Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Hazard Rate01:11

Hazard Rate

379
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
379
Time Course of Drug Effect01:14

Time Course of Drug Effect

2.6K
The progression of a drug's impact can be analyzed by examining both the concentration-time course and the effect-time course. The concentration-time course is determined by the drug's half-life and is influenced by factors such as its pharmacokinetics, including absorption, distribution, metabolism, and elimination. The effect of the drug is often related to its concentration in the plasma and is calculated using the maximum drug effect and the plasma concentration that generates 50...
2.6K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

533
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...
533
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

382
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.
382
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

712
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...
712
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

1.9K
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
1.9K

You might also read

Related Articles

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

Sort by
Same author

Glucagon-Like Peptide-1 Receptor Agonists and Incident Major Adverse Liver Outcomes in People With Type 2 Diabetes and Metabolic Dysfunction-Associated Steatotic Liver Disease.

Diabetes, obesity & metabolism·2026
Same author

Routine Susceptibility Testing of <i>Helicobacter pylori</i> in Clinical Practice-Results of a Prospective Multicentre Study.

Antibiotics (Basel, Switzerland)·2026
Same author

Communicating Time-to-Event Treatment Effects in Randomized Trials: A Randomized Experiment among General Practitioners.

Medical decision making : an international journal of the Society for Medical Decision Making·2026
Same author

A New Parametric Accelerated Failure Time Model for Semi-Competing Risks Data.

Statistics in medicine·2026
Same author

Reassessment of the Diagnostic Accuracy of HbA<sub>1c</sub> and Glucose for Type 2 Diabetes: A Systematic Review and Meta-Analysis of Observational Studies.

Diabetes/metabolism research and reviews·2026
Same author

GLP-1 receptor agonists or SGLT2-inhibitors? Evaluation of a personalized treatment algorithm for individuals with type 2 diabetes: a registry-based cohort study.

Experimental and clinical endocrinology & diabetes : official journal, German Society of Endocrinology [and] German Diabetes Association·2026
Same journal

Sparse multi-way DMDC for longitudinal classification in high dimension low sample size data.

BMC medical research methodology·2026
Same journal

Tree-based exploratory identification of predictive biomarkers in non-randomized data.

BMC medical research methodology·2026
Same journal

Comparative evaluation of interrupted time series analytical methods for healthcare quality improvement research: a Monte Carlo simulation study.

BMC medical research methodology·2026
Same journal

Methodological advances in claims-based dementia algorithms: integrating medication and clinical data for medicare populations.

BMC medical research methodology·2026
Same journal

An interpretable XGboost algorithm for predicting 30-day mortality in acute pancreatitis using routine biomarkers.

BMC medical research methodology·2026
Same journal

Increasing power and robustness in screening trials by testing stored specimens in the control arm.

BMC medical research methodology·2026
See all related articles

Related Experiment Video

Updated: Jan 8, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.3K

Time-constant absolute effect measures for time-to-event outcomes.

Oliver Kuss1,2,3, Annika Hoyer4

  • 1Deutsches Diabetes-Zentrum, Institut für Biometrie und Epidemiologie, Auf'm Hennekamp 65, Düsseldorf, 40225, Germany. oliver.kuss@ddz.de.

BMC Medical Research Methodology
|December 17, 2025
PubMed
Summary
This summary is machine-generated.

New parametric additive hazard models enable calculation of time-constant absolute effects for time-to-event outcomes. This method provides a single, interpretable Number Needed to Treat (NNT) for better clinical trial communication.

Keywords:
Additive hazardNumbers needed to treatSurvival analysisType 2 diabetes

More Related Videos

Impact of High-intensity Interval Exercise and Moderate-Intensity Continuous Exercise on the Cardiac Troponin T Level at an Early Stage of Training
07:40

Impact of High-intensity Interval Exercise and Moderate-Intensity Continuous Exercise on the Cardiac Troponin T Level at an Early Stage of Training

Published on: October 10, 2019

7.7K
High-throughput Measurement of Gut Transit Time Using Larval Zebrafish
06:48

High-throughput Measurement of Gut Transit Time Using Larval Zebrafish

Published on: October 23, 2018

7.9K

Related Experiment Videos

Last Updated: Jan 8, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.3K
Impact of High-intensity Interval Exercise and Moderate-Intensity Continuous Exercise on the Cardiac Troponin T Level at an Early Stage of Training
07:40

Impact of High-intensity Interval Exercise and Moderate-Intensity Continuous Exercise on the Cardiac Troponin T Level at an Early Stage of Training

Published on: October 10, 2019

7.7K
High-throughput Measurement of Gut Transit Time Using Larval Zebrafish
06:48

High-throughput Measurement of Gut Transit Time Using Larval Zebrafish

Published on: October 23, 2018

7.9K

Area of Science:

  • Biostatistics
  • Clinical Trial Analysis
  • Survival Analysis

Background:

  • Reporting relative and absolute treatment effects is vital in clinical trials.
  • Calculating absolute measures like Number Needed to Treat (NNT) for time-to-event data is challenging due to time-dependence.
  • Traditional models have limitations in handling outcome distributions and time-dependence.

Purpose of the Study:

  • To propose and evaluate parametric additive hazard models for computing time-constant absolute effect measures in time-to-event outcomes.
  • To provide a single, interpretable absolute effect size (e.g., hazard difference, NNT) over the entire study duration.
  • To overcome limitations of previous methods regarding distribution flexibility and time-dependence.

Main Methods:

  • Utilized a class of parametric additive hazard models for time-to-event data.
  • Fitted six different parametric distributions (exponential, linear hazard rate, Weibull, log-logistic, Gompertz, Gamma-Gompertz).
  • Applied the method to digitized Kaplan-Meier data from the EMPA-REG OUTCOME trial for all-cause mortality.

Main Results:

  • Despite varying model fits, estimated rate differences and NNTs were similar across distributions.
  • Best-fitting models (linear hazard rate, Gompertz) yielded a rate difference of -8.8/1000 person-years and an NNT of 114.
  • Increasing hazards were observed, consistent with all-cause mortality, and estimated distribution modes were plausible.

Conclusions:

  • Parametric additive hazard models provide a robust method for calculating time-constant absolute effect measures for time-to-event outcomes.
  • This approach successfully addresses time-dependence and distribution flexibility, yielding interpretable absolute effect sizes.
  • Future research can explore more complex distributions and absolute measures on the time scale.