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?

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    $\begingroup$ There are a number of ways to address heteroscedasticity. I demonstrate many of them in my answer here: Alternatives to one-way ANOVA for heteroscedastic data. $\endgroup$ – gung - Reinstate Monica May 21 '14 at 16:24
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    $\begingroup$ Is your dataset cross-sectional? $\endgroup$ – Sergio May 21 '14 at 16:24
  • $\begingroup$ @gung--these are great suggestions, but I'm wondering whether they need to be fixed at all. $\endgroup$ – dfife May 21 '14 at 16:28
  • $\begingroup$ @Sergio--these data are actually longitudinal. $\endgroup$ – dfife May 21 '14 at 16:29
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    $\begingroup$ If your data are longitudinal (multiple measures on multiple patients) we're playing a different ballgame. You need to differentiate between changing residual variance for each individual's data vs diverging individual trends over time (which is common). You can fit a mixed effects model w/ random intercepts, slopes & a correlation b/t them, get predicted trends for each patient & their individual residuals. The key question is: does that variance change over time? $\endgroup$ – gung - Reinstate Monica May 21 '14 at 17:21

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