Sentence examples for missing data analyses from inspiring English sources

This article explains the theoretical underpinnings of missing data analyses, gives an overview of traditional missing data techniques, and provides accessible descriptions of maximum likelihood and multiple imputation.

Full information maximum likelihood (FIML) estimation, integrating missing data analyses and parameter estimation under the missing at random assumption, was used to handle partially missing data (Little and Rubin 2014).

With regard to the missing data analyses in this study, it is possible that those with missing values in the outcome-variables would be more likely to be willing to pay generally and willing to pay a higher amount according to the specific co-variables detected in the regression analyses.

The missing data analyses were conducted using a logistic regression model.

Missing data analyses revealed that they were missing completely at random (MCAR), therefore we did not use any imputation method.

Because of missing data, analyses were only available for subsets of patients: 145 of 199 patients presented EGFR-positive tumors; 105 of 202 patients presented p53-positive tumors; and 65 of 163 patients presented MGMT-positive tumors.

The flow of patients through various analyses can be complicated in prognostic studies, particularly when there are missing data, multiple analyses or subgroup analyses.

To evaluate the effects of missing data on analyses of data from trauma databases, and to verify whether commonly used techniques for handling missing data work well in theses settings.

Because of the small percentage of missing data, all analyses were undertaken with complete data only.

Because of missing data, statistical analyses were performed on 133 patients.

Characteristics of the patients with missing data for analyses did not differ significantly from analyzable patients.