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Strengthening affordable housing by creating a variable rate of the definition of "affordable" linked to average earnings in each London borough.
The amount of time participants were followed up is controlled for in analyses by creating a variable of time since baseline data collection.
To that end, we determined whether or not it was the first time a resource was being visited by creating a variable with the value of 0 if the resource was visited for the first time and was not revisited after, and a value of 1 if it was the first visit and was revisited after.
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The log-linearity assumption of the continuous variables was checked by creating a design variable based on quartiles, and the assumption of proportional hazards was evaluated graphically by log-minus-log survival plots.
In all three models missing values were factored in by creating a dichotomized variable (X, IX) to identify whether or not the variable of interest was missing using the following method: X = 1 if positive, X = 0 otherwise, IX = 1, if missing, IX = 0 otherwise.
The variable childhood mortality experienced was dichotomized into two by creating a dummy variable 0 (zero) for those who had not experienced childhood deaths and 1 one) if otherwise.
The linearity assumption of continuous variables (APACHE II and SOFA scores and age) was checked by creating a design variable based on quartiles.
Tests for trend were performed by creating a continuous variable from the medians of the categories.
We attempted to address this issue by creating a single variable representing their joint effect.
The index was calculated, in the case of education, by creating a new variable that ordered the educational groups from highest to lowest level of education.
Tests for trend were performed by creating a continuous variable from the median value for intake in controls within each quartile.
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