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Sensitivity models for missing covariates in the analysis of survival data from multiple surveys

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Hemming, Karla and Hutton, Jane (2007) Sensitivity models for missing covariates in the analysis of survival data from multiple surveys. Working Paper. Coventry: University of Warwick. Centre for Research in Statistical Methodology. Working papers, Vol.2007 (No.12).

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Abstract

Using individual patient data from five independent surveys, we evaluate regional
variations in survival in cerebral palsy. The influence of four important variables measuring disability,
which are only partially observed for many cases, are analysed. Results are compared
between a naive complete case analysis; a full likelihood model in which the covariates are
assumed to be missing at random and in which each of the binary predictor variables are modelled
as independent Bernoulli random variables; a model in which the covariates are modelled
by a conditionalwise sequence, accommodating dependencies between the likelihoods of having
various mixtures of disabilities; and a model in which the likelihood of a predictor variable
being observed is allowed to depend on the value of the covariate itself (NMAR). Fully parametric
survival regression models are used and analysis carried out in BUGS. Results suggest
that proportions recorded as having severe visual or cognitive impairments are substantially
lower than the actual proportions severely impaired. Associations between the likelihood of a
particular covariate being recorded and the likelihood of a more severe disability imply that life
expectancies for those who are very severely impaired may be up to 20% less than inferences
based on complete case analyses.

Item Type:

Working or Discussion Paper (Working Paper)

Subjects:

Q Science > QA Mathematics

Divisions:

Faculty of Science > Statistics

Library of Congress Subject Headings (LCSH):

Survival analysis (Biometry), Cerebral palsy -- Mathematical models, Missing observations (Statistics)

Series Name:

Working papers

Publisher:

University of Warwick. Centre for Research in Statistical Methodology

Place of Publication:

Coventry

Official Date:

2007

Dates:

Date	Event
2007	Published

Volume:

Vol.2007

Number:

No.12

Number of Pages:

Institution:

University of Warwick

Status:

Not Peer Reviewed

Access rights to Published version:

Open Access

References:

