• Correction to: Transcriptome-wide study of TNF-inhibitor therapy in rheumatoid arthritis reveals early signature of successful treatment.

      Oliver, James; Nair, Nisha; Orozco, Gisela; Smith, Samantha; Hyrich, Kimme L; Morgan, Ann; Isaacs, John; Wilson, Anthony G; BRAGGSS; Barton, Anne; et al. (2021-05-08)
    • Towards Personalising the Use of Biologics in Rheumatoid Arthritis: A Discrete Choice Experiment.

      Vass, Caroline M; Barton, Anne; Payne, Katherine; orcid: 0000-0002-3938-4350; email: katherine.payne@manchester.ac.uk (2021-06-18)
      There have been promising developments in technologies and associated algorithm-based prescribing ('stratified approach') to target biologics to sub-groups of people with rheumatoid arthritis (RA). The acceptability of using an algorithm-guided approach in practice is likely to depend on various factors. This study quantified preferences for an algorithm-guided approach to prescribing biologics (termed 'biologic calculator'). An online discrete choice experiment (DCE) was designed to elicit preferences from patients and the public for using a 'biologic calculator' compared with conventional prescribing. Treatment approaches were described by five attributes: delay to starting treatment; positive and negative predictive value (PPV/NPV); risk of infection; and cost saving to the UK national health service. Each survey contained six choice sets asking respondents to select their preferred option from two hypothetical biologic calculators or conventional prescribing. Background questions included sociodemographics, health status and healthcare experiences. DCE data were analysed using mixed logit models. Completed choice data were collected from 292 respondents (151 patients with RA and 142 members of the public). PPV, NPV and risk of infection were the most highly valued attributes to respondents deciding between prescribing strategies. Respondents were generally receptive to personalised medicine in RA, but researchers developing personalised approaches should pay close attention to generating evidence on both the PPV and the NPV of their technologies.