Preferences for Predictive Model Characteristics among People Living with Chronic Lung Disease: A Discrete Choice Experiment

Gary E. Weissman, Kuldeep N. Yadav, Trishya Srinivasan, Stephanie Szymanski, Florylene Capulong, Vanessa Madden, Katherine R. Courtright, Joanna L. Hart, David A. Asch, Sarah J. Ratcliffe, Marilyn M. Schapira and Scott D. Halpern
Additional contact information
Gary E. Weissman: Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Kuldeep N. Yadav: Palliative and Advanced Illness Research Center, University of Pennsylvania, Philadelphia, PA, USA
Trishya Srinivasan: Palliative and Advanced Illness Research Center, University of Pennsylvania, Philadelphia, PA, USA
Stephanie Szymanski: Palliative and Advanced Illness Research Center, University of Pennsylvania, Philadelphia, PA, USA
Florylene Capulong: Palliative and Advanced Illness Research Center, University of Pennsylvania, Philadelphia, PA, USA
Vanessa Madden: Palliative and Advanced Illness Research Center, University of Pennsylvania, Philadelphia, PA, USA
Katherine R. Courtright: Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Joanna L. Hart: Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
David A. Asch: Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Sarah J. Ratcliffe: Department of Public Health Sciences and Division of Biostatistics at the University of Virginia, Charlottesville, VA, USA
Marilyn M. Schapira: Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Scott D. Halpern: Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Medical Decision Making, 2020, vol. 40, issue 5, 633-643

Abstract: Background. Patients may find clinical prediction models more useful if those models accounted for preferences for false-positive and false-negative predictive errors and for other model characteristics. Methods. We conducted a discrete choice experiment to compare preferences for characteristics of a hypothetical mortality prediction model among community-dwelling patients with chronic lung disease recruited from 3 clinics in Philadelphia. This design was chosen to allow us to quantify “exchange rates†between different characteristics of a prediction model. We provided previously validated educational modules to explain model attributes of sensitivity, specificity, confidence intervals (CI), and time horizons. Patients reported their interest in using prediction models themselves or having their physicians use them. Patients then chose between 2 hypothetical prediction models each containing varying levels of the 4 attributes across 12 tasks. Results. We completed interviews with 200 patients, among whom 95% correctly chose a strictly dominant model in an internal validity check. Patients’ interest in predictive information was high for use by themselves ( n = 169, 85%) and by their physicians ( n = 184, 92%). Interest in maximizing sensitivity and specificity were similar (0.88 percentage points of specificity equivalent to 1 point of sensitivity, 95% CI 0.72 to 1.05). Patients were willing to accept a reduction of 6.10 months (95% CI 3.66 to 8.54) in the predictive time horizon for a 1% increase in specificity. Discussion. Patients with chronic lung disease can articulate their preferences for the characteristics of hypothetical mortality prediction models and are highly interested in using such models as part of their care. Just as clinical care should become more patient centered, so should the characteristics of predictive models used to guide that care.

Keywords: discrete choice experiment; patient-centered decision making; predictive modeling (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:40:y:2020:i:5:p:633-643

DOI: 10.1177/0272989X20932152

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