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In light of the results from Pasaniuc et al. (2012) and this article, we recommend imputation-based association studies on GBS data with sporadically missing values only when coverage is high enough to produce few missing values or when a reference panel is available for imputation.
In this step, to limit the influence of missing values, only those years with at least three months that include more than five days with precipitation were used.
Due to missing values only 1535 were included.
Due to missing values, only 1304 participants were included in the final model.
In the case where possesses missing values, only the completely observed rows are used.
However, due to missing values only 536 participants were included in the multi-level linear regression model.
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We imputed missing values once only and analyzed the one set of ten complete imputed datasets separately for each outcome.
If there were missing values for only one of the symptoms in each of the diagnoses this missing value was recorded as "no symptom".
We did not impute missing values because only 2 out of 44 (4.5%) participants had missing values.
Missing values are only reported for variables with more than 25% missing data.
Second, missing values can only be imputed with some degree of uncertainty.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com