Sentence examples for missing data mechanisms from inspiring English sources

Simulation studies provide a framework to compare the performance of different approaches for handling missing data with a variety of missing data mechanisms, as the true value is known.

The aim of this study was to implement and compare methods for handling missing covariate data in nonlinear mixed effects modelling under different missing data mechanisms.

36 The potential effects of missing data will be explored under various models for nonignorable missing data mechanisms and through multiple imputation models under ignorable missing data assumptions.

The potential effects of missing data will be explored under various models for nonignorable missing data mechanisms, and through multiple imputation models under ignorable missing data assumptions [ 51].

There are two types of missing data mechanisms that can be considered ignorable.

This paper begins with a review of the hierarchy of missing data mechanisms and their relationship to likelihood-based methods, a review that is necessary not just for likelihood-based methods, but also for multiple-imputation methods.

Missing data mechanism, which is the process that governs the probability of being missing, can be classified into three categories [ 23]: Missing Completely At Random (MCAR): the probability of missingness depends neither on the observed data nor on missing data.

The missing data mechanism is modeled by nonparametric prior processes.

A more realistic situation is one in which the missing data mechanism is non‐ignorable.

This missing data mechanism is the most difficult to treat as it renders the usual imputation methods meaningless.

An ignorable missing data mechanism is one in which inferences are not affected by the process that generated the missing data.