Baker, S. G. (1994). Regression analysis of grouped survival data with incomplete covariates:
non-ignorable missing data and censoring mechanisms. Biometrics 50, 821–826.
Baker, S. G. and N. M. Laird (1988). Regression analysis for categorical variables with
outcome subject to non-ignorable non response. Journal of the American Statistical
Association 83, 62–69.
Best, N., M. K. Cowles, and K. Vines (1997). CODA: Convergence diagnostics and
output analysis software for Gibbs sampling output, version 0.4. Downloadable from
http://www.mrc-bsu.cam.ac.uk/bugs/.
Chen, M. H., J. G. Ibrahim, and S. R. Lipsitz (2002). Bayesian methods for missing
covariates in cure rate models. Lifetime data analysis 8, 117–146.
Cho, M. and N. Schenker (1999). Fitting the log-F accelerated failure time model with
incomplete covariate data. Biometrics 55, 826–833.
Evans, P. M., S. J. W. Evans, and E. Alberman (1990). Cerebral palsy: why we must plan
for survival. Arch. Dis. Ch. 65, 1329–1333.
Hemming, K., J. L. Hutton, S. Bonellie, and J. Kurinczuk (2007). Intra uterine growth and
survival in cerebral palsy. Archives of Diseases in Childhood (in press).
Hemming, K., J. L. Hutton, A. Colver, and M. J. Platt (2005). Regional variation in survival
of people with cerebral palsy in the united kingdom. Pediatrics 116, 1383–1390.
Hemming, K., J. L. Hutton, S. V. Glinianaia, S. Jarvis, andM. J. Platt (2006). A comparison
of birthweight standards for europe. Developmental Medicine and Childhood Neurology 48,
906–912.
Hemming, K., J. L. Hutton, and P. O. D. Pharoah (2006). Long term survival for a cohort
of children with cerebral palsy. Developmental Medicine and Childhood Neurology 48,
90–95.
Herring, A. J. and J. G. Ibrahim (2001). Likelihood-based methods for missing covariates in
the Cox proportional hazards model. Journal of the American Statistical Association 96,
292–302.
Herring, A. J. and J. G. Ibrahim (2002). Maximum likelihood estimation in random effects
cure rate models with non-ignorable missing covariates. Biostatistics 3, 387–405.
Herring, A. J., J. G. Ibrahim, and S. R. Lipsitz (2002). Frailty models with missing covariates.
Biometrics 58, 98–109.
Huang, L., M.-H. Chen, and J. G. Ibrahim (2005). Bayesian analysis for generalised linear
models with non-ignorably missing covariates. Biometrics 61, 767–780.
Hutton, J. L., A. F. Colver, and P. C. Mackie (2000). Effect of severity of disability on
survival in north east England cerebral palsy cohort. Arch. Dis. Ch. 83, 468–473.
Hutton, J. L. and P. F. Monoghan (2002). Choice of parametric accelerated life and proportional
hazards models for survival data: asymptotic results. Lifetime data analysis 8,
375–393.
Hutton, J. L. and P. O. D. Pharoah (2002). Effect of cognitive, motor and sensory disabilities
on survival in cerebral palsy. Arch. Dis. Ch. 86, 84–89.
Ibrahim, J. G., S. R. Lipsitz, and M. H. Chen (1999). Missing covariates in generalised
linear models when the missing data mechanism is non-ignorable. Journal of the Royal
Statistical Society, B 61, 173–190.
Jarvis, S., S. V. Glinianaia, M. G. Torrioli, M. J. Platt, M. Miceli, P. S. Jouk, A. Johnson,
J. Hutton, K. Hemming, G. Hagberg, H. Dolk, and J. Chalmers (2003). Cerebral palsy
and intrauterine growth in single births: European collaborative study. The Lancet 362,
1106–1111.
Kwong, G. P. S. and J. L. Hutton (2003). Choice of parametric models in survival analysis:
applications to monotherapy for epilepsy and cerebral palsy. Journal of the Royal
Statistical Society: Series C (Applied Statistics) 52, 153–168.
Lipsitz, S. R. and J. G. Ibrahim (1996a). A conditional model for incomplete covariates in
parametric regression models. Biometrika 83, 916–922.
Lipsitz, S. R. and J. G. Ibrahim (1996b). Using the EM algorithm for survival data with
incomplete categorical covariates. Lifetime Data Analysis 2, 5–14.
Meng, X. and N. Schenker (1999). Maximum likelihood estimation for linear regression
models with right censored outcomes and missing predictors. Computational statistics
and data analysis 29, 471–483.
Rathouz, P. L. (2007). Identifiability assumptions for missing covariate data in failure time
regression models. Biostatistics 8, 345–356.
Rubin, D. B. (2002). Statistical analysis of missing data (second edition). Wiley interscience.
Schluchter, M. D. and K. L. Jackson (1989). Log-linear analysis of censored survival data
with partially observed covariates. Journal of the American Statistical Association 84,
42–52.
Spiegelhalter, D. J., N. G. Best, B. P. Carlin, and A. van der Linde (2002). Bayesian
measures of model complexity and fit (with discussion). Journal of the Royal Statistical
Society: Series B (Statistical Methodology) 64, 583–639.
Spiegelhalter, D. J., A. Thomas, and N. G. Best (1999). WinBUGS Version 1.2 User
Manual. Downloadable from http://www.mrc-bsu.cam.ac.uk/bugs/.
Stubbendick, A. L. and J. G. Ibrahim (2003). Maximum likelihood methods for nonignorable
missing responses and covariates in random effects models. Biometrics 59,
1140–1150.
Surman, G., S. Bonellie, J. Chalmers, A. Colver, H. Dolk, K. Hemming, A. King, J. Kurinczuk,
J. Parkes, and M. J. Platt (2006). Ukcp: a collaborative network of cerebral palsy
registers in the united kingdom. Journal of Public Health 28, 148–156.