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Jeremy L Craft

Publications and source records attributed to Jeremy L Craft.

2 recordsLinked to original sources

Comparison of step-stress data among multiple groups.

Ecotoxicological studies may use organism fitness (e.g., time until exhaustion in a swim test experiment) as an indicator of the impact of some toxic chemical. Failure time studies can use constant stress or may use a protocol in which the stress level is increased at specific times. In these step-stress tests, the units are tested at a given stress level for a predetermined amount of time. At the end of that time, if there are units surviving, the stress level is increased and held constant for another amount of time. This process is repeated until all of the test units fail or the predetermined test time has expired. The purpose of this study is to suggest a method for analyzing responses from step-stress studies. A likelihood-ratio test is developed for comparing the failure times in multiple groups based on a piecewise constant hazard assumption. This likelihood-ratio test can be extended to other piecewise distributions. A small simulation study compares this analysis procedure to other methods currently used to compare multiple groups with regard to type 1 error rate and power.

Computer Simulation↗

A parametric model for studying organism fitness using step-stress experiments.

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.

Analysis of Variance↗