Reliability experiments determine which factors drive product reliability. Often, the reliability data collected in these experiments tend to follow distinctly non-normal distributions and typically include censored observations. The experimental design should accommodate the skewed nature of the response and allow for censored observations, which occur when products do not fail within the allotted test time. To account for these design and analysis considerations, Monte-Carlo simulations are frequently used to evaluate design properties for reliability experiments. 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 is inefficient for comparing multiple experiments of various sizes. 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 rapidly compare multiple designs and accommodate trade-space analyses between power, effect size, model formulation, sample size, censoring rates, and design type.