BACKGROUND Attrition, which leads to missing data, is a common problem in cluster randomized trials (CRTs), where groups of patients rather than individuals are randomized. Standard multiple imputation (MI) strategies may not be appropriate to impute missing data from CRTs since they assume independent data. In this paper, under the assumption of missing completely at random and covariate depen...

PURPOSE Multiple imputation (MI) has been proposed for handling missing data in cost-effectiveness analyses (CEAs). In CEAs that use cluster randomized trials (CRTs), the imputation model, like the analysis model, should recognize the hierarchical structure of the data. This paper contrasts a multilevel MI approach that recognizes clustering, with single-level MI and complete case analysis (CCA...

background policy makers need models to be able to detect groups at high risk of hiv infection. incomplete records and dirty data are frequently seen in national data sets. presence of missing data challenges the practice of model development. several studies suggested that performance of imputation methods is acceptable when missing rate is moderate. one of the issues which was of less concern...

The model-based approach to inference from multivariate data with missing values is reviewed. Regression prediction is most useful when the covariates are predictive of the missing values and the probability of being missing, and in these circumstances predictions are particularly sensitive to model misspecification. The use of penalized splines of the propensity score is proposed to yield robu...

Random Forests are commonly applied for data prediction and interpretation. The latter purpose is supported by variable importance measures that rate the relevance of predictors. Yet existing measures can not be computed when data contains missing values. Possible solutions are given by imputation methods, complete case analysis and a newly suggested importance measure. However, it is unknown t...

Businesses often need an accurate profile of their customers in order to better serve them, improve products and make advertising more effective. Unfortunately, customers do not always completely fill out the survey forms, especially those product registration cards. The forms often have multiple choice questions and customers may leave some questions blank. This paper presents a maximum likeli...

One popular method for analysing correlated binary data is Generalised Estimating Equations (GEE). It is well-known that the validity of this method in its simplest form when the data are incomplete relies on the often implausible assumption of Missing Completely at Random (MCAR). However, there are conditions under which the MCAR assumption can be relaxed to Missing at Random (MAR). Variants o...

Missing data imputation is an important research topic in data mining. Large-scale Molecular descriptor data may contains missing values (MVs). However, some methods for downstream analyses, including some prediction tools, require a complete descriptor data matrix. We propose and evaluate an iterative imputation method MiFoImpute based on a random forest. By averaging over many unpruned regres...