Sentence examples for missing data produced from inspiring English sources

The sensitivity analyses that we did to explore assumptions about missing data produced results that were consistent with the main analyses (supplementary tables B to E).

The FREE trial results are robust as the four alternative methods used for substituting missing data produced similar results.

The FREE trial results are robust as the alternative methods used for substituting missing data produced similar results.

The results of modelling transmission, using the weighted configuration model, show that both the low and high sexual frequency scenarios (see figure 3, top and bottom green and brown lines, assuming different numbers of UAS acts for missing data) produced very high R03M.

Unfortunately, reliable information about the pattern of missing data may be not readily available and recent experimental results show that the enforcement of an incorrect assumption about the pattern of missing data produces a dramatic decrease in accuracy of the classifier.

A sensitivity analysis estimating models 2 and 3 without missing data imputation produced similar results.

Inclusion of individuals with missing data, however, produced similar patterns of association to those presented.

Sensitivity analysis using multiple imputation of missing data also produced similar findings (see online supplementary appendices 5 and 6).

In intent-to-treat analyses with baseline weight carried forward for missing data, IBI produced significantly larger mean (SD) weight losses than IDEA at 3 months (5.5 kg [4.4] vs. 1.3 kg [2.1]) and 6 months (5.4 kg [5.6] vs. 1.3 kg [4.1]) (P < 0.001).

Essentially, MI is a means for representing uncertainty in missing data, by producing a distribution of plausible values for a missing variable in a record, given the values of that record's non-missing covariates.

Despite its good empirical performance, DACTAL provides no statistical guarantees, and it is likely that statistical methods based on models of evolution that treat indels as events, rather than as missing data, would produce more accurate trees (Warnow, 2012).