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Simulation enhanced distributed lag models for mortality displacement.

Koen Simons1, Ronald Buyl2, An Van Nieuwenhuyse3

  • 1Health and Environment, Department of Food, Medicine and Consumer Safety, Scientific Institute of Public Health, Juliette Wytsmanstraat 14, 1050 Brussels, Belgium ; Department of Biostatistics and Medical Informatics, Public Health, Vrije universiteit Brussel, Laarbeeklaan 103, 1090 Jette, Belgium.

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|December 10, 2016
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Summary
This summary is machine-generated.

Distributed lag models (DLMs) are biased for mortality displacement in multi-state models. Simulation enhanced distributed lag models (SEDLMs) offer unbiased estimates for entry and exit effects, improving accuracy.

Keywords:
Air pollutionDistributed lag modelHarvestingMortality displacementSimulation studyTime series

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical Modeling

Background:

  • Distributed lag models (DLMs) are commonly used for analyzing time-series data, particularly in epidemiology.
  • However, DLMs can produce biased estimates when applied to data generated from multi-state models, limiting their validity for assessing mortality displacement.
  • Existing alternative methods are scarce, lack feasibility, and require further validation.

Purpose of the Study:

  • To investigate the limitations of DLMs in three-state models concerning mortality displacement.
  • To propose a novel method, Simulation Enhanced Distributed Lag Models (SEDLM), to address the identified defects of DLMs.
  • To provide simultaneous and improved estimates for both net (entry) and displacement (exit) effects.

Main Methods:

  • Simulation studies were conducted to analyze the breakdown of DLMs within three-state models.
  • The Simulation Enhanced Distributed Lag Models (SEDLM) approach was developed and validated.
  • SEDLM was applied to analyze original Chicago mortality data.

Main Results:

  • DLMs exhibit substantial bias when applied to multi-state models, rendering them invalid for mortality displacement.
  • SEDLM provides simultaneous estimates of entry and exit effects with improved performance over standard DLMs.
  • SEDLM entry estimates show negligible bias and reduced variance; exit estimates are unbiased with significantly lower variance.

Conclusions:

  • DLMs are not suitable for analyzing mortality displacement in multi-state models.
  • SEDLM effectively overcomes the limitations of DLMs, offering accurate and reliable estimates for both entry and exit effects.
  • Application of SEDLM to Chicago data revealed no significant evidence for either a displacement or net effect.