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Finally, in Section 5 we discuss the use of synthetic data via the simple class imputation methods, the multiple imbalances present at the joined datasets, a comparison considering the modal origin of the highlighted features, and biological implication of the proposed biomarker sets.
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Figure 1 shows the number of validation animals per class of imputation error rate, for method C and datasets 1 and 2. The distribution was skewed, with the largest number of animals in the lower imputation error rate classes.
To deal with the problems of non-response, one-parameter classes of imputation techniques have been suggested and their corresponding point estimators have been proposed.
For each class, the average imputation error rate was computed.
Supplementary Figure S1 (PDF) Flow diagram showing inclusion in early-life overweight latent class analysis and multiple imputation analysis.
As shown in Fig S1, 4,340 study members were eligible for both the longitudinal latent class analysis and multiple imputation procedures and were included in the analysis.
A flow diagram showing inclusion in the early-life (ages 2-20 years) overweight longitudinal latent class analysis and multiple imputation analysis is shown in Fig S1 (provided as online supplementary material).
Additionally, information on the sampling structure (strata, class) was used for data imputation.
All covariates, the respective outcomes, and the cluster variables school and class were included in the imputation approach.
BRFSS: Behavioral Risk Factor Surveillance System; CDC Centers for Disease Control and Prevention CIConfidenceence Interval; HRQOL: Health-Related Quality of Life; LCR: Latent Class Regression Model; MI: Multiple Imputation.
Prevalence estimates, overall and stratified by age, gender, and socio-economic class, were derived using multiple imputation and weighted logistic regression.
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