Parametric Modeling of Failure Time data: Part II

Is a simple parametric model always the best fit? After building an initial model, a good analyst always asks, "Can we do better?" Sometimes, the underlying assumptions, like a constant failure pattern across all stress levels, need to be challenged to achieve a more accurate and nuanced understanding.

In Part II of this series, we take our analysis a step further by exploring a more advanced model where the failure characteristics themselves can change with stress. This hands-on tutorial walks you through the process of fitting and evaluating this more complex model, using a likelihood ratio test to statistically determine if the added complexity is justified. This is a crucial lesson in the art of model selection: balancing predictive power with an appropriate level of complexity.