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Patient preferences for personalized (N-of-1) trials: a conjoint analysis.
Moise, Nathalie; Wood, Dallas; Cheung, Ying Kuen K; Duan, Naihua; Onge, Tara St; Duer-Hefele, Joan; Pu, Tiffany; Davidson, Karina W; Kronish, Ian M.
Afiliação
  • Moise N; Columbia University Medical Center, New York, NY, USA. Electronic address: nm2562@cumc.columbia.edu.
  • Wood D; RTI International, Research Triangle Park, NC, USA.
  • Cheung YKK; Columbia University Medical Center, New York, NY, USA.
  • Duan N; Columbia University Medical Center, New York, NY, USA.
  • Onge TS; Columbia University Medical Center, New York, NY, USA.
  • Duer-Hefele J; Columbia University Medical Center, New York, NY, USA.
  • Pu T; Ipsos Group, Mahwah, NJ, USA.
  • Davidson KW; Columbia University Medical Center, New York, NY, USA.
  • Kronish IM; Columbia University Medical Center, New York, NY, USA.
J Clin Epidemiol ; 102: 12-22, 2018 10.
Article em En | MEDLINE | ID: mdl-29859242
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Ensaios Clínicos como Assunto / Medicina de Precisão / Preferência do Paciente Tipo de estudo: Observational_studies / Prevalence_studies / Prognostic_studies / Qualitative_research / Risk_factors_studies Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: J Clin Epidemiol Assunto da revista: EPIDEMIOLOGIA Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Ensaios Clínicos como Assunto / Medicina de Precisão / Preferência do Paciente Tipo de estudo: Observational_studies / Prevalence_studies / Prognostic_studies / Qualitative_research / Risk_factors_studies Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: J Clin Epidemiol Assunto da revista: EPIDEMIOLOGIA Ano de publicação: 2018 Tipo de documento: Article