Abstract
In this paper we investigated the use of attrition weights to cope with non-response when selecting graphical chain models for longitudinal data. We proposed a parametric bootstrap approach to account for the extra variability introduced by the estimation of the weights and compared this with results using standard test procedures.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Agresti A (2002). Categorical data analysis. Wiley, Hoboken
Agresti A and Yang MC (1987). An Empirical investigation of some effects of sparseness in contingency tables. Comput Stat Data Anal 5: 9–21
Asmussen S and Edwards D (1983). Collapsibility and response variables in contingency tables. Biometrika 70: 567–578
Borgoni R, Berrington A, Smith PWF (2004) Constructing and fitting graphical models to longitudinal data with attrition. University of Southampton Statistical Sciences Research Institute Methodological Working Paper no. 04/05
Clogg CC and Eliason SR (1987). Some common problems in log-linear analysis. Sociol Methods Res 16: 226–239
Cox DR and Wermuth N (1996). Multivariate dependencies: models. Analysis and interpretation. Chapman and Hall, London
Davison AC and Hinkley DV (1997). Bootstrap methods and their application. Cambridge University Press, Cambridge
Edwards D (1989) Discussion on mixed graphical association models (by S.L Lauritzen). Scand J Stat 16:301
Edwards D (1996). Linkage analysis using loglinear models. Comput Stat Data Anal 13: 281–290
Edwards D (2000). Introduction to graphical modelling, 2nd edn. Springer, New York
Ferri E, Bynner J, Wadsworth M (2003) Changing Britain, changing lives: three generations at the turn of the century. University of London, Institute of Education
Haberman SJ (1977). Log-linear models and frequency tables with small expected cell counts. Ann Stat 5: 1148–1169
Lauritzen SL (1996). Graphical models. Oxford University Press, Oxford
Little RJA (1986). Survey nonresponse adjustments. Int Stat Rev 54: 139–157
Little RJA and Rubin DB (2001). Statistical analysis with missing data. Wiley, Hoboken
Magadi M, Diamond I, Madise N and Smith PWF (2004). Pathways of the determinants of unfavourable birth outcomes in Kenya. J Biosocial Sci 36: 153–176
Mohamed WN, Diamond I and Smith PWF (1998). The determinants of infant mortality in Malaysia: a graphical chain modelling approach. J R Stat Soc Ser A 161: 349–366
Rosenbaum PR and Rubin DB (1985). Constructing a control group using multivariate matched sampling incorporating the propensity score. Am Stat 39: 33–38
Ruggeri M, Biggeri A, Rucci P and Tansella M (1998). Multivariate analysis of outcome of mental health care using graphical chain models. The South-Verona Outcome Project 1. Psychol Med 28: 1421–1431
Whittaker J (1990). Graphical models in applied multivariate statistics. Wiley, Chichester
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Borgoni, R., Smith, P.W.F. & Berrington, A.M. Handling the effect of non-response in graphical models for longitudinal data. Stat Methods Appl 18, 109–123 (2009). https://doi.org/10.1007/s10260-008-0093-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10260-008-0093-9