2022
DOI: 10.1186/s12885-022-09333-6
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New method for determining breast cancer recurrence-free survival using routinely collected real-world health data

Abstract: Background In cancer survival analyses using population-based data, researchers face the challenge of ascertaining the timing of recurrence. We previously developed algorithms to identify recurrence of breast cancer. This is a follow-up study to detect the timing of recurrence. Methods Health events that signified recurrence and timing were obtained from routinely collected administrative data. The timing of recurrence was estimated by finding the … Show more

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Cited by 9 publications
(2 citation statements)
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“…The primary outcome of interest for aims 2 and 3 was 5-year RFS. The occurrence of relapses is not routinely collected in administrative data; thus, a case-finding algorithm by Jung et al 21 was used to identify patients with a recurrence. Time zero or baseline was defined as the date of hormone treatment initiation.…”
Section: Methodsmentioning
confidence: 99%
“…The primary outcome of interest for aims 2 and 3 was 5-year RFS. The occurrence of relapses is not routinely collected in administrative data; thus, a case-finding algorithm by Jung et al 21 was used to identify patients with a recurrence. Time zero or baseline was defined as the date of hormone treatment initiation.…”
Section: Methodsmentioning
confidence: 99%
“…Patient management in oncology can vary based on several clinical and disease-related factors, including features discussed in Section 1 and Section 2 above and the evolution of staging and treatment paradigms in any one condition over time. Notably, demographics and management can be drastically different between trial populations and the real world [ 27 , 28 ], resulting in heterogeneous data mirrored in data sets employed for analysis, training, testing, and validation ( Figure 2 ). In real-world settings, oncologic management can be highly variable based on geographical location, access, and resources, which can significantly impact training AI algorithms and result in non-transferable products.…”
Section: Sources Of Bias and Class Imbalance In Large Scale Medical Datamentioning
confidence: 99%