cran-task-views / cran-task-views/MixedModels

Proposals for the Generalized estimating equations section: glmtoolbox, MIIPW, CRTgeeDR, drgee, geeCRT

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Description

I would like to propose a few items to be added to the View in the Generalized estimating equations section.

The glmtoolbox that I found recently is awesome.

  1. unlike geepack (no new features, no bug fixes, frozen/zombie state), glmtoolbox it's actively developed. Only it's a pity it lacks a GitHub repo, which would facilitate reporting issues a lot. Maybe you could try to convince prof. Vanegas to share it on GitHub well?
  2. unlike geepack, it doesn't crash (prints informative warning) when the unstructured cov. is combined with waves (useful when analysing longitudinal data with missing visits)
  3. supports more covariance structures than any other package
  4. provides leverage, local.influence, DFBETA and Cook's distance out of the box
  5. offers model-comparison based anova through Wald's and generalized score tests.
  6. unlike geepack, it well supports IPW (inverse-probability) weighting for dropouts out of the box (one has to specify the model for MAR dropouts), no need to play separately with the ipw package. Works on both observation and cluster level.
  7. offers a module for non-linear GEE
  8. predict() is available
  9. QIC and RJC (Rotnitzky-Jewell's) criterion out of the box
  10. Mahalanobis', Pearson's and deviance residuals
  11. forward/backward stepwise variable selection
  12. various var.cov estimators are available: robust (Sandwich), df-adjusted, model (naive), bias-corrected (small-sample Mancl-DeRouen) and jackknife.
  13. gives results consistent with other packages (only minor discrepancies)
  14. It well integrates with emmeans via qdrg(), so advanced contrasts over LS-means can be tested, also with MVT adjustment.

Personally speaking, glmtoolbox seems the new king and a standard for GEE in R, outperforming geepack in most aspects.

The other proposed packages, MIIPW, CRTgeeDR, drgee, geeCRT, are important from the clinical trials perspective, offering ways to deal with data missing at random (MAR).

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  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with the Generalized estimating equations section of the MixedModels CRAN Task View and review how existing packages are listed. Assess glmtoolbox, MIIPW, CRTgeeDR, drgee, and geeCRT against the section's conventions, then add the appropriate entries and descriptions when the proposed packages fit.

Written by the indexing model from the issue text.

Assessment

Tech stack
r
Domain
documentation
Issue type
Documentation
Difficulty
3/5
Estimated time
1-2 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
45/100

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