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Related Concept Videos

Censoring Survival Data01:09

Censoring Survival Data

218
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
218
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

252
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,...
252
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

294
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
294
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

190
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.
190
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

381
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...
381
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

277
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
277

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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A simple time-to-event model with NONMEM featuring right-censoring.

Quyen Thi Tran1, Jung-Woo Chae1,2, Kyun-Seop Bae3

  • 1College of Pharmacy, Chungnam National University, Daejeon 34134, Korea.

Translational and Clinical Pharmacology
|July 8, 2022
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Summary

This tutorial explains time-to-event (TTE) data analysis using non-linear mixed effects models (NONMEM). It provides practical guidance on coding and evaluating models for pharmacometricians.

Keywords:
NONMEMRight-CensoringTime-to-EventTutorial

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Area of Science:

  • Pharmacometrics
  • Biostatistics
  • Clinical Pharmacology

Background:

  • Time-to-event (TTE) data are frequently encountered in healthcare research.
  • Parametric modeling is a common approach for analyzing TTE data due to its simulation capabilities.
  • Familiarity with non-linear mixed effects models (NONMEM) for TTE data analysis is not universal among pharmacometricians.

Purpose of the Study:

  • To provide a tutorial on analyzing time-to-event data using NONMEM.
  • To demystify the application of NONMEM for TTE data analysis.
  • To offer practical guidance on model coding and evaluation.

Main Methods:

  • Explanation of NONMEM syntax for TTE data.
  • Step-by-step guide to model building and assessment.
  • Inclusion of a hands-on example for user training.

Main Results:

  • Demonstration of how to implement TTE analysis in NONMEM.
  • Guidance on interpreting model outputs for TTE data.
  • A practical, reproducible example for skill development.

Conclusions:

  • NONMEM can be effectively utilized for time-to-event data analysis.
  • This tutorial serves as a valuable resource for pharmacometricians seeking to apply NONMEM to TTE data.
  • The provided example facilitates hands-on learning and model development.