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64 65 66 A mixed model for repeated measures not only can yield unbiased estimates of parameters when missing observations are explained by observed outcomes, but not by unobserved outcomes (missing at random), but also performs surprisingly well even on simulated clinical trial data that include both observations missing at random and observations missing not at random.
Long-term data: Data on long-term outcomes missing.
When confronted with missing data, often it is reasonable to assume that the mechanism underlying missingness is related to observed but not to unobserved outcomes (missing at random, MAR).
As for clinical outcomes, missing resource use and QALY data will be estimated by multiple imputation.
Limitations of the Registry include possible underreporting of outcomes, missing data, and/or inadequate follow-up.
For the primary, but not the secondary, outcomes missing values are imputed.
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Focusing only on comparative analysis and relative outcomes misses the wider context of changes in absolute outcomes.
We used the ice command [ 21] in Stata to impute confounder and outcome missing data.
For each outcome, missing data were categorised into early and late dropout/completer patterns.
However, this arid, texbook debate, "equal opportunity" versus "equal outcome", misses the point: how today's unequal outcomes shape tomorrow's unequal opportunities.
For NDSCR, 3.36% of cases for which outcomes were missing were excluded from the outcomes analysis only.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com