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Activity Number: 294
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #304874
Title: Measurement Error Case Series Models with Application to Infection-Cardiovascular Risk in Older Patients on Dialysis
Author(s): Danh Nguyen*+ and Sandra Mohammed and Damla Senturk and Lorien Dalrymple
Companies: University of California at Davis and University of California at Davis and University of California at Los Angeles and University of California at Davis School of Medicine
Address: Division of Biostatistics, Davis, CA, 95616, United States
Keywords: Case series models ; Non-homogeneous Poisson process ; Time-varying exposure onset ; Measurement error ; End stage renal disease ; USRDS

Infection and cardiovascular (CV) disease are leading causes of hospitalization and death in older patients on dialysis. Our recent work found an increase in the relative incidence of CV outcomes during the 30 days after infection-related hospitalizations using the case series model, which adjusts for measured and unmeasured baseline confounders. A major challenge in modeling the infection-CV risk is that the exact time of infection (or "exposure") onsets cannot be ascertained based on hospitalization data. Only imprecise markers of the timing of infection onsets are available. We propose the measurement error case series (MECS) models, to account for measurement error in time-varying exposure onsets. We describe the general nature of bias resulting from estimation that ignores measurement error and proposed a bias-correction method. Hospitalization data from U.S. Renal Data System, which captures nearly all patients with end-stage renal disease in the U.S. over time, is used to illustrate the new method. The results suggest that the estimate of the CV incidence following the 30 days after infections is substantially attenuated in the presence of infection onset measurement error.

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