Exact(1)
LODS and PEDIA were well calibrated, whereas SICK showed poor calibration in this population.> -wrap-foot> Total data missing: LODS, 1.2%; SICK, 10.3%; PEDIA, 4.8%), default models consider missing data normal.
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According to the developers of SICK, missing data should be treated as normal; however, in the context of our cohort we found better model discrimination if we considered missing data as abnormal since a number of variables were not missing at random.
Gaps were considered missing.
The unknown variables were considered missing variables.
All other cases were considered missing.
Heterozygous individuals were considered missing value "U".
In addition, sensitivity analysis considering plausible models and missing data assumptions will be used to check the robustness of results across possible missing data assumptions.
Coming back to Davuluri, there has been an unpleasant undercurrent about whether or not she is a good role model, considering she allegedly called the previous Miss America title holder "fat as (expletive)." Ungraceful indeed.
For example, if email was the primary method for obtaining response data then, if correct, this suspicion suggests that qualifications would be a crucially important variable to consider when modelling the missing data.
My Buffett model considers that exceptional.
In our modelling, only the first of these were used as absences, the second category being considered as missing data and excluded from model fitting for that species.
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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