Assessing Differences in Survival Distributions in Complex Survey Data: A Comparison Study

When analyzing time-to-event data, the Cox proportional hazards model is often used to compare the relative risk of a given outcome (e.g., mortality) for various characteristics. Although several statistical methods have been developed for analyzing complex survey data emanating from population-level health surveys, methods for analyzing time-to-event data from such surveys have not progressed as rapidly. For comparing differences in survival between groups, log rank and linear rank tests are frequently utilized. As an initial step to facilitate these comparisons for complex survey data, Rader (2014) proposed an approach to compare censored survival outcomes for two groups for complex surveys based on linear rank tests (log-rank, Peto-Peto, and Harrington-Fleming ). While this approach is limited to two groups, this method does attempt to control for confounding effects through the use of propensity scores (as opposed to using a stratified test). In addition, Ritter (2021) adapted the use of non-parametric k-sample tests developed by Gray (1988) to time-to-event data from complex surveys for comparing survival distributions in the presence of competing risks. In the absence of competing risks, Gray's test simplifies to the log-rank test. Because the methods proposed by Rader (2014) and Ritter (2021) have not been previously compared, the primary aim of this presentation is to contrast these approaches for comparing survival distributions in time-to-event data collected from complex health surveys, including differences in the use of covariates and software implementation. These comparisons will be based on a simulation approach utilizing methods developed by Rader (2014) to simulate clustered survival outcomes with a general covariance structure based on a set of covariates. These findings will be useful for extending the analytic uses for time-to-event complex survey data.

Survival analysis

Complex surveys

Log-rank test

Grays test

Score test 

Practice and Applications

Symposium on Data Science and Statistics (SDSS) 2024