Survival Analysis

Survival analysis is a statistical method used to analyze time-to-event data. While death (overall survival) is a common event studied, survival analysis can also be applied to other events such as disease progression, relapse, or the occurrence of a specific event in prevention studies. It is particularly prevalent in oncology, where overall survival and time-to-progression are key endpoints in clinical trials. Survival analysis forms the basis for many economic evaluations, especially using partitioned survival models.

Key aspects of survival analysis include:

– Time-to-Event Data: Focuses on the time until a specified event occurs, such as death or disease progression.

– Longitudinal Consideration: Health outcomes are considered over time, providing a more dynamic view than cross-sectional analyses.

– Kaplan-Meier Method: A non-parametric approach to estimating survival over the study period, accounting for censored data (e.g., patients lost to follow-up).

– Parametric Models: Uses statistical distributions (e.g., Weibull, Gompertz, exponential) to model survival data and extrapolate beyond observed data. This is crucial for economic modeling where long-term predictions are needed.

– Economic Evaluation: Survival analysis allows for the calculation of economic endpoints like life-years gained and quality-adjusted life years (QALYs), which are integral to cost-effectiveness analyses.

Survival analysis is essential in health economics and clinical research, providing robust methods to analyze and interpret time-to-event data, which is crucial for understanding treatment effects and informing decision-making processes.