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Related Experiment Videos

Robust distributed lag models using data adaptive shrinkage.

Yin-Hsiu Chen1, Bhramar Mukherjee1, Sara D Adar2

  • 1Department of Biostatistics, University of Michigan, Washington Heights, Ann Arbor, MI, USA.

Biostatistics (Oxford, England)
|October 18, 2017
PubMed
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This study introduces shrinkage methods for distributed lag models (DLMs) used in environmental epidemiology. These new approaches improve the bias-variance trade-off for estimating air pollution

Area of Science:

  • Environmental Epidemiology
  • Biostatistics
  • Time-Series Analysis

Background:

  • Distributed lag models (DLMs) are standard in environmental epidemiology for assessing air pollution's lagged health impacts.
  • Constrained DLMs offer efficiency but risk bias if the assumed lag function is incorrect.
  • Unconstrained DLMs are unbiased but potentially inefficient, leading to higher variance.

Purpose of the Study:

  • To propose a general framework for shrinking DLM coefficients, balancing bias and variance.
  • To explore various shrinkage methods including empirical Bayes, hierarchical Bayes, and generalized ridge regression.
  • To evaluate the performance of shrinkage methods against traditional approaches via simulation and real-world data.

Main Methods:

  • Developed a shrinkage framework combining unconstrained and constrained DLM coefficient estimates.

Related Experiment Videos

  • Investigated empirical Bayes-type shrinkage, hierarchical Bayes, and generalized ridge regression.
  • Incorporated a two-stage shrinkage approach to ensure effects diminish at longer lags.
  • Validated methods through extensive simulations and application to the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) data.

    • Main Results:

    • Shrinkage methods demonstrated superior average performance across various scenarios, indicated by lower mean squared error (MSE).
    • The proposed framework effectively achieves a desirable bias-variance trade-off in DLM analyses.
    • Empirical evidence supports the utility of shrinkage for robust air pollution health effect estimation.

    Conclusions:

    • Shrinkage methods offer a valuable improvement over traditional distributed lag models in environmental epidemiology.
    • These techniques provide more reliable estimates of air pollution's lagged health effects, particularly when lag function assumptions are uncertain.
    • The study highlights the practical benefits of shrinkage for analyzing complex environmental health time-series data.