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Eighteen individual questionnaires sheets (FGAS: 9, GSRS: 1, HADS: 5 and HI: 3) had to be excluded from the analysis due to missing values in proportions that did not allow for imputation.
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The wealth of data on participants from previous assessments in ALSPAC allowed for imputation of missing data using a rich list of relevant variables.
In total, we simulated a region of 1,050 kb, including a 50 kb gene and 500 kb up- and down-stream to allow for an imputation buffer to improve accuracy by avoiding edge effects and taking advantage of the expected long-range linkage disequilibrium (LD) with rare variants [ The International HapMap Consortium, 2007.
We implemented multiple imputation using the sequential regression multivariate imputation approach (SRMI), also referred to as Fully Conditional Specification (FCS) and Multiple Imputation by Chained Equations (MICE): this method allows for efficient imputation by fitting a model to each variable, conditional on all others, and imputing one variable at a time [ 50, 51].
‡Unadjusted for covariates, but allowing for multiple imputation.
A future modification of the multinomial logit model that allows for the imputation of all three genotypes would form a valuable extension of the work presented here.
The limited recombination in F2s allows for precise imputation of missing SNPs within long range regions using the bin-map strategy.
Subsequently we carried out a sensitivity analysis to determine the influence of missing data on smoking status on the study conclusions, varying the strength of association between smoking status and "missingness" (that is, whether data are missing), and using multiple imputation to allow for variation in individual sampling and imputation.
In order for Rubin's rules to produce valid results, multiple imputation must allow for uncertainty in the parameters of the imputation model.
For these variables, to allow for the missing data, multiple imputations (10 imputations) were carried out using the Multiple Imputations by Chained Equations with predicted mean matching.
One of its main advantages is that it produces reliable estimates of standard errors: single imputation methods do not allow for the additional error introduced by imputation.
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