American Heart Association PREVENT Equations & ASCVD Risk in Arab Women & Men

Okay, here’s a breakdown of the statistical⁣ analysis methods used in the study, based on the provided⁢ text. I’ll organize it into sections for clarity:

1. Handling Missing Data:

* Multiple Imputation: Missing values were addressed‍ using multiple imputation.
* ⁢ Variables with Missing Data: The following variables had missing data:
⁣ ‍* Total Cholesterol (TC) (<1%)
‍ * High-Density Lipoprotein Cholesterol (HDL-C) (<1%)
* ‍ HbA1c (4.7%)
* Creatinine ‍(<1%)
* Systolic Blood Pressure (SBP) (<1%)
* Body Mass Index (BMI) (<1%)
* Assumption: ‍The study assumed that the⁤ missing data were “missing at random” (MAR).
* Imputation Method: Predictive mean-matching was used‍ to generate five imputed datasets.
* Combining Results: Rubin’s rule was used to combine the⁣ results from the five imputed datasets. (Reference 21)

2. Cohort Comparison:

* Comparison Groups: The Emirati validation cohort was compared to the original PREVENT ⁤study cohort.
* Statistical Tests:

‍ * Continuous variables: Student’s t-test
* ⁣ Categorical Variables: Chi-square test (two-sided)
* Stratification: All comparisons were stratified by sex (male/female). This was done to align with the original PREVENT Equation Study, which developed separate‍ risk equations for men and women.

3. Risk Prediction⁣ & Equations Used:

* Risk Equations: Two equations were used to calculate 10-year⁢ ASCVD risk:
* PREVENT-ASCVD Base: ⁢⁢ Includes age, sex, ⁣TC, HDL-C, SBP, diabetes, smoking status, BMI, eGFR, and medication use (blood pressure and cholesterol).
⁣ * PREVENT-ASCVD HbA1c: Adds HbA1c levels to the PREVENT-ASCVD Base equation.
* Purpose of HbA1c Equation: The HbA1c equation is intended to ‍provide ‍a more precise risk assessment, notably for individuals with diabetes.

4. Evaluating Predictive Accuracy:

* Measures Used: Both calibration and discrimination were assessed.
* Discrimination:

* ‍ Measure: C-index (proposed by Harrell et al. – Reference 22). This is comparable to the Area under the Receiver Operating Characteristic Curve (AUC).
* ‍ ⁤⁤ Interpretation: A C-index > 0.75 was considered indicative of good discrimination.
* Calibration: Assesses the alignment between observed and predicted risk. (The text⁣ stops mid-sentence here, so details on how calibration was assessed are missing.)
* ‍ Analysis by Sex: The performance of both equations ‍was compared separately for men and women.

In essence, the study used robust statistical methods to ⁤validate existing risk ⁤prediction equations in a new population (Emirati cohort), carefully addressing missing data and considering sex-specific differences.