I've got a dataset where I'm attempting to predict when an individual will develop a particular disease based on a set of biomarkers. I'm able to find a pretty good fitting model, but it has a high degree of heteroskedasticity. However, this heteroskedasticity is expected--it makes sense that the model will have smaller residuals as the individual nears diagnosis. I began thinking about various "fixes," but wasn't sure if I should fix it. Any thoughts on this?