Censoring

Right censoring indicates that an event had not occurred by the time observation ended, so the recorded information provides a timing boundary rather than an exact event time. Survival analysis uses that boundary when estimating event-time patterns and risks. This prevents the study endpoint from being incorrectly treated as the event time itself.

When a measurement falls below or above a detection limit, its exact value is unavailable, but its position relative to that threshold remains known. Censoring methods preserve this directional information instead of replacing the observation with an ordinary measured value. The resulting analysis can therefore account for incomplete measurements while estimating the underlying distribution.

Interval censoring records that an event occurred somewhere between two observed times, even though the precise time is unknown. Those two boundaries constrain the possible event time and can be incorporated into likelihood-based models. This approach uses the available timing information rather than discarding the observation or assigning an unsupported exact time.

Treating a censored value as exact ignores the information that only a range or timing constraint is known. That can distort estimates of event times, distributions, or risks because the recorded value does not represent the unobserved quantity itself. Statistical methods designed for censoring retain the constraint and produce more reliable summaries and comparisons.

First, identify what is known about each observation, such as an event boundary, a detection threshold, or an interval of possible timing. Next, represent that constraint explicitly in a survival-analysis or likelihood-based model. The selected model can then estimate event times, distributions, risks, or treatment effects while using both observed and censored information.

Survival analysis is useful when the primary outcome concerns the timing of an event, particularly when a study ends before all events occur. Likelihood-based models provide a broader way to incorporate constraints from incomplete observations, including timing intervals or detection limits. Together, these approaches support estimation of risks, distributions, event times, and treatment effects.