Sentence examples for missing data measurement from inspiring English sources

It includes risk of bias due to confounding factors (lack of information on type of pacifier, start and finish of the habit, initial malocclusion, presence of digital or object sucking), selection of participants into the study, classification of interventions, deviations from intended intervention, missing data, measurement of outcomes, and selection of the reported results [21].

A multilevel hierarchical Bayesian model accounted for differences in national and regional levels of missing data, measurement incomparability, study representativeness, and sampling and modelling uncertainty.

Studies of the health effects of ozone exposure need to address the incorporation of missing data, measurement error, and the combination of complementary studies.

In case of missing data (measurement of less than five days), we averaged the number of days with and without physical education separately and, on the basis of the class schedule, extrapolated them to a school week of five days.

The tool has been developed for non-randomised studies of interventions and, while built environment interventions pose particular challenges, it is important to be aware of and minimise bias due to confounding, participant selection, measurement of interventions, departure from intended interventions, missing data, measurement of outcomes, and selection of reported results.

In the Cox regression, participants will automatically be censured at the first missing data measurement during follow-up – no matter the reason for the missing data (i.e. spot-like missing or complete drop-out).

In the case of missing data, measurements from the same study period were carried forward to substitute for missing end-of-period values.

19 20 21 Seven domains were assessed: confounding, selection, exposure measurement, misclassification over time, missing data, outcome measurement, and selective reporting.

Clinical incomparability can be caused by differences in the VISN level populations, and design incomparability can be caused due to differences, for example, in missing data and measurement error.

Last, the performance evaluation was limited in that we examined a single site with a moderate amount of missing data, and measurement noise, especially important at the low CO levels encountered, was not considered.

With 30% missing data, 5 measurement times, N = 200 patients, I = 4 items, ρ = 0.7, the statistical power decreased from 81% to 72% for the SM method, from 55% to 28% for "TTD baseline", from 39%to20%0% for "TTD best" and from 46%to28%8% for "TUDD baseline".