A parametric model for studying organism fitness using step-stress experiments. (English) Zbl 1274.62780
Summary: 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.
MSC:
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
| 62N05 | Reliability and life testing |
| 62F15 | Bayesian inference |
References:
| [1] | Bai, Optimum simple step-stress accelerated life tests with censoring, IEEE Transactions on Reliability 38, pp 528– (1989) · Zbl 0695.62226 · doi:10.1109/24.46476 |
| [2] | Endicott, Proceedings of the 7th National Symposium on Reliability and Quality Control in Electronics pp 229– (1961) |
| [3] | Gouno, Advances in Reliability, Handbook of Statistics 20 pp 623– (2001) |
| [4] | Harris-Eze, Oxygen improves maximal exercise performance in interstitial lung disease, American Journal of Respiratory and Critical Care Medicine 150, pp 1616– (1994) · doi:10.1164/ajrccm.150.6.7952624 |
| [5] | Kalbfleisch, The Statistical Analysis of Failure Time Data. (1980) · Zbl 0504.62096 |
| [6] | Khamis, Optimum M-step, step-stress design with K stress variables, Communications in Statistics-Simulation 26, pp 1301– (1997) · Zbl 0925.62423 · doi:10.1080/03610919708813441 |
| [7] | Kolok, Interindividual variation in the prolonged locomotor performance of ectothermic vertebrates: A comparison of fish and herpetofaunal methodologies and a brief review of the recent fish literature, Canadian Journal of Fisheries and Aquatic Science 56, pp 700– (1999) · doi:10.1139/f99-026 |
| [8] | Miller, Optimum simple step-stress plans for accelerated life testing, IEEE Transactions on Reliability R-32, pp 59– (1983) · Zbl 0513.62094 · doi:10.1109/TR.1983.5221475 |
| [9] | Nelson, Accelerated life testing: Step-stress models and data analyses, IEEE Transactions of Reliability R-29, pp 103– (1980) · Zbl 0462.62078 · doi:10.1109/TR.1980.5220742 |
| [10] | SAS, SAS/IML User’s Guide Version 8 (1999) |
| [11] | Shaked, Inference for step-stress accelerated life tests, Journal of Statistical Planning and Inference 7, pp 295– (1983) · Zbl 0512.62099 · doi:10.1016/0378-3758(83)90001-0 |
| [12] | Starr, Progressive stress: A new accelerated approach to voltage endurance, Transactions of the American Institute of Electrical Engineers-Part 3. Power Apparatus and Systems 80, pp 515– (1961) · doi:10.1109/AIEEPAS.1961.4501081 |
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