Information anchored reference‐based sensitivity analysis for truncated normal data with application to survival analysis

Atkinson, Andrew; Cro, Suzie; Carpenter, James R.; Kenward, Michael G. (2021). Information anchored reference‐based sensitivity analysis for truncated normal data with application to survival analysis. Statistica Neerlandica, 75(4), pp. 500-523. Wiley 10.1111/stan.12250

Preview

Text stan.12250.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (527kB) | Preview

The primary analysis of time-to-event data typically makes the censoring at random assumption, that is, that—conditional on covariates in the model—the distribution of event times is the same, whether they are observed or unobserved. In such cases, we need to explore the robustness of inference to more pragmatic assumptions about patients post-censoring in sensitivity analyses. Reference-based multiple imputation, which avoids analysts explicitly specifying the parameters of the unobserved data distribution, has proved attractive to researchers. Building on results for longitudinal continuous data, we show that inference using a Tobit regression imputation model for reference-based sensitivity analysis with right censored log normal data is information anchored, meaning the proportion of information lost due to missing data under the primary analysis is held constant across the sensitivity analyses. We illustrate our theoretical results using simulation and a clinical trial case study.

Interest & Impact

Downloads

66 since deposited on 16 Dec 2021
37 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item