Sentence examples for missing values follows from inspiring English sources

MVNI – This imputation algorithm, adopted by the NORM software [ 19], assumes the complete data (observed and missing values) follows a multivariate normal distribution.

Missing data value is the absence of the data value completely at random (if missing values of any variable dose not depend on any value) or at random (if missing values in response variable does not depend on in its' own value but dependent on other variables) or may be not at random (if missing values follow some structure or model) [8].

The dataset was first filtered to remove microarray slides with >50% missing values followed by a second filter to remove gene models containing >50% missing values, resulting in a final set of 602 arrays.

Pre-processing of the 450 k data first removes any CpG sites with missing values, followed by removal of any sample where >90% CpG sites have detection P value >0.05, and any CpG sites where >75% samples have detection P value >10−5.

Lp(a) and triglycerides (which have a non-Gaussian distribution), and the binary secondary endpoints (proportion of patients with LDL-C <70 mg/dL and <100 mg/dL) will be analyzed using a multiple imputation approach for handling of missing values followed by robust regression [ 30] (for Lp(a) and triglycerides) or logistic regression (for binary endpoints).

Not all variables sum up to N = 3,220/N = 2,004 due to missing values as follows: 189/141 missings concerning marital status, 503/565 missings concerning living situation, 89/184 missings concerning employment status, 127/202 missings concerning severity of illness at admission, 270/1,264 missings concerning psychosocial functioning, 240/283 missings concerning severity of illness at discharge.

If outcome measures have missing values at follow-up or at baseline the participants will be excluded from the analysis.

However, using the GLIMMIX procedure to account for missing values at follow-up strengthens the validity of our estimates.

23 Once again, to facilitate this process, imputation, conditional on age, gender and treatment arm, was undertaken to estimate missing values at follow-up.

Imputation of missing values at follow-up were performed by adding the natural seasonal change in pain and DASH score – defined as the mean change from baseline to follow-up in REF – to the baseline value.

Because of missing values during follow-up, we confined our analyses to dietary data at baseline, a decision unlikely to have biased our results given that the Burke-type diet history is highly reproducible (12).