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It is difficult to generalize the response of single drug with multiple drugs, variable time intervals for evaluating response and classifying responders and non responders with different threshold for response.
Third, we sought to analyze certain covariates in the most informative way possible: the "immunosuppressive drugs" variable was assessed according to the type of drug and to the systemic exposure to the drug; the "anti-hypertensive treatment" covariate, according to the type and to the number of drugs.
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It was derived using data on age, sex, smoking, family history of diabetes, BMI and prescribed steroids and antihypertensive drugs, variables, which are increasingly routinely available in primary care.
In those participants with DM, a dichotomous drug variable was created in which 1 was equal to the presence of an oral anti-diabetic oral treatment or 0 equal to diet controlled treatment.
In case a SALAR drug variable was the same as an ATC-based category variable, one of them was excluded.
This additional information can be incorporated in the model weighting each drug variable d k in the objective function according to its non-metabolic effects (for which only a pure superposition is considered because of the lack of more quantitative models).
Clinical and drug variables were included.
Reliability coefficients for the combined-drug variables are provided in Table 3 and labeled "All drug variables".
Results of the meta-analysis are presented in Tables 33 (drug variables) and 4 (sex variables).
Fourteen clinical variables and 64 drug variables met the inclusion criteria and were subject to the PCA.
For simplicity, we assumed independence among the six drug variables, even though some drugs were in more than one category.
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