AI Article Synopsis
- The proposed method uses parametric survival analysis to evaluate step-stress data, typically seen in industrial reliability but also applicable to biological experiments like fish swimming performance and human treadmill tests.
- A likelihood-ratio test is introduced to compare failure times across two groups with a piecewise constant hazard assumption, and it can be adapted for different distributions and covariates.
- An example data set demonstrates the methodology, alongside a small simulation study that assesses the effectiveness of this approach compared to existing methods in terms of type I error rate and statistical power.
Article Abstract
We propose a method based on parametric survival analysis to analyze step-stress data. Step-stress studies are failure time studies in which the experimental stressor is increased at specified time intervals. While this protocol has been frequently employed in industrial reliability studies, it is less common in the life sciences. Possible biological applications include experiments on swimming performance of fish using a step function defining increasing water velocity over time, and treadmill tests on humans. A likelihood-ratio test is developed for comparing the failure times in two groups based on a piecewise constant hazard assumption. The test can be extended to other piecewise distributions and to include covariates. An example data set is used to illustrate the method and highlight experimental design issues. A small simulation study compares this analysis procedure to currently used methods with regard to type I error rate and power.
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| http://dx.doi.org/10.1111/j.0006-341X.2004.00230.x | DOI Listing |
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