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Although the proportion of data missing for individual variables was generally small, this became more significant in the multivariate analysis.
Furthermore, a well-controlled prospective study would enable collection of appropriate clinical information, which was generally missing for individual patients from the published data sets used herein.
Where data were missing for individual subjects (within the "minimum dataset" constraints outlined above), median values from the final study cohort were imputed to enable multivariate analysis of the complete dataset.
We used the 26-taxon best tree inferred from the codon-based substitution model ML analysis (see supplementary fig. S6, Supplementary Material online) as a constraint tree, but with branch lengths generated in PAML, and pruned any taxa missing for individual genes.
However, because data were missing for individual risk factors, the sample size for each analysis varied from 783 – 1039, with the exception of interleukin 6 (IL-6) and insulin-like growth factor 1 (IGF-1), which were available only on a subset of the first seven communities enrolled in the study (n = 360 and 363, respectively).
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Although cognitive function is "missing" for individuals who drop out of a study, it is more appropriately described as "undefined" for people who die [ 29].
For the variables English proficiency and education, for which two versions of each variable are created depending on the Asian subgroup, the values are set to "missing" for individuals for whom the variable does not apply.
A dataset is said to have a monotone missing pattern when a measurement Y j is missing for an individual implies that all subsequent measurements Y k, k > j, are all missing for the individual.
Data were missing for one individual's MDA session due to a computer error.
Metabolite data was missing for one individual.
* Information on place of death is missing for 1 individual.
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