Sentence examples for missing data depending from inspiring English sources

Finally, both intermittent and monotone missing data depending on patients' HRQoL level (MNAR profile) were studied, thereby approaching the actual conditions of clinical trials.

This could be explained by the simulation of missing data depending on HRQoL level, i.e. patients with a low HRQoL level were more likely to present missing data.

To reflect the reality of most clinical trials, the impact of informative missing data on these methods was also studied, with the implementation of both intermittent and monotone missing data, depending on the patients' HRQoL level (MNAR profile).

The risk of bias due to missing data depends on the reasons why data are missing.

MNAR means that the probability of missing data depends on the unobserved data.

The proportion of missing data depends on the sequencing depth and library complexity.

This approach has been shown to be robust when missing data depend on baseline values [ 62].

Whether estimates are biased by missing data depends on the relationships between the chance of data being missing and the variables involved in the analysis.

Our use of general linear mixed models will insure unbiased results as long as missing data depend only upon observed variable (i.e., missing at random [MAR]).

In contrast, data will be considered MNAR if the probability of missing data depends on the patient's (unobserved) HR-QoL.

Despite this, it is possible that the mechanism for missing data was non-ignorable; in other words, the missing data depended on variables not measured in this study or on the health status of non-respondents at follow-up.