Each week, we select a recently published Open Access article to feature. This week’s article comes from Statistica Neerlandica and shows how inference using a Tobit regression imputation model for reference-based sensitivity analysis with right censored log normal data can be used to ensure that the proportion of information lost due to missing data is held constant across the analysis. The authors demonstrate this result through simulation and a clinical trial case study. 

The article’s abstract is given below, with the full article available to read here.

Atkinson, A., Cro, S., Carpenter, J. R., & Kenward, M. G. (2021). Information anchored reference-based sensitivity analysis for truncated normal data with application to survival analysis. Statistica Neerlandica, 1– 24. https://doi.org/10.1111/stan.12250

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.