Design for Reliability on Test Science Research Document Library https://research.testscience.org/keywords/design-for-reliability/ Recent content in Design for Reliability on Test Science Research Document Library Hugo -- 0.129.0 en-us Institute for Defense Analyses Mon, 01 Jan 2018 00:00:00 +0000 Power Approximations for Reliability Test Designs https://research.testscience.org/post/2018-power-approximations-for-reliability-test-designs/ Mon, 01 Jan 2018 00:00:00 +0000 https://research.testscience.org/post/2018-power-approximations-for-reliability-test-designs/ Reliability tests determine which factors drive system reliability. Often, the reliability or failure time data collected in these tests tend to follow distinctly non- normal distributions and include censored observations. The experimental design should accommodate the skewed nature of the response and allow for censored observations, which occur when systems under test do not fail within the allotted test time. To account for these design and analysis considerations, Monte Carlo simulations are frequently used to evaluate experimental design properties. Reliability tests determine which factors drive system reliability. Often, the reliability or failure time data collected in these tests tend to follow distinctly non- normal distributions and include censored observations. The experimental design should accommodate the skewed nature of the response and allow for censored observations, which occur when systems under test do not fail within the allotted test time. To account for these design and analysis considerations, Monte Carlo simulations are frequently used to evaluate experimental design properties. Simulation provides accurate power calculations as a function of sample size, allowing researchers to determine adequate sample sizes at each level of the treatment. However, simulation may be inefficient for comparing multiple experiments of various sizes. In this document, we present a closed form approach for calculating power, based on the non- central chi-squared approximation to the distribution of the likelihood ratio statistic. The solution can be used to compare multiple designs and accommodate trade-space analyses between power, effect size, model formulation, sample size, censoring rates, and design type. To demonstrate the efficiency of our approach, we provide a comparison to estimates that are generated using Monte Carlo simulation.

Suggested Citation

Johnson, Thomas H., Rebecca M. Medlin, and Laura Freeman. “Power Approximations for Failure-Time Regression Models.” Quality and Reliability Engineering International 35, no. 6 (2019): 1666–75. https://doi.org/10.1002/qre.2467.

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