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Cardiol Young ; 30(6): 807-817, 2020 Jun.
Article in English | MEDLINE | ID: mdl-32605679

ABSTRACT

BACKGROUND/AIMS: Registry-based trials have emerged as a potentially cost-saving study methodology. Early estimates of cost savings, however, conflated the benefits associated with registry utilisation and those associated with other aspects of pragmatic trial designs, which might not all be as broadly applicable. In this study, we sought to build a practical tool that investigators could use across disciplines to estimate the ranges of potential cost differences associated with implementing registry-based trials versus standard clinical trials. METHODS: We built simulation Markov models to compare unique costs associated with data acquisition, cleaning, and linkage under a registry-based trial design versus a standard clinical trial. We conducted one-way, two-way, and probabilistic sensitivity analyses, varying study characteristics over broad ranges, to determine thresholds at which investigators might optimally select each trial design. RESULTS: Registry-based trials were more cost effective than standard clinical trials 98.6% of the time. Data-related cost savings ranged from $4300 to $600,000 with variation in study characteristics. Cost differences were most reactive to the number of patients in a study, the number of data elements per patient available in a registry, and the speed with which research coordinators could manually abstract data. Registry incorporation resulted in cost savings when as few as 3768 independent data elements were available and when manual data abstraction took as little as 3.4 seconds per data field. CONCLUSIONS: Registries offer important resources for investigators. When available, their broad incorporation may help the scientific community reduce the costs of clinical investigation. We offer here a practical tool for investigators to assess potential costs savings.


Subject(s)
Cost Savings/statistics & numerical data , Pragmatic Clinical Trials as Topic/economics , Registries , Research Design , Humans , Markov Chains , Models, Economic
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