Your English writing platform
Discover LudwigExact(5)
There are 86 MSAs/PMSAs in column (9), where we do not impose the same data sufficiency requirement on the employment and wage samples.
The MSAs/PMSAs beginning in columns (3 -(4) are the MSAs and PMSAs that constitute the 39 CMSAs that we can track for the entire sample period and that meet the data sufficiency requirement (50 valid wage observations per MSA/PMSA and month).
Another common sufficiency requirement is based on laboratory and/or medication data.
However, as their work demonstrates this assumption is wrong, and the imposition of just this one sufficiency requirement biases the population towards sicker and older patients [ 23].
The addition of this frequently overlooked sufficiency requirement has the potential to lead to bias in the selection of patients for inclusion in EHR based studies, which may limit their external validity.
Similar(55)
These findings highlight an important but usually overlooked problem inherent to studies using EHR data: the selection of records with sufficient data, as measured by human imposed sufficiency requirements, for research may bias the sample towards patients who are sicker than the population from which the sample is drawn.
Example sufficiency requirements include "a sub-population who have sufficient health record data at institution {X} frequenting the {X} hospital system for routine care" and "total number of individuals that have male gender and serum creatinine 1.5 mg/dL or female gender and serum creatinine 1.3 mg/dL.
EHR-based studies sample patients based on sufficiency requirements with the aim of selecting only those records containing sufficient data to overcome the data missingness problem.
Low fertilizer level fell short of minimum sufficiency requirements (0.091 g N, 0.019 g P), whereas high fertilizer level exceeded sufficiency requirements (0.46 g N, 0.097 g P).
We chose two kinds of data - laboratory results and medication orders – commonly used as sufficiency requirements.
The more stringent the sufficiency requirements, the sicker the resultant sample population.
Write better and faster with AI suggestions while staying true to your unique style.
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