Suggestions(2)
Exact(3)
In such complex responses consisting of many correlated variables, substitution of missing data generally improves parameter estimation.
Conservative bias is expected from the latter, where those with missing data generally had lower socioeconomic status and thus higher rates of mental health problems.
This effect, reflecting the strong informativity of the missing data, generally enlarged the bias that was already observed except for the WORST method for which the bias was attenuated but still remained.
Similar(57)
Fifth, although the missing rate was as high as 36%, the subjects with missing data were generally comparable with those with missing data regarding sex, age, weight status, and highest parental education.
Levels of missing data were generally low (typically 4.5-6%).
Levels of missing data were generally very low across countries indicating the acceptability of the questionnaire.
In SUD treatment trials, missing data are generally assumed to be positive, i.e. to represent relapse to drug use.
However, the rate of missing data was generally low, with the highest rate recorded for speech deficits being 12%.
By contrast, the rate of missing data was generally low and consistent across the treatment groups in PSUMMIT-1 (table 1).
The prevalence of missing data was generally low with 2.5% for BMI, 3.7% for education and 0.7% for smoking during pregnancy.
Simulations suggest that adding genes with extensive missing data should generally either increase accuracy in Bayesian analyses, or else have no effect [ 83].
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