Related Experiment Video
Updated: May 23, 2025
10:46
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
10.6K
Semiparametric Modeling of Biomarker Trajectory and Variability With Correlated Measurement Errors.
Renwen Luo1, Chuoxin Ma1, Jianxin Pan1
1Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, China.
Statistics in Medicine
|March 10, 2025
Summary
This study introduces a new statistical model to accurately assess disease risk from biomarker variability over time. It accounts for measurement errors, improving predictions for conditions like cardiovascular mortality.
Area of Science:
- Biostatistics
- Epidemiology
- Longitudinal Data Analysis
Background:
- Biomarker variability is crucial for predicting disease risk.
- Current methods often ignore within-subject measurement error correlations, leading to biased results.
- Existing models require complex computations and assume normal random effects.
Purpose of the Study:
- To develop a robust statistical model for analyzing biomarker variability and time-to-event data.
- To address limitations of existing methods, including correlated measurement errors and non-normal random effects.
- To jointly model biomarker variability and event risk.
Main Methods:
- Proposed a semiparametric multiplicative random effects model.
- Incorporated correlated longitudinal measurement errors.
- Integrated biomarker variability as a covariate in a Cox model for time-to-event data, avoiding high-dimensional integration.
Main Results:
- Demonstrated asymptotic properties of the proposed estimators.
- Validated the model's performance through simulation studies.
- Applied the methodology to real-world data, assessing systolic blood pressure variability and cardiovascular mortality.
Conclusions:
- The novel model effectively handles correlated longitudinal measurement errors and non-normal random effects.
- This approach offers improved accuracy and broader applicability in survival analysis.
- The findings provide a more reliable method for understanding the impact of biomarker variability on disease outcomes.
Related Concept Videos
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
23
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...
23
01:14Parametric Survival Analysis: Weibull and Exponential Methods
320
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...
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...
320
01:24Kaplan-Meier Approach
74
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
74
01:15Systematic Error: Methodological and Sampling Errors
1.4K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.4K
03:37Uncertainty in Measurement: Accuracy and Precision
73.1K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
73.1K
01:15Assumptions of Survival Analysis
83
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.
83