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Analysis of longitudinal binary data from multiphase sampling

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  • David Clayton
  • David Spiegelhalter
  • Graham Dunn
  • Andrew Pickles

Abstract

The efficient use of surrogate or auxiliary information has been investigated within both model‐based and design‐based approaches to data analysis, particularly in the context of missing data. Here we consider the use of such data in epidemiological studies of disease incidence in which surrogate measures of disease status are available for all subjects at two time points, but definitive diagnoses are available only in stratified subsamples. We briefly review methods for the analysis of two‐phase studies of disease prevalence at a single time point, and we discuss the extension of four of these methods to the analysis of incidence studies. Their performance is compared with special reference to a study of the incidence of senile dementia.

Suggested Citation

  • David Clayton & David Spiegelhalter & Graham Dunn & Andrew Pickles, 1998. "Analysis of longitudinal binary data from multiphase sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 71-87.
    • Handle: RePEc:bla:jorssb:v:60:y:1998:i:1:p:71-87
    DOI: 10.1111/1467-9868.00109
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    Cited by:

    1. Shen-Ming Lee & T. Martin Lukusa & Chin-Shang Li, 2020. "Estimation of a zero-inflated Poisson regression model with missing covariates via nonparametric multiple imputation methods," Computational Statistics, Springer, vol. 35(2), pages 725-754, June.
    2. repec:jss:jstsof:43:i13 is not listed on IDEAS
    3. Richard M. Golden & Steven S. Henley & Halbert White & T. Michael Kashner, 2019. "Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data," Econometrics, MDPI, vol. 7(3), pages 1-27, September.
    4. James R. Carpenter & Michael G. Kenward & Stijn Vansteelandt, 2006. "A comparison of multiple imputation and doubly robust estimation for analyses with missing data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 571-584, July.
    5. Rubin Daniel B & van der Laan Mark J., 2008. "Empirical Efficiency Maximization: Improved Locally Efficient Covariate Adjustment in Randomized Experiments and Survival Analysis," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-42, May.
    6. Sophia Rabe-Hesketh & Anders Skrondal, 2007. "Multilevel and Latent Variable Modeling with Composite Links and Exploded Likelihoods," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 123-140, June.
    7. Lois G. Kim & Simon G. Thompson, 2011. "Estimation of life‐years gained and cost effectiveness based on cause‐specific mortality," Health Economics, John Wiley & Sons, Ltd., vol. 20(7), pages 842-852, July.

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