Robust sandwich estimator

[danielinteractive]

To do:

• Understand and describe what combinations of ST and KR work or don't work from methods point of view with sandwich estimator
• Check how package clubSandwich does it
  • Especially for gls result objects (since our model is also a general least squares model)
• Derive formulas for our case -> write design doc
• Implement prototype calculations
• Compare for one or two examples with SAS results
• extend with method here in mmrm package
  • add news
  • add vignettes
  • add both jackknife and empirical
  • a simple "residual" degree of freedom is added; n_obs - n_params to make things run;
  • add unit tests
  • add integration tests
    • compared empirical with SAS result
    • compared Jackknife with nlme::gls + clubSandwich::vcovCR(type = "CR3")
    • compared weighted empirical with SAS
    • compared weighted Jackknife with nlme::gls + clubSandwich::vcovCR(Please note that the weight used in gls is the inverse of the weight used in SAS and mmrm)

Just fyi: Here is some simple example of gls() with clubSandwich - might be nice to play around with

library(nlme)
m <- gls(
  value ~ time*method,
  correlation =corCompSymm(form=~1|patient_id), 
  data = x, 
  na.action = na.omit, 
  method = "REML"
)
 
emmeans::emmeans(
  m, 
  specs = ~time*method,
  vcov = matrix(clubSandwich::vcovCR(m, type = "CR3"), nrow=6)
)