Survival Analysis

In a large proportion of observational studies, including prospective or retrospective longitudinal cohort studies, the outcome of primary interest is the time to the occurrence of a specific event or endpoint, such as death or hospitalization. Because often the events of interest are observable for only some study participants, specialized analytical methods are required to deal with ‘censored observations’, i.e. those subjects who had no event until the end of their follow-up. The development of statistical methods able to handle such censored time-to-event outcomes is the main focus of survival analysis, which is increasingly applied in longitudinal studies across a broad spectrum of empirical sciences. Whereas some methods developed for other types of outcomes, such as continuous, normally distributed or binary variables, can be easily adapted to the analyses of censored data, several important conceptual and analytical challenges are specific to survival analysis. Accordingly, this is a very active but also a rather specialized area of statistical research. Indeed, the end-users (empirical researchers and data analysts) are often either unaware of new survival analytical methods, typically published in statistical journals, or unable to understand why, when and how these methods should be implemented. As a result, a vast majority of real-life applications of survival analysis use only a few, very popular statistical methods, such as Kaplan-Meier curves, log rank test, or Cox proportional hazards (PH) model (Cox 1972), which is employed in >90% of the multivariable time-to-event analyses of clinical data (Altman et al 1995). Yet many end-users may not understand the important assumptions on which such conventional methods rely and do not recognize the impact of violations of these assumptions, and most do not know what alternative, more robust methods can be employed in such cases.

TG8 attempts to help the understanding of the analytical issues, frequently encountered in real-life applications of survival analysis, and provide practical guidance regarding the validated methods and the user-friendly software that can be used to address these issues. To this end, we will draw on both earlier published reviews of the main issues and methods of survival analysis (e.g., Andersen et al 2012, Clark et al 2003, Clayton 1988) and expertise of the TG8 members.