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Least Squares and Robust Rank-Based Double Bootstrap Analyses for Time-Series Intervention Designs.

Shaofeng Zhang1, Joseph W McKean1, Bradley E Huitema2

  • 1Department of Statistics, 4175Western Michigan University, Kalamazoo, MI, USA.

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Summary

New statistical methods improve time-series intervention analysis in healthcare. An updated double bootstrap approach and a robust version offer more accurate intervention tests and confidence intervals, addressing limitations of traditional models.

Keywords:
Monte Carloautoregressiveinterrupted time-seriesintervention analysislinear modelquasi-experimentrobust methods

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Area of Science:

  • Biostatistics
  • Health Services Research

Background:

  • Time-series intervention designs are crucial in healthcare research.
  • Existing linear models with autoregressive errors face limitations in accuracy.

Purpose of the Study:

  • To present an updated double bootstrap approach for analyzing time-series intervention designs.
  • To introduce a robust method for improved accuracy and outlier insensitivity.

Main Methods:

  • Utilizing an updated double bootstrap approach.
  • Implementing a new robust statistical method, available in an R package.
  • Conducting Monte Carlo simulations and analyzing published data.

Main Results:

  • Traditional generalized linear models yield biased intervention tests and confidence intervals.
  • The updated and robust methods provide more accurate estimations.
  • The robust method demonstrates insensitivity to outliers and non-normal error distributions.

Conclusions:

  • The updated double bootstrap and robust methods offer superior analysis for time-series intervention designs.
  • These advanced statistical techniques enhance the reliability of healthcare intervention research.
  • Accessible R code facilitates the application of these improved analytical tools.