@f2harrell could you clarify what was not a good idea? Unless I’m mistaken, your comment was referring to a model which includes the baseline measurement as an outcome. The text shared by @FurlanLeo showed the baseline adjusted model for 2 post-treatment outcome measurements, which appears to be the method you prefer.
Yes I always use baseline as baseline. So @FurlanLeo had 2 follow-ups making correlation assumptions less of of assume, but still continuous time should be used if times vary much.
Regarding “I am finding that the baseline needs to be nonlinearly modeled (using e.g. a restricted cubic spline) for these types of outcomes because some patients can start off at extremely bad levels and get major benefits from treatments.”
Do you interpret the rcs(baseline) variable here, or no interpretation needed and it’s simply being adjusted for in the model nonlinearly because it’s better than the linear form?
You can choose either to interpret it or not interpret it. You can estimate anything you want from this model, including the expected change from baseline over a grid of possible baseline values.
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RE: “You can choose either to interpret it or not interpret it. You can estimate anything you want from this model, including the expected change from baseline over a grid of possible baseline values.”
Below are some findings comparing rcs(baseline) vs. the raw baseline values using an ANCOVA model for our seizure dataset:
- The raw baseline values appear to be a much stronger predictor than rcs(baseline)
- raw baseline values: p<.00001, with a fairly small estimate
- rcs(baseline): none of the segments are statistically significant, though the estimates for each segment are about 100 times larger than that of the linear form
- visually inspecting the baseline values plotted against the outcome (follow-up values) adjusting for treatment group, it appears okay to use the raw baseline values
- The choice of either using the linear or nonlinear form of baseline didn’t seem to affect the estimates of the treatment groups compared to placebo.
- similar estimates for the treatment-to-placebo comparisons and p-values for the 2 models
- similar adjusted r squared for the 2 models
- Residual plots for both appear to be similar. However, both models showed some issues for the two tails in the residual plots. ANCOVA likely is not the best approach if one can explore ordinal longitudinal approaches.
In this case, would you just use the linear form of baseline if one has to use the ANCOVA model? Is the decision based on p-values and/or what makes sense visually? Thank you.
Fundamental problem: you can’t interpret individual spline terms. Instead compute AIC for the linear model and AIC for the spline model, or just look at chunk \chi^2 that combines all terms for baseline.
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