Sentence examples for missing data defined from inspiring English sources

This software automatically deletes missing data, defined as continuous sequences of 60 consecutive epochs (i.e. 10 minutes) or more with zero counts.

To secure the accuracy of the data, we excluded cases from hospitals with high rates of missing data, defined as an average rate of missing data for variables in our model of more than 10%.

The desired joint probability may then be written as a summation over the two types of missing data defined by a local parse tree: Summation over missing data x′, π′ results in two terms.

All accelerometer data was analysed using SAS-based software (SAS Institute Inc ,Cary, NC, USA), witch automatically deletes missing data defined as continuous sequences of 10 minutes (60 consecutive epochs) or more with zero counts.

Criteria 4. Missing data defined as samples that could not be reliably intrapolated from the standard curve.

The 8- and 13-taxon data sets differ in the number of sites containing missing data (defined throughout as either gaps or ambiguous character states).

To address the possibility that outcome data may have been missing not at random (MNAR), we stratified the analysis by the extent of missing data, defining strata as follows: (1) those with any missing data due to medical reasons and (2) those with complete data or those with missing data due to non-medical reasons.

Seven steps to minimizing the amount of missing data are defined as documentation, training, monitoring reports, patient contact, data entry and management, pilot studies, and communication.

We then performed filtering; first, in order to avoid inter-population biases in missing data, we defined a threshold of at least three genotypes called per population sample.

Women with missing mood data, defined as one or more missing items (n = 390), and the 68 women who self-identified as having a history of a diagnosis of depression but scored below a 16 on the CES-D were excluded from this analysis.

It is, however, worth mentioning some factors that increase the likelihood of poor performance in a validation study: stepwise selection of variables from a large number of candidate variables with a small number of events, data-dependent cutpoint selection for continuous predictors, extensive missing data, a differently defined endpoint.