Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

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:

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

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...

Introduction To Survival Analysis

Introduction To Survival Analysis

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 until a...

Assumptions of Survival Analysis

Assumptions of Survival Analysis

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.

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

Study Design in Statistics

Study Design in Statistics

A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Avoiding Cisplatin-Related Hearing Loss, Including Implementing Sodium Thiosulfate as Otoprotectant Into Daily Pediatric Clinical Practice: Proceedings Based on Evidence and Expert Opinion From the Ototoxicity Taskforce of the SIOP Supportive Care Network.

Ear and hearing·2026

Same author

Targeting homologous recombination deficiency with intensified chemotherapy versus standard chemotherapy followed by olaparib in stage III breast cancer (SUBITO): an open-label, randomised, controlled, phase 3 trial.

The Lancet. Oncology·2026

Same author

Practical guide to implementing pre-emptive pharmacogenetic screening in routine pediatric oncology care.

Pharmacogenomics·2026

Same author

Sex Impacts Progression-Free Survival of Alectinib through Drug Exposure in Patients with <i>ALK-</i>Positive Non-Small Cell Lung Cancer.

Cancer communications (London, England)·2026

Same author

Transfer of trastuzumab and pertuzumab into human breast milk: a case report.

Cancer chemotherapy and pharmacology·2026

Same author

The SHERPA trial: A phase I study combining SHP2 inhibitor RMC-4630 and ERK inhibitor LY3214996 in patients with KRAS-mutant pancreatic, non-small cell lung and colorectal cancer.

European journal of cancer (Oxford, England : 1990)·2026

Search research articles

Related Experiment Video

Updated: May 13, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Development of a framework for cohort simulation in cost-effectiveness analyses using a multistep ordinary

Gerardus W J Frederix¹, Johan G C van Hasselt^1,2, Johan L Severens³

¹Department of Clinical Pharmacology & Experimental Therapy, The Netherlands Cancer Institute, Amsterdam, The Netherlands (GWJF, JGCVH, JHMS)

Medical Decision Making : an International Journal of the Society for Medical Decision Making

|March 22, 2013

Summary

Using a modern ODE solver in cost-effectiveness analysis (CEA) reduces bias. This study implemented LSODA in R, showing single-step methods can underestimate outcomes like life-years gained in CEA.

Keywords:

Markov model R cohort simulation cost-effectiveness analysis tamoxifen

More Related Videos

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Related Experiment Videos

Last Updated: May 13, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

Health economics
Computational modeling

Background:

Cost-effectiveness analysis (CEA) often uses ordinary differential equations (ODEs) to model dynamic processes.
Commonly employed single-step ODE solvers may introduce bias due to incorrect step size selection.

Purpose of the Study:

Implement and demonstrate the LSODA ODE solver in R for CEA.
Quantify the bias introduced by single-step ODE solvers in CEA outcomes.

Main Methods:

A previously published CEA comparing breast cancer therapies was used as a case example.
Compared a single-step ODE solver with the multistep LSODA algorithm to assess bias.

Main Results:

Developed a computational framework using the LSODA ODE solver in R.
A single-step solver underestimated life-years gained by 5.6% and costs by 6.8% compared to LSODA.

Conclusions:

The LSODA framework is suitable for conducting CEAs.
Single-step ODE solvers can cause significant bias in CEA outcomes.
R enhances transparency and reproducibility in health economics research.