Related Experiment Video
Updated: Jan 15, 2026
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
2.5K
A Noise-Tolerant Inference Procedure for Quasi-Monte Carlo Likelihood Estimation of a Joint Model for Multiple
L Chabeau1,2, P Rinder2, S Desmée1
1INSERM, MethodS in Patients-centered outcomes and HEalth Research, UMR 1246 SPHERE, Nantes University, Tours University, Nantes, France.
Statistics in Medicine
|October 11, 2025
Summary
Estimating complex joint models for longitudinal and survival data is challenging. This study introduces a noise-tolerant Quasi-Newton algorithm with Quasi-Monte Carlo integration, improving parameter estimation for joint models with multiple markers and competing risks.
Area of Science:
- Biostatistics
- Statistical Modeling
- Medical Data Analysis
Background:
- Complex joint models for longitudinal and survival data are increasingly used but difficult to estimate.
- The computational challenge arises from intractable integrals over random effects, especially with high dimensionality.
- Accurate estimation is crucial for understanding disease progression and treatment outcomes.
Purpose of the Study:
- To propose a novel method for estimating complex joint models.
- To address the computational difficulties associated with intractable integrals in likelihood calculations.
- To improve the estimation of joint models with multiple longitudinal markers and competing risks.
Main Methods:
- Utilized a Quasi-Monte Carlo (QMC) approach to approximate intractable integrals over random effects.
- Combined QMC with a noise-tolerant Quasi-Newton algorithm to handle likelihood randomness.
- Validated the method through simulation studies and application to kidney transplantation data.
Main Results:
- The noise-tolerant Quasi-Newton algorithm effectively estimated parameters in a joint model with two longitudinal markers and competing events.
- Performance was comparable to a standard Quasi-Newton algorithm using common draws in QMC.
- Demonstrated relevance in modeling serum creatinine and antibody immunization in kidney transplant recipients.
Conclusions:
- The proposed noise-tolerant inference procedure for QMC likelihood estimation is a relevant advancement.
- This method enhances the estimation of joint models involving multiple longitudinal markers and competing risks.
- The approach shows promise for analyzing complex biomedical data, such as in kidney transplantation studies.
Related Concept Videos
01:15Assumptions of Survival Analysis
392
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.
392
01:16Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
454
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
454
01:19Expected Frequencies in Goodness-of-Fit Tests
7.2K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
7.2K
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
288
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
288
01:24One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
1.1K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
1.1K
00:59Propagation of Uncertainty from Random Error
1.7K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.7K