Recently developed computational methods allow the imputation of human leukocyte antigen (HLA) genes using intergenic single nucleotide polymorphism markers. To improve the imputation accuracy in HLA imputation, it is essential to increase the sample size and the diversity of alleles in the reference panel. Our software, MergeReference, helps achieve this goal by providing a streamlined pipelin...

Imputation is a commonly used technique that exploits linkage disequilibrium to infer missing genotypes in genetic datasets, using a well-characterized reference population. While there is agreement that the reference population has to match the ethnicity of the query dataset, it is common practice to use the same reference to impute genotypes for a wide variety of phenotypes. We hypothesized t...

MOTIVATION Significance analysis of differential expression in DNA microarray data is an important task. Much of the current research is focused on developing improved tests and software tools. The task is difficult not only owing to the high dimensionality of the data (number of genes), but also because of the often non-negligible presence of missing values. There is thus a great need to relia...

UNLABELLED BACKGROUND Multiple imputation is becoming increasingly popular for handling missing data. However, it is often implemented without adequate consideration of whether it offers any advantage over complete case analysis for the research question of interest, or whether potential gains may be offset by bias from a poorly fitting imputation model, particularly as the amount of missing...

Missing outcome data are encountered in many clinical trials and public health studies and present challenges in imputation. We present a simple and easy to use SAS-based imputation method for missing discrete outcome data. The method is based on minimum distance between baseline covariates of those with missing data and those without missing data. The imputation algorithm, a method that may be...

We derive an estimator of the asymptotic variance of both single and multiple imputation estimators. We assume a parametric imputation model but allow for non-and semipara-metric analysis models. Our variance estimator, in contrast to the estimator proposed by Rubin (1987), is consistent even when the imputation and analysis models are misspecified and incompatible with one another.

PURPOSE Survival analysis of gastric cancer patients requires knowledge about factors that affect survival time. This paper attempted to analyze the survival of patients with incomplete registered data by using imputation methods. MATERIALS AND METHODS Three missing data imputation methods, including regression, expectation maximization algorithm, and multiple imputation (MI) using Monte Carl...

BACKGROUND Spatial analysis is increasingly important for identifying modifiable geographic risk factors for disease. However, spatial health data from surveys are often incomplete, ranging from missing data for only a few variables, to missing data for many variables. For spatial analyses of health outcomes, selection of an appropriate imputation method is critical in order to produce the most...

Title of Document: THE MISSING VALUE PROBLEM: A REVIEW AND CASE STUDY Jing Zhou, M. A., 2006 Directed By: Professor Paul J. Smith, Statistics Program, Department of Mathematics. The purpose of this thesis is to review methods of imputation and apply them to data collected by Equal Employment Opportunity Commission (EEOC). First, I discuss several imputation methods and review theory of multiple...