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Tests for trend were performed using the quartiles of BP as ordinal variables in corresponding regression models.
This variable was further categorized using the quartiles of the distribution as cutoffs: ≤ 1926; 1927 1946; 1947–1959; ≥ 1960.
A variable for income was then generated with four levels using the quartiles of the calculated equivalised income.
Calibration was also assessed using the quartiles of the predicted survival at 3 and 5 years and calculation of the corresponding observed Kaplan Meier estimates.
The only continuous variable, age, was transformed into a categorical variable using the quartiles of the frequency distribution (≤20, > 20 30, > 31 50, > 50 years).
For all measured volumes, exploratory analysis was performed using the quartiles of the corresponding distribution as predefined cut-offs, testing their distinguishing ability in patient's prognosis.
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We had two considerations for the classification: one is that we used the quartiles of annual family income per capita to determine the approximate boundaries for each group; the other is we chose 2300 yuan as the upper limit for the first group given, that the Chinese poverty line is 2300 yuan.
In this study, we used the quartiles of weekly data in each city, to separately define the periods with normal (middle) and extreme (low and high) weather.
We used the quartiles derived from the phthalate distribution of the overall population in the stratified analysis.
The original continuous erythrocyte parameters were categorized into 4 levels (Q1-Q4) using the 3 quartiles of P25, P50 and P75 as critical values, with ≤ P25 for Q1, >P25 and ≤ P50 for Q2, >P50 and ≤ P75 for Q3, and > P75 for Q4 respectively.
A strategy of using the lowest quartile of score as "normal" would have increased the effect size in the highest quartile and would have eliminated the problem of presenting "lower" genetic risk results to otherwise phenotypically high-risk individuals.
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