Imputation of Missing Data for Time-to-Event Endpoints Using Retrieved Dropouts.

ABSTRACT: We have explored several statistical approaches to impute missing time-to-event data that arise from outcome trials with relatively long follow-up periods. Aligning with the primary estimand, such analyses evaluate the robustness of results by imposing an assumption different from censoring at random (CAR). Although there have been debates over which assumption and which method is more appropriate to be applied to the imputation, we propose to use the collection of retrieved dropouts as the basis of missing data imputation. As retrieved dropouts share a similar disposition, such as treatment discontinuation, with subjects who have missing data, they can reasonably be assumed to characterize the distribution of time-to-event among subjects with missing data. In terms of computational intens

SUBMITTER: Wang S 

PROVIDER: S-EPMC10764582 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature