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A k is the weighting value for each of these estimates.
Each of these estimates of GFR has weaknesses and strengths.
Each of these estimates carries inherent uncertainty, as does using a hypothetical cohort.
We corrected each of these estimates for bias caused by overdiagnosis and selective attendance.
Finally each of these estimates is combined to give an overall estimate of the true outcome effect.
Where available, we will describe how each of these estimates varies by age, gender, socioeconomic status (SES) and ethnicity and over time.
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Though not explicitly testing the hypothesis of the criticality of each variable, the value of these estimates is corroborated by the prediction performance of the model overall.
The standard errors for each of these proportion estimates were calculated as P (DM ) × q N, where q = 1 − P DM) and N = total number of subjects.
Three of these estimates, each from pair-wise array comparisons involving the same array, were extreme outliers in both the normalized and raw dataset (see Figure 3).
We used kernel density estimates to plot the frequency distribution of these estimates for each group.
We have not provided standard errors of these estimates, because each point used for calculation of the regression parameters is based on subdivision of the same chromosome 3 data in different ways.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com