In AJE:
Missing data? Don't drop observations! A longitudinal study in @hrsisr shows multiple imputation with predictive mean matching performs well, is easy to implement, and helps recover unbiased results
https://t.co/tVQjbMEzzG
Just released on cran, psfmi package version 1.4.0 including new possibility to pool and select stratified Cox models after Multiple Imputation #rstats https://t.co/tdIxIZpckI
@RoelWing just published his last PhD paper in the @JPhysiother!
A prognostic model for neck-related disability in patients with sub-acute neck pain was externally validated at 6 weeks with acceptable discrimination and calibration.
https://t.co/mxL8p9sXJo
His publications are suggested reading for anyone interested in prognostic modelling for neck pain.
Starting with this systematic review...
https://t.co/l8JijhviOT
...to this paper failing to externally validate the most promising models:
https://t.co/kyDyyeWLpj
2/3
Proud to present this paper about Handling Missing Data in Clinical Research as a Key Concept in Clinical Epidemiology @JClinEpi https://t.co/qBhL9hTJhA
Ever wondered whether it is necessary to impute cost data before using longitudinal linear mixed-model analyses? Check out our paper below for the answer! @hannekevandonge@VroomenJanet@mwheymans https://t.co/6ngH7m3X8m