I have a database of 22,720 nurses with four observation points say Jan 2011, Jan 2012, Jan 2013 and Jan 2014. I know at each observation point if they developed a condition or not. Some new nurses entered the study at each observation point; I rescaled the temporal scale to assume that all nurses start at time 0 If they are disease free in 2011 but are diseased in 2013 I assume they developed the disease in a time period of 2 years. Most of them never develop the disease.So, my final data looks like this:

       observed: 1yr      observed: 2yr observed 3yr:
disease 0:  177          937         19933
disease 1:  642          482          549

#So, simulating some data:
n.0.disease.1year<-data.frame(event=rep(0,times=177), right=rep(1,times=177))
n.0.disease.2year<-data.frame(event=rep(0,times=937), right=rep(2,times=937))
n.0.disease.3year<-data.frame(event=rep(0,times=19933), right=rep(3,times=19933))
n.1.disease.1year<-data.frame(event=rep(1,times=642), right=rep(1,times=642))
n.1.disease.2year<-data.frame(event=rep(1,times=482), right=rep(2,times=482))
n.1.disease.3year<-data.frame(event=rep(1,times=549), right=rep(3,times=549))

my_data<-rbind(n.0.disease.1year, n.0.disease.2year, n.0.disease.3year, n.1.disease.1year, n.1.disease.2year, n.1.disease.3year)


#Now, I understand that this is interval censored data, so I need to use the survival package in R as #follows:

require('survival')
my_data$left<-0 my.surv<-Surv(my_data$left,my_data$right, type='interval2') sf.my.surv<-survfit(my.surv ~ 1, data = my_data) summary(sf.my.surv) # Or alternatively I can use my_data$event[my_data$event==1]<-3 my.surv.2<-Surv(my_data$left,my_data$right, my_data$event,type='interval')
sf2.my.surv <- survfit(my.surv.2 ~ 1, data = my_data)
summary(sf2.my.surv)

#With  the first option I get the result
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0.5  22720   22720        0     NaN           NA           NA
#With  the second option I get the result
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0.5  22720   22720        0     NaN           NA           NA


Could someone please tell me what I am doing wrong? Thanks in advance

• Please make everything that doesn't have to be an image in your post into text rather than an image (I don't think any of your post should be an image). There is editing help available while you're editing. Note that computer input and output will be readable if you indent it by 4 spaces (select the input or output and click the code tool $\{\,\}\:$, which is very quick).. Sep 4 '17 at 5:54

For models with interval censored response, each subject as a response variable given by $(L_i, R_i]$, where $L_i$ is the last time the subject $i$ was known not have experienced an event and $R_i$ is the first time subject $i$ was known to have an event. So in your study, if a nurse tested negative on year 2 and positive on year 3, they should have response interval $(2, 3]$.
But as you've coded your data, left is a constant at 0. Since every single interval contains the single time point 0, the NPMLE (i.e. fit provided by survfit) places all the probability mass on time 0.
If I understand your problem correctly, for all the subjects that were observed for 1 year only and never tested positive, you should code as $(1, \infty)$, where as subjects who tested positive in the first year I think should be $(0, 1]$ (I say "I think" because this is conditional on the subjects only being exposed for 1 year, which may not be the case in this study!). Next, subjects who were followed for 2 years and never tested positive should be coded as $(2, \infty)$, while subjects that were followed for two years, tested negative after one year but positive after two would be coded as $(1, 2]$.
• @Chris: whether you use NA or inf to specify right censored depends on the syntax used by the software package you use. My package, icenReg, allows either. Mar 6 '18 at 21:20