BACKGROUND Multiple imputation is a commonly used method for handling incomplete covariates as it can provide valid inference when data are missing at random. This depends on being able to correctly specify the parametric model used to impute missing values, which may be difficult in many realistic settings. Imputation by predictive mean matching (PMM) borrows an observed value from a donor wit...

managing Multiple imputation can improve data quality.

In randomized trials, it is typical that a number of outcome variables are collected at each follow-up time. Sometimes, a composite outcome may be of scientific interest. A composite outcome is composed as a function of several patient-specific outcomes and could measure, for example, improvement or deterioration in the condition of a patient. Multiple imputation is one method for handling pati...

We propose a non-parametric multiple imputation scheme, NPMLE imputation, for the analysis of interval censored survival data. Features of the method are that it converts interval-censored data problems to complete data or right censored data problems to which many standard approaches can be used, and that measures of uncertainty are easily obtained. In addition to the event time of primary int...

in this study, effect of two genotype imputation strategies, relatedness between reference panel and test populations and minor allele frequency on imputation error rate were examined with using a stochastic simulated population. reference panel and test populations were composed of 1,000 and 500 individuals, respectively. individuals in the reference panel were genotyped with using a high and ...

Functional trait databases are powerful tools in ecology, though most of them contain large amounts of missing values. The goal of this study was to test the effect of imputation methods on the evaluation of trait values at species level and on the subsequent calculation of functional diversity indices at community level using functional trait databases. Two simple imputation methods (average a...

Sir, In a recent publication in Brain, Jack Jr et al. (2010) reported on the value of hippocampal atrophy and amyloid-b measures in predicting conversion from mild cognitive impairment to Alzheimer’s disease. The authors used data from 218 subjects in the Alzheimer’s Disease Neuroimaging Initiative with mild cognitive impairment, who had a measure of amyloid-b either through CSF amyloid-b42 or ...

The appropriate choice of a method for imputation of missing data becomes especially important when the fraction of missing values is large and the data are of mixed type. The proposed dynamic clustering imputation (DCI) algorithm relies on similarity information from shared neighbors, where mixed type variables are considered together. When evaluated on a public social science dataset of 46,04...

Missing data frequently complicates data analysis for scientific investigations. The development of statistical methods to address missing data has been an active area of research in recent decades. Multiple imputation, originally proposed by Rubin in a public use dataset setting, is a general purpose method for analyzing datasets with missing data that is broadly applicable to a variety of mis...