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drizopoulos · Oct 15, 2023 · 110eb76 · 110eb76
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diff --git a/docs/articles/Time_Varying_Effects.html b/docs/articles/Time_Varying_Effects.html
diff --git a/vignettes/Time_Varying_Effects.Rmd b/vignettes/Time_Varying_Effects.Rmd
@@ -20,7 +20,7 @@ library("JMbayes2")
 ```
 
 # Non Proportional Hazards
-The basic definition of the joint model assumes the the coefficients that quantify the association between the versions of the longitudinal outcomes and the hazard of the event are time-constant (i.e., the proportional hazards assumption). We can relax this assumption by specifying time-varying coefficients via the `functional_forms` argument of function `jm()`.
+The basic definition of the joint model assumes the coefficients that quantify the association between the versions of the longitudinal outcomes and the hazard of the event are time-constant (i.e., the proportional hazards assumption). We can relax this assumption by specifying time-varying coefficients via the `functional_forms` argument of function `jm()`.
 
 We will illustrate this capability using the PBC dataset. We start by fitting a Cox model for the composite event transplantation or death, including sex as a baseline covariate:
 ```{r}