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B) the missing data pattern.
If the missing data pattern was already monotone, then this step was skipped.
The data were then imputed by assuming a monotone missing data pattern using the regression method proposed by Rubin [11].
This imputation was conducted by first imputing enough data to impose a monotone missing data pattern on the original data via a Markov Chain Monte Carlo (MCMC) algorithm.
A dataset with variables X1, X2,.., Xp has a monotone missing data pattern when Xi is missing and subsequently Xj for j>I is missing for a patient.
The second filtering step should use a moving window that is as wide as possible, depending on the drift, missing data pattern in the data, and window width of the previous filter.
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Missing data patterns and assumptions will be described, and the potential impact of alternative missing data assumptions on the primary analysis will be explored in sensitivity analyses.
Zhang and Ahn (2012) derived a closed-form sample size formula for two-sample comparisons of time-averaged responses using the generalized estimating equation (GEE) approach, which takes into account different correlation structures and missing data patterns.
For both random and chunks missing data patterns, the K-SVD and DCT present the best results.
This can be related to missing data patterns, distribution of alleles and non-syntenic association.
Marker data were simulated under the null hypothesis of no linkage or association to the observed phenotypes while retaining the same pedigree structures, maps, marker allele frequencies, and missing data patterns.
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