No worry about that.
It is very typical for survival data, such as fixed observation window or end of study, etc.
if no event at time t30, just set it as censored. Remember that S(infinite time) = 0. that is, all should experience event, sooner or later. Censored = (event time > observed time). Missing = unknown status. But we do know that the events do not occur to them.
Jingju

Thank you for your reply, but if I set them as censored, I have to set a value for these points. They can not be".", that will be ignored. But if I choose a value, such as 31 or other value larger than 30, it will influence the output and residual plot. there is an obvious trend in the residual plot, formed by all points valued 31, is that Ok?? It looks so strange so I really got confused. I never find a data like this before.

I may not fully understand your question. But say,

You had observed subjects periodically for maximum 31 times, and noted as t0, t1, ..., t30.
for subject 1, at t15 you observed an event on him/her. so, you have a record likefor subject=2, you followed him to the end of the study (at t30) and no event observed. you have a record like:

for basic survival analysis, the data like above is enough, that is, 3 variables subject, time and status(event).
for example, in KM method in sas you wrote it as

see, that is, event =0,  censored. Remember that, in survial we only consider two statuses of censored or not censored(event).
Here variable 'subject' may not be required. But when you had  multiple records from one subject,you may think that, between subjects, observations are independent; but not within subjects. for example,  time-dependent covariates, or multiple events, etc. Those need special stastics treatment.
JingJu

请问楼主最后是怎么解决这个问题的？

求问怎么解决