Sentence examples for imputation as a from inspiring English sources

The present study is a two-stage analysis: we used a genome-wide genotyping array in the first set of subjects (stage 1) primarily to verify the homogeneity of our population by several statistical analysis like PCA, homogeneity test, and to perform imputation as a huge number of nucleotide variations were screened simultaneously.

In this paper, we focus on multiple imputation as a tool for treating missings in genetic data analysis and use that approach to test markers for HWE.

Although Moore et al. [ 17] recommended using multiple imputation as a more accurate data model, we chose the simple approach of eliminating all patients with missing data because of the size of our data set.

However, depending on the type and scope of any missing data, we will also explore multiple imputation as a strategy to use in conjunction with our primary analytic tools [ 61].

Although Moore et al. recommended using multiple imputation as a more accurate data model than the one we used [ 15], we chose the simple approach of eliminating all patients with missing data on important covariates because of the size of our data set.

In this study investigating methods for addressing missing data, specifically when including interactions in the analysis, we found support for building the model using an EM generated set of data and then applying multiple imputation as a robust method to address this common shortcoming in epidemiological studies.

These will include an analysis based on Last Observation Carried Forward imputation, as well as multiple imputations based on differential patterns of drop out/missing data reasons.

PMM and LRD differ from parametric imputation as follows.

Hornung and Reed [ 29] recommended this method of imputation as an accurate approach for computing means and standard deviations for variables that include nondetectable values.

Data imputation was performed using two methods, depending on whether the known clinical truth (healthy or UC) was used (supervised) or not (unsupervised) during data imputation as an input variable.

An alternative treatment for missing data, which we could have implemented as a fourth scaling approach, would have been to use imputations as a means of replacing missing information with "pseudo-information".