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Activity Number: 82 - Statistical Methods for Disease Prevention and Prediction
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #305062
Title: Frailty Model to Account for Unmeasured Heterogeneity in SEER Registry Data: An Illustration to Estimate Race-Ethnic Mortality Risk in Pediatric Acute Myeloid Leukemia
Author(s): Hacene Boukari* and Fatima Boukari and Md Jobayer Hossain
Companies: Delaware State University and Delaware State University and Nemours children Healthcare Systems
Keywords: Frailty Model; SEER data; heterogeneous hazard risk; Simpson's paradox; unmeasured heterogeneity

The SEER program is the most comprehensive and reliable source of population-based information on cancer incidence and survival in the US. This is an outstanding resource for researching and explaining aspects of cancers. While SEER data-based research has great utility in improving survival outcomes and reducing cancer burden in the US, individuals in this dataset can have a systematically heterogeneous hazard risk of mortality. Patients in the SEER dataset are diagnosed over many years. Risk factors may change over time, and many important variables may not be available in SEER data. These together may impose a heterogeneous mortality risk among patients. We identified a Simpson’s paradox in race-ethnic effects of pediatric AML survival in the SEER data of 1973–2014. Over the years, Hispanic patients demonstrated a higher risk of mortality compared to Caucasians. This heterogeneity was caused by the concurrence of a decreased overall death rate and an increased proportion of Hispanic patients in SEER data over time. This study illustrates the use of Frailty model to accurately capture this unmeasured heterogeneity that can produce unbiased estimation of hazard risk in SEER data.

Authors who are presenting talks have a * after their name.

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