Hazard Ratios for Models using simple Office-based non-laboratory Predictors of CVD

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 following variables

• Age (as a continuous variable)

• Sex (0=male, 1=female)

• BMI (as a continuous variable)

• SPB treatment status (0=not treated, 1=treated)

• SPB (as a continuous variable)

• Smoking status (0=no, 1=yes)

• Diabetes (0=no, 1=yes)

This article provides a risk model for calculation cvd risk, and directly apply the Cox model on by data. 

https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.107.699579?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed

In the article following equation is given:

where S0(t) is baseline survival at follow-up time t (here t=10 years), βi is the estimated regression coefficient (log hazard ratio), Xi is the log-transformed value of the ith risk factor, (if continuous),  i is the corresponding mean, and p denotes the number of risk factors.

variable “cvdrisk” using the equation given above, Regression Coefficients and Hazard Ratio given in following table, and the variables in my file.

Supplementary Table I. Regression Coefficients and Hazard Ratios for Models using simple Office-based non-laboratory Predictors of CVD

Women (10-year Baseline Survival: So(10) = 0.94833)

Variable Beta* p-value Hazard Ratio 95% CI 

Log of Age 2.72107 <.0001 15.20 (8.59, 26.87)

Body Mass Index 0.51125 <.0609 1.67 (0.98, 2.85)

Log of SBP if not treated 2.81291 <.0001 16.66 (8.27, 33.54)

Log of SBP if treated 2.88267 <.0001 17.86 (8.97, 35.57)

Smoking 0.61868 <.0001 1.86 (1.53, 2.25)

Diabetes 0.77763 <.0001 2.18 (1.63, 2.91)

 

Men (10-year Baseline Survival: So(10) = 0.88431)

Log of Age 3.11296 <.0001 22.49 (14.80,  34.16)

Body Mass Index 0.79277 <.0066 2.21 (1.25,  3.91)

Log of SBP if not treated 1.85508 <.0001 6.39 (3.61, 11.33)

Log of SBP if treated 1.92672 <.0001 6.87 (3.90, 12.08)

Smoking 0.70953 <.0001 2.03 (1.75, 2.37)

Diabetes 0.53160 <.0001 1.70 (1.37, 2.11)

*Estimated regression coefficient.

 

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