05).

CONCLUSION:

To optimize dissemination, Personalized Trial designers should seek to minimize out-of-pocket costs and time burden of self-monitoring. They should also consider adaptive designs that can accommodate subgroup differences in design preferences.

KEYWORDS:

Conjoint analysis; Discrete choice; Heterogeneity of treatment effects; Multi-morbidity; N-of-1 trials; Patient-centered care

PMID:
29859242
PMCID:
PMC6119511
[Available on 2019-10-01]
DOI:
10.1016/j.jclinepi.2018.05.020

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