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Weighted hurdle regression method for joint modeling of cardiovascular events likelihood and rate in the US dialysis

Damla Sentürk1, Lorien S Dalrymple, Yi Mu

  • 1Department of Biostatistics, University of California, Los Angeles, CA 90095, U.S.A.

Statistics in Medicine
|June 17, 2014
PubMed

Insights

This study introduces a weighted hurdle regression to accurately model cardiovascular events in dialysis patients. The method corrects for varying follow-up times, improving analysis of event probability and rate.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Cardiovascular disease (CVD) is a primary cause of mortality and hospitalization in patients undergoing dialysis.
  • Standard regression models can produce biased results when analyzing count data with excess zeros, especially when follow-up times vary among individuals.
  • Accurate modeling of CVD events in this high-risk population is crucial for effective healthcare management.

Purpose of the Study:

  • To develop and present a novel weighted hurdle regression method for modeling count data, specifically for cardiovascular events in dialysis patients.
  • To address the bias introduced by differential follow-up times in observational studies of cardiovascular events.
  • To jointly model the probability of experiencing a cardiovascular event and the rate of events once they occur.

Main Methods:

  • Proposed a weighted hurdle regression model incorporating a zero-truncated Poisson distribution for event rates.
  • Employed a weighted likelihood approach for model estimation, allowing the use of standard maximum likelihood estimation.
  • Validated the method using data from the United States Renal Data System.

Main Results:

  • The weighted hurdle regression method effectively adjusts for differential follow-up times, mitigating bias in event probability estimation.
  • The method successfully incorporates the influence of covariates on both the likelihood and rate of cardiovascular events.
  • Simulation studies confirmed the robustness and accuracy of the proposed approach.

Conclusions:

  • The weighted hurdle regression offers a statistically sound approach for analyzing cardiovascular event data in dialysis patients with variable follow-up periods.
  • This method provides more reliable estimates of the association between covariates and cardiovascular event risk in this population.
  • The proposed technique enhances the analysis of count data in epidemiological and clinical research where time-at-risk varies.

Keywords:
Poisson regressionUnited States Renal Data Systemcardiovascular outcomesdialysisend-stage renal diseasehurdle modelinfection

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