Sentence examples for strategies for missing data from inspiring English sources

A series of planned imputation strategies for missing data will be used in examining outcomes for missing data.

We applied a Last Observation Carried Forward (LOCF) strategy for missing data after the first round that is to say that, if an expert did not answer the second round, we considered his answers at the first round.

Descriptive statistics were generated for reported and actual data analytic strategy and for missing data strategy.

It seems important to point out that these results should not eclipse the strategy used for missing data.

Genomic selection models for parent selection were constructed from phenotypic data of their dense-planted half-sib progenies, assessing their selection accuracy for different SNP calling procedures, strategies and algorithms for missing data imputation, and prediction models.

Only 1 study reported adequate power to identify treatment effect (item 20) [ 32] and only 2 studies reported adequate strategies to account for missing data in their analyses (item 22) [ 30, 31].

The Trial Steering Committee and Data Monitoring Committee agreed these conventions in a detailed strategy, and reasons for missing data were categorised by a member of the Trial Management Group blinded to participants' allocated group wherever possible.

A multiple imputation strategy was adopted for missing data on job category (7% in the mortality study; 5% in the morbidity study) using 20 imputed datasets in both the regression models [ 25, 26] under the reasonable assumption of their missing at random (MAR).

Since an ideal ITT requires a complete set of data [ 89], multiple imputation (or a similar strategy) will be used for missing data, if possible.

We will use imputation strategies that we have applied previously for missing data at the individual level.

For each reference population, genomic prediction using these models was explored for 48 data sets deriving from the combination of two SNP calling strategies, four imputation algorithms and six thresholds for missing data.