Semiparametric modeling for the cardiometabolic risk index and individual risk factors in the older adult population: A novel proposal
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new semiparametric model for monitoring cardiometabolic risk index in older adults, improving early detection and management of metabolic syndrome.
Area Of Science
- Gerontology
- Epidemiology
- Biostatistics
Background
- Accurate monitoring of metabolic syndrome in older adults is crucial for early detection and management.
- Cardiometabolic risk index (CMRI) and its associated risk factors require precise assessment in aging populations.
Purpose Of The Study
- To propose a novel semiparametric modeling approach for assessing CMRI and individual risk factors in older adults.
- To enhance the understanding of associations between CMRI and risk factors using advanced statistical methods.
Main Methods
- Utilized multivariate semiparametric regression models for secondary data analysis from the SABE study (Colombia, 2015).
- Employed a stepwise procedure for risk factor selection.
- Investigated non-linear relationships and interactions between CMRI and covariates.
Main Results
- Identified significant non-linear relationships and interactions between CMRI and factors including BMI, age, arm/calf circumferences, sex, walking speed, and joint pain.
- Demonstrated non-linear interactions: BMI-age (p<0.00), arm/calf circumferences (p<0.00), age-females (p<0.00), walking speed-joint pain (p<0.02), arm circumference-joint pain (p<0.00).
Conclusions
- The proposed semiparametric modeling approach explained 24.5% of the observed deviance in CMRI.
- This represents a significant improvement over the 18.2% deviance explained by traditional linear models, highlighting the utility of semiparametric methods for this population.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Descriptive Statistics: These provide basic...
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Survival Times Are Positively Skewed
Survival times often exhibit positive skewness, unlike the normal distribution assumed...