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Machine Learning Prediction of Clinical Trial Operational Efficiency.
Wu, Kevin; Wu, Eric; DAndrea, Michael; Chitale, Nandini; Lim, Melody; Dabrowski, Marek; Kantor, Klaudia; Rangi, Hanoor; Liu, Ruishan; Garmhausen, Marius; Pal, Navdeep; Harbron, Chris; Rizzo, Shemra; Copping, Ryan; Zou, James.
Afiliação
  • Wu K; Department of Biomedical Data Science, Stanford University, Stanford, California, USA. kevinywu@stanford.edu.
  • Wu E; Department of Electrical Engineering, Stanford University, Stanford, California, USA.
  • DAndrea M; Genentech, South San Francisco, San Francisco, California, USA.
  • Chitale N; Genentech, South San Francisco, San Francisco, California, USA.
  • Lim M; Genentech, South San Francisco, San Francisco, California, USA.
  • Dabrowski M; Roche Pharmaceuticals, Warsaw, Poland.
  • Kantor K; Roche Pharmaceuticals, Warsaw, Poland.
  • Rangi H; Roche Pharmaceuticals, Mississauga, Canada.
  • Liu R; Department of Electrical Engineering, Stanford University, Stanford, California, USA.
  • Garmhausen M; Roche Pharmaceuticals, Basel, Switzerland.
  • Pal N; Genentech, South San Francisco, San Francisco, California, USA.
  • Harbron C; Roche Pharmaceuticals, Welwyn Garden City, UK.
  • Rizzo S; Genentech, South San Francisco, San Francisco, California, USA.
  • Copping R; Genentech, South San Francisco, San Francisco, California, USA.
  • Zou J; Department of Biomedical Data Science, Stanford University, Stanford, California, USA.
AAPS J ; 24(3): 57, 2022 04 21.
Article em En | MEDLINE | ID: mdl-35449371
Clinical trials are the gatekeepers and bottlenecks of progress in medicine. In recent years, they have become increasingly complex and expensive, driven by a growing number of stakeholders requiring more endpoints, more diverse patient populations, and a stringent regulatory environment. Trial designers have historically relied on investigator expertise and legacy norms established within sponsor companies to improve operational efficiency while achieving study goals. As such, data-driven forecasts of operational metrics can be a useful resource for trial design and planning. We develop a machine learning model to predict clinical trial operational efficiency using a novel dataset from Roche containing over 2,000 clinical trials across 20 years and multiple disease areas. The data includes important operational metrics related to patient recruitment and trial duration, as well as a variety of trial features such as the number of procedures, eligibility criteria, and endpoints. Our results demonstrate that operational efficiency can be predicted robustly using trial features, which can provide useful insights to trial designers on the potential impact of their decisions on patient recruitment success and trial duration.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article