Hypothesis Testing of Failure Time Data: Part II

The hypothesis testing in Part I handled a simple scenario of comparison between groups, but how do you handle more complex scenarios in reliability? What if you suspect a specific trend in failure rates across different groups, or need to account for a confounding variable that might be skewing your results? The standard log-rank test is powerful, but it has its limits.

In Part II, we move beyond basic comparisons to tackle these advanced challenges. This practical, hands-on tutorial will show you how to perform a test for trend and a stratified log-rank test using real-world datasets and step-by-step R code. You'll learn how to statistically confirm an ordered trend in failure rates and how to adjust for confounding factors for a more robust analysis.