#longitudinalanalysis — Public Fediverse posts

I often look at papers where authors used a lot of effort to shoehorn a #LongitudinalAnalysis into a trajectory or #MixedModels that do not quite the job the team wants.

Analysing longitudinal data (esp. w time-varying covariates) via G-Estimation is an alternative for consideration:
https://journals.sagepub.com/doi/full/10.1177/25152459231174029 #Tutorial

The underlying thinking is not entirely different, but often one needs only a little step / laterality to get a new view on an analysis problem.

#QuantitativeMethods #Rstats

I often look at papers where authors used a lot of effort to shoehorn a #LongitudinalAnalysis into a trajectory or #MixedModels that do not quite the job the team wants.

Analysing longitudinal data (esp. w time-varying covariates) via G-Estimation is an alternative for consideration:
https://journals.sagepub.com/doi/full/10.1177/25152459231174029 #Tutorial

The underlying thinking is not entirely different, but often one needs only a little step / laterality to get a new view on an analysis problem.

#QuantitativeMethods #Rstats

I often look at papers where authors used a lot of effort to shoehorn a #LongitudinalAnalysis into a trajectory or #MixedModels that do not quite the job the team wants.

Analysing longitudinal data (esp. w time-varying covariates) via G-Estimation is an alternative for consideration:
https://journals.sagepub.com/doi/full/10.1177/25152459231174029 #Tutorial

The underlying thinking is not entirely different, but often one needs only a little step / laterality to get a new view on an analysis problem.

#QuantitativeMethods #Rstats