Definition of Event History Analysis
Event History Analysis, also known as survival analysis or duration analysis, is a statistical method used to analyze the timing or duration until an event of interest occurs. It allows researchers to analyze longitudinal data with time-to-event outcomes by accounting for censoring, meaning cases where the event of interest does not occur during the observation period or is not observed.
Key Components of Event History Analysis:
- Time-to-event Outcome: Event history analysis focuses on a specific event or outcome of interest that has not yet occurred for all or some of the subjects in a dataset. It examines the time it takes for this event to happen.
- Censoring: Censoring occurs when the event of interest is not observed for some individuals within the study. There are different types of censoring, including right censoring (event does not occur within the observation period), left censoring (event has already occurred before the start of the study), and interval censoring (event occurs between specific time intervals).
- Survival Function: The survival function, also known as the survival curve, is an essential component of event history analysis. It represents the probability that an individual will survive or remain event-free beyond a specific time point.
- Hazard Function: The hazard function describes the probability of the event occurring at a given time, conditional on surviving up to that point. It is useful to ascertain how the risk of the event changes over time.
- Modeling Techniques: Event history analysis employs various modeling techniques, such as Kaplan-Meier estimation, Cox proportional hazards model, and parametric survival models (e.g., Weibull, exponential, or log-normal distribution models). These models allow researchers to estimate the effects of covariates on the timing or duration of the event.
- Covariates: Covariates are independent variables that can be included in event history analysis models to examine their impact on the event of interest. They help understand the relationship between different factors and the timing or likelihood of the event occurring.
- Applications: Event history analysis finds applications in various fields, including epidemiology, social sciences, economics, demography, and medical research. It is particularly useful when studying time-to-event outcomes like survival times, time to failure, time to event recurrence, or event durations.
By utilizing event history analysis, researchers can gain insights into the factors influencing the timing or duration of events, enabling a better understanding of important phenomena with time-related outcomes.