NDLI: Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation

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Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation

Content Provider	Scilit
Author	Jackson, Dan White, Ian R. Seaman, Shaun Evans, Hannah Baisley, Kathy Carpenter, James
Copyright Year	2014
Description	Journal: Statistics in medicine The Cox proportional hazards model is frequently used in medical statistics. The standard methods for fitting this model rely on the assumption of independent censoring. Although this is sometimes plausible, we often wish to explore how robust our inferences are as this untestable assumption is relaxed. We describe how this can be carried out in a way that makes the assumptions accessible to all those involved in a research project. Estimation proceeds via multiple imputation, where censored failure times are imputed under user-specified departures from independent censoring. A novel aspect of our method is the use of bootstrapping to generate proper imputations from the Cox model. We illustrate our approach using data from an HIV-prevention trial and discuss how it can be readily adapted and applied in other settings. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.
Related Links	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4282781/pdf
Ending Page	4694
Page Count	14
Starting Page	4681
e-ISSN	10970258
DOI	10.1002/sim.6274
Journal	Statistics in medicine
Issue Number	27
Volume Number	33
Language	English
Publisher	Wiley-Blackwell
Publisher Date	2014-07-25
Access Restriction	Open
Subject Keyword	Journal: Statistics in medicine Mathematical Social Sciences Statistics and Probability Informative Censoring Multiple Imputation Schoenfeld Residuals Sensitivity Analysis Survival Analysis
Content Type	Text
Resource Type	Article

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