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The multiple imputation described in Rubin ([1987]) has been routinely used to handle missing data.
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We elected to use the Amelia II package[2] of R[11], a multiple imputation method described in depth in a report entitled "What to do About Missing Values in Time Series Cross-Section Data", available at http://gking.harvard.edu/amelia/.edu/amelia/
Separate estimates of observed counts, expected counts and Poisson regression parameters were obtained for each imputed CTS dataset and combined using multiple imputation methods described in Appendix 1.
(Methods used for multiple imputation are described in online-only Data Supplement Methods III).
With the use of multiple imputation, we described the course of self-rated health using the short form 12-item survey's mental component summary (MCS) and physical component summary (PCS) and analyzed adjusted differences by sex, age, educational level, indication for PCI, and left ventricular ejection fraction along with an analysis of decrease in health status.
Methods for the supplementary multiple imputation analyses are described in an online appendix.
Missing data, not missing at random, will be addressed using standard multiple imputation techniques as described by Rubin.
Missing covariate data were replaced using multiple imputation (imputed datasets: N=30) applying multivariate chained equations as described for use in the Cox model.
We used multiple imputation in Stata as described previously 26 to impute several data at baseline: math score (n=1), outmigration of father (n=24), outmigration of mother (n=41), family wealth (n=145), education level of father (n=14), education of mother (n=23), and awareness of being myopic (n=20).
Missing cost and effect data are imputed using multiple imputation [ 54], as previously described.
In previous work (Graffelman et al. 2013) we described multiple imputation for inference on HWE based on the inbreeding coefficient.
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