BACKGROUND AND OBJECTIVES Baseline creatinine (BCr) is frequently missing in AKI studies. Common surrogate estimates can misclassify AKI and adversely affect the study of related outcomes. This study examined whether multiple imputation improved accuracy of estimating missing BCr beyond current recommendations to apply assumed estimated GFR (eGFR) of 75 ml/min per 1.73 m(2) (eGFR 75). DESIGN,...

Web-based Data Imputation enables the completion of incomplete data sets by retrieving absent field values from the Web. In particular, complete fields can be used as keywords in imputation queries for absent fields. However, due to the ambiguity of these keywords and the data complexity on the Web, different queries may retrieve different answers to the same absent field value. To decide the m...

Virtually all surveys encounter some level of item nonresponse. To address this potential source of bias, practitioners often use imputation to replace missing values with valid values through some form of stochastic modeling. In order to improve the reliabilities of such models, imputation classes are formed to produce homogenous groups of respondents, where homogeneity is measured with respec...

Background Genome-wide association studies and genomic predictions are thought to be optimized by using whole-genome sequence (WGS) data. However, sequencing thousands of individuals of interest is expensive. Imputation from SNP panels to WGS data is an attractive and less expensive approach to obtain WGS data. The aims of this study were to investigate the accuracy of imputation and to provide...

in interventional or observational longitudinal studies, the issue of missing values is one of the main concepts that should be investigated. the researcher's main concerns are the impact of missing data on the final results of the study and the appropriate methods that missing values should be handled. regarding the role and the scale of the variable that missing values have been occurred and ...

An important source of bias in structural equation modeling (SEM) employing the partial least squares method (PLS) is missing data. Deletion methods, such as listwise and pairwise deletion, have traditionally been used to deal with missing data. These methods are perceived as leading to selective loss of data and significant related biases. Missing data imputation methods, on the other hand, do...

Genotype imputation methods have become increasingly popular for recovering untyped genotype data. An important application with imputed genotypes is to test genetic association for diseases. Imputation-based association test can provide additional insight beyond what is provided by testing on typed tagging SNPs only. A variety of effective imputation-based association tests have been proposed....

Genotype imputation is a vital tool in genome-wide association studies (GWAS) and meta-analyses of multiple GWAS results. Imputation enables researchers to increase genomic coverage and to pool data generated using different genotyping platforms. HapMap samples are often employed as the reference panel. More recently, the 1000 Genomes Project resource is becoming the primary source for referenc...

Sample surveys typically gather information on a sample of units from a finite population and assign survey weights to the sampled units. Survey frequently have missing values for some variables for some units. Fractional regression imputation creates multiple values for each missing value by adding randomly selected empirical residuals to predicted values. Fractional imputation methods assign ...

There are many competing computational algorithms in multiple imputation. To this date, however, it is unknown which of these algorithms outperforms the others under what circumstances. In this paper, we describe the mechanisms of various multiple imputation algorithms and compare their performance in a variety of situations to determine which algorithm is best suited to the imputation of missi...