Lindsay J Collin1,2, Anders H Riis2, Richard F MacLehose3, Thomas P Ahern4, Rune Erichsen2,5, Ole Thorlacius-Ussing6, Timothy L Lash1. 1. Department of Epidemiology, Emory University, Atlanta, GA, USA. 2. Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark. 3. Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA. 4. Department of Surgery, The Robert Larner, M.D. College of Medicine at the University of Vermont, Burlington, VT, USA. 5. Department of Surgery, Randers Regional Hospital, Randers, Denmark. 6. Department of Gastrointestinal Surgery, Aalborg University Hospital, Aalborg, Denmark.
Abstract
BACKGROUND: Among men and women diagnosed with colorectal cancer (CRC), 20-50% will develop a cancer recurrence. Cancer recurrences are not routinely captured by most population-based registries; however, linkage across Danish registries allows for the development of predictive models to detect recurrence. Successful application of such models in population-based settings requires validation against a gold standard to ensure the accuracy of recurrence identification. OBJECTIVE: We apply a recently developed validation study design for prospectively collected validation data to validate predicted CRC recurrences against gold standard diagnoses from medical records in an actively followed cohort of CRC patients in Denmark. METHODS: We use a Bayesian monitoring framework, traditionally used in clinical trials, to iteratively update classification parameters (positive and negative predictive values, and sensitivity and specificity) in an adaptive validation substudy design. This design allows determination of the sample size necessary to estimate the corresponding parameters and to identify when validation efforts can cease based on predefined criteria for parameter values and levels of precision. RESULTS: Among 355 men and women diagnosed with CRC in Denmark and actively followed semi-annually, there were 63 recurrences diagnosed by active follow-up and 70 recurrences identified by a predictive algorithm. The adaptive validation design met stopping criteria for the classification parameters after 120 patients had their recurrence information validated. This stopping point yielded parameter estimates for the classification parameters similar to those obtained when the entire cohort was validated, with 66% less patients needed for the validation study. CONCLUSION: In this proof of concept application of the adaptive validation study design for outcome misclassification, we demonstrated the ability of the method to accurately determine when sufficient validation data have been collected. This method serves as a novel validation substudy design for prospectively collected data with simultaneous implementation of a validation study.
BACKGROUND: Among men and women diagnosed with colorectal cancer (CRC), 20-50% will develop a cancer recurrence. Cancer recurrences are not routinely captured by most population-based registries; however, linkage across Danish registries allows for the development of predictive models to detect recurrence. Successful application of such models in population-based settings requires validation against a gold standard to ensure the accuracy of recurrence identification. OBJECTIVE: We apply a recently developed validation study design for prospectively collected validation data to validate predicted CRC recurrences against gold standard diagnoses from medical records in an actively followed cohort of CRC patients in Denmark. METHODS: We use a Bayesian monitoring framework, traditionally used in clinical trials, to iteratively update classification parameters (positive and negative predictive values, and sensitivity and specificity) in an adaptive validation substudy design. This design allows determination of the sample size necessary to estimate the corresponding parameters and to identify when validation efforts can cease based on predefined criteria for parameter values and levels of precision. RESULTS: Among 355 men and women diagnosed with CRC in Denmark and actively followed semi-annually, there were 63 recurrences diagnosed by active follow-up and 70 recurrences identified by a predictive algorithm. The adaptive validation design met stopping criteria for the classification parameters after 120 patients had their recurrence information validated. This stopping point yielded parameter estimates for the classification parameters similar to those obtained when the entire cohort was validated, with 66% less patients needed for the validation study. CONCLUSION: In this proof of concept application of the adaptive validation study design for outcome misclassification, we demonstrated the ability of the method to accurately determine when sufficient validation data have been collected. This method serves as a novel validation substudy design for prospectively collected data with simultaneous implementation of a validation study.
Authors: Ashley C Holmes; Anders H Riis; Rune Erichsen; Veronika Fedirko; Eva Bjerre Ostenfeld; Mogens Vyberg; Ole Thorlacius-Ussing; Timothy L Lash Journal: Acta Oncol Date: 2017-03-24 Impact factor: 4.089
Authors: Rebecca L Siegel; Kimberly D Miller; Stacey A Fedewa; Dennis J Ahnen; Reinier G S Meester; Afsaneh Barzi; Ahmedin Jemal Journal: CA Cancer J Clin Date: 2017-03-01 Impact factor: 508.702
Authors: Peer Wille-Jørgensen; Ingvar Syk; Kenneth Smedh; Søren Laurberg; Dennis T Nielsen; Sune H Petersen; Andrew G Renehan; Erzsébet Horváth-Puhó; Lars Påhlman; Henrik T Sørensen Journal: JAMA Date: 2018-05-22 Impact factor: 56.272
Authors: Freddie Bray; Jacques Ferlay; Isabelle Soerjomataram; Rebecca L Siegel; Lindsey A Torre; Ahmedin Jemal Journal: CA Cancer J Clin Date: 2018-09-12 Impact factor: 508.702