5.2 - Special Considerations for Event Times

Event Times Section

Event times often are useful endpoints in clinical trials. Examples include survival time from onset of diagnosis, time until progression from one stage of disease to another, and time from surgery until hospital discharge. In each case time is measured from study entry until the event occurs. With an endpoint that is based on an event time, there always is the chance of censoring. An event time is censored if there is some amount of follow-up on a subject, but the event is not observed because of loss-to-follow-up, death from a cause other than the trial endpoint, study termination, and other reasons unrelated to the endpoint of interest.. This is known as right censoring and occurs frequently in studies of survival.

Patients Day 0 Day 365 Time X X = censored X = event

Right-censoring example

Consider the table above which displays time until infection for Patients 1-6. In some cases, the event did not occur, Patient 1 (from top) was followed for a year and was censored at the end of the study). The second patient experienced an infection at approximately 325 days. Patients 3 and 6 dropped out of the study and were censored when this occurred.

Left censoring occurs when the initiation time for the subject, such as time of diagnosis, is unknown. Interval censoring occurs when the subject is not followed for a period of time during the trial and it is unknown if the event occurred during that period.

Right Censoring Types Section

There are three types of right censoring that are described in the statistical literature.

Type I censoring occurs when all subjects are scheduled to begin the study at the same time and end the study at the same time. This type of censoring is common in laboratory animal experiments, but unlikely in human trials.

Type II censoring occurs when all subjects begin the study at the same time and the study is terminated when a predetermined proportion of subjects have experienced the event.

Type III censoring occurs when the censoring is random, which is the case in clinical trials because of staggered entry (not every patient enters the study on the first day) and unequal follow-up on subjects.

Statistical methods appropriate for event time data, survival analyses, do not discard the right-censored observations. Instead, the methods account for the knowledge that the event did not occur in a subject up to the censoring time. Survival methods include life table analysis, Kaplan-Meier survival curves, logrank and Wilcoxon tests, and proportional hazards regression (more discussion on these in a later lesson).

In order to conduct event-time analyses, two measurements must be recorded, namely, the follow-up time for a subject and an indicator variable as to whether this is an event time or a censoring time. These statistical methods assume that the censoring mechanisms and the event are independent. If this is not the case, e.g., patients have a tendency to be censored prior to the occurrence of the event, the event rate will be underestimated.

When the event of interest is death, it is common to examine two different endpoints, namely, death from all causes and death primarily due to the disease.

At first glance, death primarily due to the disease appears to be the most appropriate. It is, however, susceptible to bias because the assumption of independent causes of death may not be valid. For example, subjects with a life-threatening cancer are prone to death due to myocardial infarction. It can also be very difficult to determine the exact cause of death.