Sentence examples for missing data focused from inspiring English sources

Initial work by Stamatakis and Ott (2008b) on methods for efficiently computing the likelihood on phylogenomic alignments with missing data focused on computing the likelihood and optimizing branch lengths on a single, fixed tree topology using pointer meshes.

Previous reviews of missing data have generally focused on documenting the handling and reporting of missing outcome data in randomised clinical trials or missing covariate measures in cohort studies with multiple waves of data collection.

Previous studies on missing data [ 8- 11] have focused on the impact of missing information on study results and the methods to treat the missing variables.

The handling of item-level missing data is a focus of statistical consideration in clinical trial analyses; in this study, item-level missing data were treated as a random effect for each of the measures and an additional pattern analysis was conducted, to account for this effect.

13– 15 In recent decades, different approaches have been developed to deal with missing data, but we will focus on imputation.

Recent attempts to handle the corruptions by noise, artifact, and missing data in physiological signals focus on using redundant measurements, and fusing data from multiple sensors.

One explanation for the missing data is reluctance of caregivers to focus on pain in women who are not asking for pain relief.

A great deal of recent methodological research has focused on two modern missing data analysis methods: maximum likelihood and multiple imputation.

We do not show parameter estimates of the outcome model (1) because we do not wish to distract the reader from the investigation of the missing data mechanism, which provides our focus.

In spite of the increasing focus on missing data theory and methodology, it is still not often implemented in research beyond reporting missing percentages and correlations between missingness and variables in the study (McKnight et al., 2007; Schafer and Graham, 2002).

In this paper we focus on missing data in exposure measures that are made repeatedly in a cohort study because studies of this type (in which the outcome is often a single episode of disease or death obtained from a registry and therefore, known for all participants) are common and increasingly important in chronic disease epidemiology.