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

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  • Andrew Atkinson
  • Suzie Cro
  • James R. Carpenter
  • Michael G. Kenward

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 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.

Suggested Citation

  • Andrew Atkinson & Suzie Cro & James R. Carpenter & Michael G. Kenward, 2021. "Information anchored reference‐based sensitivity analysis for truncated normal data with application to survival analysis," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(4), pages 500-523, November.
    • Handle: RePEc:bla:stanee:v:75:y:2021:i:4:p:500-523
    DOI: 10.1111/stan.12250
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    References listed on IDEAS

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    1. Suzie Cro & James R. Carpenter & Michael G. Kenward, 2019. "Information‐anchored sensitivity analysis: theory and application," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 623-645, February.
    2. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    3. Suzie Cro & Tim P. Morris & Michael G. Kenward & James R. Carpenter, 2016. "Reference-based sensitivity analysis via multiple imputation for longitudinal trials with protocol deviation," Stata Journal, StataCorp LP, vol. 16(2), pages 443-463, June.
    4. Sehee Kim & Donglin Zeng & Jeremy M. G. Taylor, 2017. "Joint partially linear model for longitudinal data with informative drop-outs," Biometrics, The International Biometric Society, vol. 73(1), pages 72-82, March.
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