Timeline for Categorising a continuous variable for Cox proportional hazards analysis using "quartiles by event"
Current License: CC BY-SA 4.0
7 events
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| Jul 12, 2020 at 21:47 | comment | added | Alexis | I think using quartiles (and other forms of binning) is a poor general approach to smoothing regressions. See for example, Buja, A., Hastie, T., and Tibshirani, R. (1989). Linear Smoothers and Additive Models. The Annals of Statistics, 17(2):453–510. | |
| Jul 12, 2020 at 12:21 | comment | added | EdM | @Luke yes. The "validity" argument has mostly to do with not throwing away useful data and not setting up massive unrealistic breaks by categorizing. You might end up using as many or more df with a spline model, but the spline model would use the data more efficiently. Categorizing by age could mean a massively different prediction for someone who is 49 years and 11 months old versus someone who is 50 years old. Splines avoid that problem, too. | |
| Jul 12, 2020 at 12:19 | vote | accept | Luke | ||
| Jul 12, 2020 at 12:15 | comment | added | Luke | Ahh I see! So to confirm my understanding, this approach is valid to institute a stratification factor (as a way to include the variable in your model when it violates PH assumptions, without providing a HR for the variable). However, it is NOT valid if including the variable as a standard categorical variable in your model associated with an HR. In this context, a better approach would be restricted cubic splines, with a worse alternative being standard quartiles (lower number of df) | |
| Jul 12, 2020 at 11:36 | comment | added | EdM | @Luke there is no inference on a stratification factor in a Cox model, so in that context it's more a question of what ends up best representing the data rather than bias with respect to the stratified variable. Calculations of hazard ratios are based on event times, so I see some point in using the events to split. If there were no censoring, splitting by events or by quartiles of the predictor would give the same result. The problem would be if you used the process to create a 4-level categorical predictor in the model rather than stratifying by it. | |
| Jul 12, 2020 at 7:24 | comment | added | Luke | Thank you for your comprehensive answer, I really appreciate it! Just to clarify, is there little difference in validity between using quartiles to determine cut-offs for strata of your continuous variable and using quartiles "by event" (trying to equally spit the events between groups)? There's no intrinsic bias to using your collected data to determine your cut-offs? | |
| Jul 11, 2020 at 23:08 | history | answered | EdM | CC BY-SA 4.0 |