"No (non-missing) observations" error in jointModelBayes function

#1
Hi all,

I'm trying to fit a joint longitudinal and time-to event model in R as shown here:http://www.r-bloggers.com/joint-models-for-longitudinal-and-survival-data/

Patients were randomized to placebo or treatment ("Treatment_Group"), had a biomarker ("CII") measured at multiple times (denoted by "Month_visit") throughout the study and their time to disease development was recorded (denoted as "Months_to_status").

I have two dataframes, one in long format ("long") with the longitudinal data and one with the time-to-event data ("status") in the format of one row per subject. Here is my code so far:

Code:
##LOAD JMBAYES AND NLME PACKAGES##
library("JMbayes")
library("nlme")

##MIXED EFFECTS MODELS FOR TIME-DEPENDENT COVARIATE##
lmeCII <- lme(log2(CII) ~ Month_visit, data = long, random = ~ Month_visit | Patient_ID)

##COX MODEL FOR FIXED EFFECT OF TREATMENT##
coxFit <- coxph(Surv(Months_to_status, RA_Develop) ~ Treatment_Group, data = status, x = TRUE)

##JOINT MODEL##
jointFit <- jointModelBayes(lmeCII, coxFit, timeVar = "Months_to_status")
but I get the following error:

Code:
Error in coxph(Surv(start, stop, event) ~ ., data = DF[DF$stop > DF$start,  : 
  No (non-missing) observations
In addition: Warning message:
In max(event[who2]) : no non-missing arguments to max; returning -Inf
I have no missing data in either dataframe and the same subjects are included in both. So can anyone suggest what I might be doing wrong? Thanks, HRJ21.