The article explores a method called generated quantile regression, which helps estimate relationships between variables even when some data is missing. By using a two-step process, researchers can still find important patterns in the data. They developed a way to calculate how accurate their estimates are, even when dealing with missing information. Through simulations and real-world examples, they showed that this method works well in practice.