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1/ Mixed models are a powerful statistical tool for analyzing complex data with both fixed and random effects. R has several great packages for fitting mixed models. #rstats #datascience 
2/ One of the most popular packages for mixed models in R is "lme4". This package provides functions for fitting linear and generalized linear mixed models, including models with crossed and nested random effects. #rstats #lme4 cran.r-project.org/web/packages/l…
3/ Another popular mixed model package in R is "nlme". It has similar functionality to "lme4" but is designed to handle longitudinal or repeated-measures data. #rstats #nlme cran.r-project.org/web/packages/n…
Very excited to present my first article, "Mechanistic modeling of metastatic relapse in early-stage breast cancer (eBC) to investigate the biological impact of prognostic biomarkers" !
(supervision @SBenzekry)
#cancer #mathonco #mechanisticModeling #nlme
doi.org/10.1016/j.cmpb…
(supervision @SBenzekry)
#cancer #mathonco #mechanisticModeling #nlme
doi.org/10.1016/j.cmpb…
Our model is (voluntarily) simple and describes the metastatic process through 2 mathematical parameters: alpha (growth) and mu (dissemination), to be confronted to distant metastasis-free survival (DMFS) data
We studied 3 clinical datasets, 2 datasets with routine data (Bergonie, n = 591 and AP-HM, n = 167) and the latter from public databases (IPC, n= 676).
