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Reporting and Methods in Clinical Prediction Research: A Systematic Review

Author

Listed:
  • Walter Bouwmeester
  • Nicolaas P A Zuithoff
  • Susan Mallett
  • Mirjam I Geerlings
  • Yvonne Vergouwe
  • Ewout W Steyerberg
  • Douglas G Altman
  • Karel G M Moons

Abstract

Walter Bouwmeester and colleagues investigated the reporting and methods of prediction studies in 2008, in six high-impact general medical journals, and found that the majority of prediction studies do not follow current methodological recommendations. Background: We investigated the reporting and methods of prediction studies, focusing on aims, designs, participant selection, outcomes, predictors, statistical power, statistical methods, and predictive performance measures. Methods and Findings: We used a full hand search to identify all prediction studies published in 2008 in six high impact general medical journals. We developed a comprehensive item list to systematically score conduct and reporting of the studies, based on recent recommendations for prediction research. Two reviewers independently scored the studies. We retrieved 71 papers for full text review: 51 were predictor finding studies, 14 were prediction model development studies, three addressed an external validation of a previously developed model, and three reported on a model's impact on participant outcome. Study design was unclear in 15% of studies, and a prospective cohort was used in most studies (60%). Descriptions of the participants and definitions of predictor and outcome were generally good. Despite many recommendations against doing so, continuous predictors were often dichotomized (32% of studies). The number of events per predictor as a measure of statistical power could not be determined in 67% of the studies; of the remainder, 53% had fewer than the commonly recommended value of ten events per predictor. Methods for a priori selection of candidate predictors were described in most studies (68%). A substantial number of studies relied on a p-value cut-off of p

Suggested Citation

  • Walter Bouwmeester & Nicolaas P A Zuithoff & Susan Mallett & Mirjam I Geerlings & Yvonne Vergouwe & Ewout W Steyerberg & Douglas G Altman & Karel G M Moons, 2012. "Reporting and Methods in Clinical Prediction Research: A Systematic Review," PLOS Medicine, Public Library of Science, vol. 9(5), pages 1-13, May.
  • Handle: RePEc:plo:pmed00:1001221
    DOI: 10.1371/journal.pmed.1001221
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    Cited by:

    1. Ben Van Calster & Andrew J. Vickers, 2015. "Calibration of Risk Prediction Models," Medical Decision Making, , vol. 35(2), pages 162-169, February.
    2. Paul Bach & Christine Wallisch & Nadja Klein & Lorena Hafermann & Willi Sauerbrei & Ewout W Steyerberg & Georg Heinze & Geraldine Rauch & for topic group 2 of the STRATOS initiative, 2020. "Systematic review of education and practical guidance on regression modeling for medical researchers who lack a strong statistical background: Study protocol," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-10, December.
    3. Thomas P A Debray & Karel G M Moons & Ghada Mohammed Abdallah Abo-Zaid & Hendrik Koffijberg & Richard David Riley, 2013. "Individual Participant Data Meta-Analysis for a Binary Outcome: One-Stage or Two-Stage?," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-10, April.
    4. Igor O Korolev & Laura L Symonds & Andrea C Bozoki & Alzheimer's Disease Neuroimaging Initiative, 2016. "Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-25, February.
    5. Marta Morales-Puerto & María Ruiz-Díaz & Marta Aranda-Gallardo & José Miguel Morales-Asencio & Purificación Alcalá-Gutiérrez & José Antonio Rodríguez-Montalvo & Álvaro León-Campos & Silvia García-Mayo, 2022. "Development of a Clinical Prediction Rule for Adverse Events in Multimorbid Patients in Emergency and Hospitalisation," IJERPH, MDPI, vol. 19(14), pages 1-14, July.
    6. Michael Lebenbaum & Osvaldo Espin-Garcia & Yi Li & Laura C Rosella, 2018. "Development and validation of a population based risk algorithm for obesity: The Obesity Population Risk Tool (OPoRT)," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-11, January.
    7. Ben Van Calster & Ewout W. Steyerberg & Ralph B. D’Agostino Sr & Michael J. Pencina, 2014. "Sensitivity and Specificity Can Change in Opposite Directions When New Predictive Markers Are Added to Risk Models," Medical Decision Making, , vol. 34(4), pages 513-522, May.
    8. Taro Takeshima & Yosuke Yamamoto & Yoshinori Noguchi & Nobuyuki Maki & Koichiro Gibo & Yukio Tsugihashi & Asako Doi & Shingo Fukuma & Shin Yamazaki & Eiji Kajii & Shunichi Fukuhara, 2016. "Identifying Patients with Bacteremia in Community-Hospital Emergency Rooms: A Retrospective Cohort Study," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-17, March.
    9. John H Wasson & Lynn Ho & Laura Soloway & L Gordon Moore, 2018. "Validation of the What Matters Index: A brief, patient-reported index that guides care for chronic conditions and can substitute for computer-generated risk models," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-13, February.
    10. Helder Novais Bastos & Nuno S Osório & António Gil Castro & Angélica Ramos & Teresa Carvalho & Leonor Meira & David Araújo & Leonor Almeida & Rita Boaventura & Patrícia Fragata & Catarina Chaves & Pat, 2016. "A Prediction Rule to Stratify Mortality Risk of Patients with Pulmonary Tuberculosis," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-14, September.

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