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Asked: 2026-06-07T17:41:05+00:00

I am developing a censored dependent variable for use in survival analysis. My goal

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I am developing a censored dependent variable for use in survival analysis. My goal is to find the last time (“time”) that someone answers a question in a survey (e.g. the point where “q.time” is coded as “1”, and “q.time+1” and q at all subsequent times are coded as “0”).

By this logic, the last question answered should be coded as “1” (q.time). The first question that is NOT answered (q.time+1) should be coded as “0”. And all questions subsequent to the first question NOT answered should be coded as “NA”. I then want to remove ALL rows where the DV=NA from my dataset.

A very generous coworker has helped me to develop the following code, but he’s on vacation now and it needs a little more lovin’. Code is as follows:

library(plyr)  # for ddply 
library(stats)  # for reshape(...) 
# From above 
dat <- data.frame( 
  id=c(1, 2, 3, 4), 
  q.1=c(1, 1, 0, 0), 
  q.2=c(1, 0, 1, 0), 
  dv.1=c(1, 1, 1, 1), 
  dv.2=c(1, 1, 0, 1)) 
# From above 
  long <- reshape(dat, 
                direction='long', 
                varying=c('q.1', 'q.2', 'dv.1', 'dv.2')) 
   ddply(long, .(id), function(df) { 
# figure out the dropoff time 
answered <- subset(df, q == 1) 
last.q = max(answered$time) 
subs <- subset(df, time <= last.q + 1) 
# set all the dv as desired 
new.dv <- rep(last.q,1) 
if (last.q < max(df$time)) new.dv <- c(0,last.q) 
subs$dv <- new.dv 
subs 
})

Unfortunately, this yields the error message:

"Error in `$<-.data.frame`(`*tmp*`, "dv", value = c(0, -Inf)) : 
 replacement has 2 rows, data has 0"

Any ideas? The problem seems to be located in the “rep” command, but I’m a newbie to R. Thank you so much!

UPDATE: SEE EXPLANATIONS BELOW, and then REFER TO FOLLOW-UP QUESTION

Hi there-I completely followed you, and really appreciate the time you took to help me out. I went back into my data and coded in a dummy Q where all respondents have a value of “1” – but, discovered where the error really may be. In my real data set, I have 30 questions (i.e., 30 times in long form). After I altered the dataset so FOR SURE q==1 for all id variables, the error message changed to saying 

"Error in `$<-.data.frame`(`*tmp*`, "newvar", value = c(0, 29)) : replacement has 2 rows, data has 31"

If the problem is with the number of rows assigned to subs, then is the source of the error coming from…

subs <- subset(df, time <= last.q + 1)

i.e., $time <= last.q + 1$ is setting the number of rows to the value EQUAL to last.q+1?

UPDATE 2: What, ideally, I’d like my new variable to look like!

 id  time q  dv   
 1    1   1   1
 1    2   1   1
 1    3   1   1
 1    4   1   1
 1    5   0   0
 1    6   0   NA
 2    1   1   1
 2    2   1   1
 2    3   0   0
 2    4   0   NA
 2    5   0   NA
 2    6   0   NA

Please note that “q” can vary between “0” or “1” over time (See the observation for id=1 at time=2), but due to the nature of survival analysis, “dv” cannot. What I need to do is create a variable that finds the LAST time that “q” changes between “1” and “0”, and then is censored accordingly. After Step 4, my data should look like this:

 id  time q  dv   
 1    1   1   1
 1    2   1   1
 1    3   1   1
 1    4   1   1
 2    1   1   1
 2    2   1   1
 2    3   0   0
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1 Answer

  1. Editorial Team

    First, to give credit where credit is due, the code below is not mine. It was generated in collaboration with another very generous coworker (and engineer) who helped me work through my problem (for hours!).

    I thought that other analysts tasked with constructing a censored variable from survey data might find this code useful, so I am passing the solution along.

    library(plyr)
#A function that only selects cases before the last time "q" was coded as "1"
slicedf <- function(df.orig, df=NULL) {
if (is.null(df)) {
    return(slicedf(df.orig, df.orig))
}
if (nrow(df) == 0) {
    return(df)
}
target <- tail(df, n=1)
   #print(df)
   #print('--------')
   if (target$q == 0) {
       return(slicedf(df.orig, df[1:nrow(df) - 1, ]))
   }
if (nrow(df.orig) == nrow(df)) {
    return(df.orig)
}
return(df.orig[1:(nrow(df) + 1), ])
}
#Applies function to the dataset, and codes over any "0's" before the last "1" as "1"
long <- ddply(long, .(id), function(df) {
df <- slicedf(df)
if(nrow(df) == 0) {
return(df)
}
q <- df$q
if (tail(q, n=1) == 1) {
df$q <- rep(1, length(q))
} else {
df$q <- c(rep(1, length(q) - 1), 0)
}
return(df)
})

    Thanks to everyone online who commented for your patience and help.

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