RP. Obermayr(1), C. Temml(2), G. Gutjahr(3), A. Kainz(4), R. Klauser-Braun(1), R. Függer(5), R. Oberbauer(4)
1- 3rd Medical Department (Division of Nephrology, Diabetes, Hypertension), Donauspital, Sozialmedizinisches Zentrum Ost der Stadt Wien, Austria
2- Department of Health Prevention, Vienna, Austria
3- Institute of Medical Statistics, Medical University Vienna, Austria
4- Department of Nephrology, KH Elisabethinen, Linz, and Medical University Vienna, & Austrian Dialysis and Transplant Registry, Austria
5- Department of Surgery, KH Elisabethinen, Linz, Austria
Tests for proportional hazards assumption concerning cardiovascular mortality.
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Scaled Schoenfeld residuals for the factor smoke concerning cardiovascular mortality.
Legend: Under the proportional hazards assumption, the residuals are constant over time. For the factor smoke, residuals are decreasing. Therefore a time dependent term was used for the factor smoke in the final model.
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Effect contrasts for cardiovascular death (alternative model 1)
Legend: To validate the model of the main text (table 3a) we fitted three additional models: In alternative model 1, variables were included as confounders in a multivariable Cox model if the change in the Chi-Square statistics of the BMI*GFR interaction term exceeded 10%. Contrasts were calculated identically to Table 3a of the main paper. Wald test for the effect size of the BMI*GFR interaction term: P = 0.0001
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Effect contrasts for cardiovascular death (alternative model 2)
Legend: To validate the model of the main text (table 3a) we fitted three additional models: For alternative model 2 a propensity score was created by logistic regression of cardiovascular death on all potential confounders. Alternative model 2 uses this propensity score in a Cox regression model stratified by quintiles of the propensity score. Contrasts were calculated identically to Table 3a of the main paper. Wald test for the effect size of the BMI*GFR interaction term: P = 0.0070.
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Effect contrasts for cardiovascular death (alternative model 3)
Legend: To validate the model of the main text (table 3a) we fitted three additional models: For alternative model 2 a propensity score was created by logistic regression of cardiovascular death on all potential confounders. Alternative model 3 uses this propensity score as time dependent covariable in a Cox regression model. Contrasts were calculated identically to Table 3a of the main paper. Wald test for the effect size of the BMI*GFR interaction term: P = 0.0001.
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Effect contrasts for cancer death (alternative model 1)
Legend: To validate the model of the main text (table 3a) we fitted three additional models: In alternative model 1, variables were included as confounders in a multivariable Cox model if the change in the Chi-Square statistics of the BMI*GFR interaction term exceeded 10%. Contrasts were calculated identically to Table 3a of the main paper. Wald test for the effect size of the BMI*GFR interaction term: P = 0.0808.
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Effect contrasts for cancer death (alternative model 2)
Legend: To validate the model of the main text (table 3a) we fitted three additional models: For alternative model 2 a propensity score was created by logistic regression of cardiovascular death on all potential confounders. Alternative model 2 uses this propensity score in a Cox regression model stratified by quintiles of the propensity score. Contrasts were calculated identically to Table 3a of the main paper. Wald test for the effect size of the BMI*GFR interaction term: P = 0.2609.
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Effect contrasts for cancer death (alternative model 3)
Legend: To validate the model of the main text (table 3a) we fitted three additional models: For alternative model 2 a propensity score was created by logistic regression of cardiovascular death on all potential confounders. Alternative model 3 uses this propensity score as time dependent covariable in a Cox regression model. Contrasts were calculated identically to Table 3a of the main paper. Wald test for the effect size of the BMI*GFR interaction term: P = 0.0829.
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