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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.
Evaluation & the Health Professions
|August 22, 2022
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.
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.
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