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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.
The risk of bias due to missing data depends on the reasons why data are missing.
In contrast, data will be considered MNAR if the probability of missing data depends on the patient's (unobserved) HR-QoL.
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.
We acknowledge that the attrition has resulted in missing data over time, however the use of linear mixed models has been shown to be robust when missing data depends on baseline values [ 75].
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This approach has been shown to be robust when missing data depend on baseline values [ 62].
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]).
Finally, both intermittent and monotone missing data depending on patients' HRQoL level (MNAR profile) were studied, thereby approaching the actual conditions of clinical trials.
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).
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.
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