Survival Analysis Part I: Basic concepts and first analyses

By T. Clark, M. Bradburn, S. Love, Douglas G. Altman. British Journal of Cancer, 2003

986 citations recorded by the source corpus.

Survival analysis is a collection of statistical procedures for data analysis where the outcome variable of interest is time until an event occurs. Because of censoring–the nonobservation of the event of interest after a period of follow-up–a proportion of the survival times of interest will often be unknown. It is assumed that those patients who are censored have the same survival prospects as those who continue to be followed, that is, the censoring is uninformative. Survival data are generally described and modelled in terms of two related functions, the survivor function and the hazard function. The survivor function represents the probability that an individual survives from the time of origin to some time beyond time t. It directly describes the survival experience of a study cohort, and is usually estimated by the KM method. The logrank test may be used to test for differences between survival curves for groups, such as treatment arms. The hazard function gives the instantaneous potential of having an event at a time, given survival up to that time. It is used primarily as a diagnostic tool or for specifying a mathematical model for survival analysis. In comparing treatments or prognostic groups in terms of survival, it is often necessary to adjust for patient-related factors that could potentially affect the survival time of a patient. Failure to adjust for confounders may result in spurious effects. Multivariate survival analysis, a form of multiple regression, provides a way of doing this adjustment, and is the subject the next paper in this series.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9e0756d89b42c7f314bdc0c7d42c070c7b3f6fbc

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Citations in the corpus

COVID-19 lockdown measures and travel behavior: The case of Thessaloniki, Greece (2021, background): “…model is applied under the assumption that the calculated hazard ratios are constant over time (proportional hazards assumption), as well as the assumption that, apart from the explanatory variables, all other effects on duration are considered equal (Clark et al., 2003; Kleinbaum and Klein, 2012).”
An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics (2018, methodology): “Assessment of clinical endpoints of OS, PFI, DFI, DSS—Time-to-event studies must have sufficient follow-up to capture enough events and thereby ensure there is sufficient power to perform appropriate statistical tests (Clark et al., 2003).”
Survival Analysis Part II: Multivariate data analysis – an introduction to concepts and methods (2003, positive): “Examples of survival and hazard functions derived from some of these parametric models were presented in the previous paper (Clark et al , 2003).”
Global vascular guidelines on the management of chronic limb-threatening ischemia (2019, background): “These estimates, however, are based on specific assumptions and are therefore vulnerable to attrition bias.(635,636) Consequently, incomplete follow-up might lead to relevant but easily missed false outcome estimates that can affect study groups differently.”
Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration (2012, methodology): “More extensive discussions of statistical analysis methods for binary outcome and for survival data can be found elsewhere [74,102,114-125].”