Abstract
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 insensitivity 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 isinformation 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.
Highlights
Carefully clinical trials are designed and planned, some outcome data are often missing
Given the increasingly prominent role of sensitivity analysis in the analysis of clinical trials, exemplified by the ICH E9 addendum published in 2019 CHMP (2019), it is important to provide methods which are easy to implement and use, but which are clinically plausible and contextually relevant to the trial team and other stakeholders
This responds to the FDA mandated report by the U.S National Research Council in 2010, which highlighted the lack of such sensitivity analysis methods NRC (2010)
Summary
Carefully clinical trials are designed and planned, some outcome data are often missing. Recently Cro, Carpenter, and Kenward (2019) proved that, at least for continuous longitudinal data, RBMI is information anchored, meaning that the proportion of information lost due to missing data is held constant across the primary and sensitivity analysis. We are using “information” in a rather specific sense, defined to be the inverse of the derived estimator of the sampling variance In their recent paper Atkinson et al showed that, counterintuitively, the empirical variance decreases as the proportion of censored data increases (columns 7 and 8 of Table 1 of reference 2), whereas information anchoring was shown to hold for Rubin’s variance estimator (albeit with simulated data). That of Cro et al, in this article we provide theoretical results showing that information anchoring holds for a reference-based sensitivity analysis with a Tobit imputation model assuming truncated normal data. Notes: Comparison of variances under J2R: A, Theoretical calculation of information anchored variance (the gold standard); B, Theoretical calculation of Rubin’s MI variance; C, Empirical estimate of information anchored variance; D, Empirical estimate of MI variance under J2R
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.