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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

600
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...
600
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

85
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
85
Actuarial Approach01:20

Actuarial Approach

132
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
132
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

394
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...
394
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

196
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.
196
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

258
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,...
258

You might also read

Related Articles

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

Sort by
Same author

PTSD symptoms, loneliness, and amygdala volumes during pandemic social distancing associated with risk of suicidal ideation in trauma survivors with and without pre-pandemic PTSD.

Journal of affective disorders reports·2026
Same author

The NAC Transcription Factor SlNAP2 Enhances Tomato Resistance to Ralstonia solanacearum.

Physiologia plantarum·2026
Same author

Insights into the ascorbate-glutathione cycle and methylglyoxal detoxification systems during leaf yellowing of macadamia.

Protoplasma·2026
Same author

Relationships between upper extremity neuromuscular function and patient-reported outcomes among individuals with a history of glenohumeral labral repair.

PloS one·2025
Same author

Predictors of adverse events and recurrence of esophageal food bolus impaction: a systematic review and meta-analysis.

Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus·2025
Same author

Analyzing the impact of a discounted parameter on the reduction of collision time in Brownian particle trajectories.

Scientific reports·2025

Related Experiment Video

Updated: Sep 9, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K

Forecasting mortality rates in hyponatremia: a statistical approach using Holt-Winters models.

Rawiyah Muneer Alraddadi1, Mohamed Abd Allah El-Hadidy2, Qin Shao3

  • 1Department of Mathematics and Statistics, College of Science in Yanbu, Taibah University, Madinah, Saudi Arabia.

The International Journal of Biostatistics
|September 2, 2025
PubMed
Summary

This study forecasts hyponatremia mortality using time series analysis. Predictive analytics can improve patient care and resource allocation for this common electrolyte imbalance.

Keywords:
Holt-winters seasonal methodhyponatremiastatistical forecastingtime series

More Related Videos

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

213
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K

Related Experiment Videos

Last Updated: Sep 9, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

213
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K

Area of Science:

  • Clinical Medicine
  • Biostatistics
  • Healthcare Analytics

Background:

  • Hyponatremia (serum sodium < 135 mEq/L) is a common electrolyte imbalance.
  • It is linked to increased patient morbidity and mortality in various conditions.
  • Effective prediction of hyponatremia-related deaths is crucial for healthcare management.

Purpose of the Study:

  • To forecast mortality rates associated with hyponatremia.
  • To identify temporal patterns and trends in hyponatremia-related deaths.
  • To highlight the value of statistical forecasting in healthcare.

Main Methods:

  • Utilized the Holt-Winters seasonal method for time series forecasting.
  • Analyzed retrospective mortality data from US hospitals.
  • Focused on hyponatremia-related mortality trends.

Main Results:

  • The study successfully applied time series forecasting to predict hyponatremia mortality.
  • Temporal patterns in hyponatremia-related deaths were elucidated.
  • Demonstrated the utility of predictive analytics in healthcare.

Conclusions:

  • Statistical forecasting is vital for proactive healthcare resource allocation.
  • Targeted interventions can mitigate mortality risks from electrolyte imbalances.
  • Integrating predictive analytics enhances patient care for hyponatremia complications.