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Informative censoring in transplantation statistics

Record type: Thesis (Doctoral)

Observations are informatively censored when there is dependence between the time to the event of interest and time to censoring. When considering the time to death of patients on the waiting list for a transplant, particularly a liver transplant, patients that are removed for transplantation are potentially informatively censored, as generally the most ill patients are transplanted. If this censoring is assumed to be non-informative then any inferences may be misleading.

The existing methods in the literature that account for informative censoring are applied to data to assess their suitability for the liver transplantation setting. As the amount of dependence between the time to failure and time to censoring variables cannot be identified from the observed data, estimators that give bounds on the marginal survival function for a given range of dependence values are considered. However, the bounds are too wide to be of use in practice. Sensitivity analyses are also reviewed as these allow us to assess how inferences are affected by assuming differing amounts of dependence and whether methods that account for informative censoring are necessary. Of the other methods considered IPCW estimators were found to be the most useful in practice.

Sensitivity analyses for parametric models are less computationally intensive than those for Cox models, although they are not suitable for all sets of data. Therefore, we develop a sensitivity analysis for piecewise exponential models that is still quick to apply. These models are flexible enough to be suitable for a wide range of baseline hazards. The sensitivity analysis suggests that for the liver transplantation setting the inferences about time to failure are sensitive to informative censoring. A simulation study is carried out that shows that the sensitivity analysis is accurate in many situations, although not when there is a large proportion of censoring in the data set. Finally, a method to calculate the survival benefit of liver transplantation is adapted to make it more suitable for UK data. This method calculates the expected change in post-transplant mortality relative to waiting list mortality. It uses IPCW methods to account for the informative censoring encountered when estimating waiting list mortality to ensure the estimated survival benefit is as accurate as possible.

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Citation

Staplin, Natalie (2012) Informative censoring in transplantation statistics University of Southampton, School of Mathematics, Doctoral Thesis , 226pp.

More information

Published date: March 2012
Organisations: University of Southampton, Social Sciences

Identifiers

Local EPrints ID: 341789
URI: http://eprints.soton.ac.uk/id/eprint/341789
PURE UUID: 2344d1ce-29b5-4d43-be3f-3e1f448570bf
ORCID for Paul Roderick: ORCID iD orcid.org/0000-0001-9475-6850

Catalogue record

Date deposited: 12 Nov 2012 14:39
Last modified: 18 Jul 2017 05:32

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Contributors

Author: Natalie Staplin
Thesis advisor: Alan Kimber
Thesis advisor: David Collett
Thesis advisor: Paul Roderick ORCID iD

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