Year: 2026
Type: article
Abstract: When observation of recurrent events ends before the planned follow-up time, conclusions may depend on untestable assumptions about outcomes during the unobserved period. This is especially challenging for recurrent outcomes such as hospitalizations or infections, for which each event creates a temporary period during which a new event cannot occur and at-risk time stops accruing. In such settings, multiple imputation must jointly determine future event counts and future at-risk exposure. This paper proposes an off-risk-aware extension of controlled multiple imputation that simulates post-dropout trajectories in calendar time while allowing exposure to accrue only during on-risk intervals. Standard assumptions, including missing at random and jump-to-reference, are implemented by modifying only the post-dropout rate assignment while leaving the exposure-generation mechanism unchanged. In simulation, missing-at-random imputation closely matched direct likelihood, whereas jump-to-reference progressively attenuated the treatment effect toward the null as missingness increased. In the rhDNase illustration, missing-at-random imputation again yielded results very close to direct likelihood, while jump-to-reference produced only modest attenuation. The method provides a practical sensitivity-analysis framework for trials in which recurrent events temporarily remove participants from risk, helping align missing-data assumptions, outcome generation, and analysis on the at-risk time scale.
Source: Pharmaceutical Statistics
DOI: 10.1002/pst.70134
Author: Szilárd Nemes
Institution: AstraZeneca (Sweden)
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
Funder: AstraZeneca
OpenAlex ID: W7221467035