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Compensating for missing data in psychiatric surveys

Published online by Cambridge University Press:  11 October 2011

Graham Dunn
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Abstract

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Type
Editorials
Information
Epidemiology and Psychiatric Sciences , Volume 6 , Issue 3 , December 1997 , pp. 159 - 162
Copyright
Copyright © Cambridge University Press 1997

References

REFERENCES

Breslow, N.E. & Holubkov, R. (1997). Weighted likelihood, pseudo-likelihood and maximum likelihood methods for logistic regression analysis of two-stage data. Statistics in Medicine 16, 103–116.3.0.CO;2-P>CrossRefGoogle ScholarPubMed
Binder, D.A. (1983). On the variances of asymptotically normal estimators from complex surveys. International Statistical Review 51, 279–292.CrossRefGoogle Scholar
Brick, J.M. & Kalton, G. (1996). Handling missing data in survey research. Statistical Methods in Medical Research 5, 215–238.CrossRefGoogle ScholarPubMed
Clayton, D., Dunn, G., Pickles, A. & Spiegelhalter, D. (1997). Analysis of longitudinal binary data from multiphase sampling (with discussion). Journal of the Royal Statistical Society, Series B (in press).Google Scholar
Gornbein, J.A., Lazaro, C.G. & Little, R.J.A. (1992). Incomplete data in repeated measures analysis. Statistical Methods in Medical Research 1, 275–295.CrossRefGoogle ScholarPubMed
Lehtonen, R. & Pahkinen, E.J. (1995). Practical Methods for Design and Analysis of Complex Surveys. Wiley & Sons: Chichester.Google Scholar
Little, R.J.A. (1995). Modelling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association 90, 1112–1121.CrossRefGoogle Scholar
Little, R.J.A. & Rubin, D.B. (1987). Statistical Analysis With Missing Data. Wiley & Sons: New York & Chichester.Google Scholar
Pickles, A. & Dunn, G. (1998). Estimation of disease prevalence from screening data. In Encyclopedia of Biostatistics (ed. Armitage, P. and Colton, T.). Wiley & Sons: New York & Chichester.Google Scholar
Pickles, A., Dunn, G. & Vazquez-Barquero, J.L. (1995). Screening for stratification in two-phase (‘two-stage’) epidemiological surveys. Statistical Methods in Medical Research 4, 73–89.CrossRefGoogle ScholarPubMed
Reilly, M. & Pepe, M. (1997). The relationship between hot-deck multiple imputation and weighted likelihood. Statistics in Medicine 16, 5–19.3.0.CO;2-8>CrossRefGoogle ScholarPubMed
Robins, J.M., Rotnitsky, A. & Zhao, L.P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association 89, 846–866.CrossRefGoogle Scholar
Schill, W. & Drescher, K. (1997). Logistic analysis of studies with two-stage sampling: a comparison of four approaches. Statistics in Medicine 16, 117–132.3.0.CO;2-5>CrossRefGoogle ScholarPubMed
StataCorp (1997). Stata Statistical Software: Release 5.0. Stata Corporation: College Station, Texas.Google Scholar