Measurement Error Case Series Models With Application to Infection-Cardiovascular Risk in Older Patients on Dialysis

Abstract

Infection and cardiovascular disease are leading causes of hospitalization and death in older patients on dialysis. Our recent work found an increase in the relative incidence of cardiovascular outcomes during the ∼ 30 days after infection-related hospitalizations using the case series model, which adjusts for measured and unmeasured baseline confounders. However, a major challenge in modeling/assessing the infection-cardiovascular risk hypothesis is that the exact time of infection, or more generally “exposure,” onsets cannot be ascertained based on hospitalization data. Only imprecise markers of the timing of infection onsets are available. Although there is a large literature on measurement error in the predictors in regression modeling, to date, there is no work on measurement error on the timing of a time-varying exposure to our knowledge. Thus, we propose a new method, the measurement error case series (MECS) models, to account for measurement error in time-varying exposure onsets. We characterized the general nature of bias resulting from estimation that ignores measurement error and proposed a bias-corrected estimation for the MECS models. We examined in detail the accuracy of the proposed method to estimate the relative incidence of cardiovascular events. Hospitalization data from the United States Renal Data System, which captures nearly all (>99%) patients with end-stage renal disease in the United States over time, are used to illustrate the proposed method. The results suggest that the estimate of the relative incidence of cardiovascular events during the 30 days after infections, a period where acute effects of infection on vascular endothelium may be most pronounced, is substantially attenuated in the presence of infection onset measurement error.

Keywords:

• Cardiovascular outcomes
• Case series models
• End-stage renal disease
• Infection
• Measurement error
• Nonhomogeneous Poisson process
• Time-varying exposure onset
• United States Renal Data System

Acknowledgments

This publication was made possible by grant UL1 RR024146 from the National Center for Research Resources and NIH K12 grant through the UC Davis Clinical and Translational Science Center (Nguyen, Dalyrymple) and partially by NIDDK (National Institute of Diabetes and Digestive and Kidney Diseases) grant R01DK092232 (Şentürk, Dalyrymple, Nguyen). We thank Barbara Grimes, Department of Biostatistics, University of California, San Francisco, and Yi Mu at UC Davis Department of Public Health Sciences. We are grateful to two reviewers and an associate editor for thoughtful suggestions, which improved the article. The interpretation and reporting of the data presented here are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the U.S. government. This study was approved by the Institutional Review Board of the University of California Davis Health System.