Missing.... presumed at random: cost-analysis of incomplete data

Missing... presumed at random: cost-analysis of incomplete data.

When collecting patient-level resource use data for statistical analysis, for some patients and in some categories of resource use, the required count will not be observed. Although this problem must arise in most reported economic evaluations containing patient-level data, it is rare for authors to detail how the problem was overcome. Statistical packages may default to handling missing data t...

Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data.

Test of homogeneity of covariances (or homoscedasticity) among several groups has many applications in statistical analysis. In the context of incomplete data analysis, tests of homoscedasticity among groups of cases with identical missing data patterns have been proposed to test whether data are missing completely at random (MCAR). These tests of MCAR require large sample sizes n and/or large ...

Regression analysis of incomplete medical cost data.

The accumulation of medical cost over time for each subject is an increasing stochastic process defined up to the instant of death. The stochastic structure of this process is complex. In most applications, the process can only be observed at a limited number of time points. Furthermore, the process is subject to right censoring so that it is unobservable after the censoring time. These special...

Analysis of missing data with random forests

imputation in missing not at random snps data using em algorithm

the relation between single nucleotide polymorphisms (snps) and some diseases has been concerned by many researchers. also the missing snps are quite common in genetic association studies. hence, this article investigates the relation between existing snps in dnmt1 of human chromosome 19 with colorectal cancer. this article aims is to presents an imputation method for missing snps not at random...