Related Experiment Video
Updated: May 11, 2026
10:46
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
surtvep: An R package for estimating time-varying effects.
Lingfeng Luo1, Wenbo Wu2, Jeremy M G Taylor1
1Department of Biostatistics, School of Public Health, University of Michigan.
Journal of Open Source Software
|December 24, 2024
Summary
The surtvep R package efficiently estimates time-varying effects in survival analysis for large datasets. It addresses computational challenges in Cox non-proportional hazards models, improving medical study insights.
Area of Science:
- Biostatistics
- Computational Biology
- Medical Informatics
Background:
- Large-scale time-to-event data from national registries necessitates methods for time-varying effects.
- Existing software struggles with memory limitations and numerical instability for complex survival models.
- Accurate modeling of dynamic effect trajectories is critical in medical research.
Purpose of the Study:
- Introduce the surtvep R package for estimating time-varying effects in survival analysis.
- Provide a computationally efficient solution for large-scale time-to-event data.
- Address limitations of current software in handling Cox non-proportional hazards models.
Main Methods:
- Utilizes a Kronecker product-based proximal algorithm for computational efficiency.
- Implements P-spline and smoothing spline penalties for improved estimation.
- Supports both unstratified and stratified Cox models with parallel computation.
Main Results:
- surtvep offers efficient estimation of time-varying effects in survival analysis.
- The package provides confidence intervals, hypothesis testing, and hazard/survival probability estimations.
- Optimized tuning parameters via cross-validation and information criteria.
Conclusions:
- The surtvep package provides a robust and flexible tool for analyzing dynamic effect trajectories in large time-to-event datasets.
- It overcomes computational hurdles, enabling more accurate survival analysis.
- Facilitates deeper insights into medical studies with time-varying covariates.
Related Concept Videos
01:24One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
221
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...
221
01:05Noncompartmental Analysis: Mean Residence Time
59
According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
59
01:25Statistical Methods for Analyzing Epidemiological Data
234
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:
234
01:18Introduction To Survival Analysis
114
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
114
01:29Econometric Views (EViews)
88
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
88
01:14Parametric Survival Analysis: Weibull and Exponential Methods
271
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...
271