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In each linear regression model, we determined the robust standard error to control for heteroskedasticity.
We used a robust estimator for the standard error to control for the clustering of patients within practices.
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We allow for clustering at the birth-year-community cohort level in the calculation of the standard errors to control for potential serial correlation among children born in the same community in the same year; this yields 64 clusters.19.19
We used robust standard errors to control for potential autocorrelation in the error term.
The analysis was adjusted for ward population and used robust standard errors to control for within-ward clustering.
In this way, Type 2 errors (false negatives) are reduced in the first step while the pathway test in the second step includes an experiment wise error rate to control for Type 1 errors (false positives).
We cluster standard errors by firm to control for cross-sectional correlation.
Finally, all above equations would be estimated with the Huber-White robust standard errors, in order to control for the heteroskedasticity in nonlinear models.
Robust estimate of standard errors were used to control for clustering within schools [ 24].
Sandwich estimates for the standard errors were used to control for the fact that some people might have contributed more than one event.
Patients from whom more than one sample was obtained contributed to the analysis with more than one measurement; therefore, robust standard errors were used to control for non-independence of episodes coming from the same patient.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com