Sentence examples for missing data assumed from inspiring English sources

Third, sensitivity analyses for missing data assumed plausible arm-specific differences between responders and non-responders [32].

† Don't know answers and missing data assumed as "receipt" of process of care.

** Don't know answers and missing data assumed as "no receipt" of process of care.

The use of multiple imputation for missing data assumed that the data are missing at random (MAR).

An alternative method of dealing with unobserved smoking data is to dichotomise smoking status into current smokers and non-current smokers with missing data assumed to pertain to non-current smokers.

Analysis will be carried out in two ways: (a) through a complete-case analysis, i.e. only participants with complete outcome data will be analysed; (b) through an imputed-case analysis, i.e. the multiple imputation method will be used to impute values for missing data (assumed to be missing at random) [ 32].

This method of modeling missing data assumes that the data are missing at random and permits inclusion of all available cases, although it is not as efficient as multiple imputation procedures.

We then carried out analyses adjusting for all characteristics associated with missing data (assuming data were missing at random) and additionally adjusted for infants' sex, quintiles of socioeconomic status, and quintiles of exposure to sunlight.

For participants with missing outcomes, we used the baseline outcomes and other explanatory covariates (treatment group, sex, age, ethnicity, region, and disease duration) to impute the missing data, assuming unobserved measurements were missing at random (see appendix table A).

All analyses will use full information maximum likelihood estimation, which provides unbiased estimates and correct standard errors in the presence of missing data, assuming data loss is missing for non-ignorable reasons.

Multivariate multiple imputation, in common with other procedures for dealing with missing data, assumes similarity in the associations between the exposure and outcome in participants with and without missing data.