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

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

115
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,...
115
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

392
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...
392
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

337
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:
337
Actuarial Approach01:20

Actuarial Approach

69
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,...
69
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

7.6K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
7.6K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

35
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...
35

You might also read

Related Articles

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

Sort by
Same author

Excess Mortality Estimation.

Annual review of statistics and its application·2026
Same author

Direct-Assisted Bayesian Unit-level Modeling for Small Area Estimation of Rare Event Prevalence.

Journal of survey statistics and methodology·2026
Same author

Toward a Principled Workflow for Prevalence Mapping Using Household Survey Data.

Journal of survey statistics and methodology·2026
Same author

Small Area Estimation of Education Levels in Low- and Middle-Income Countries.

The annals of applied statistics·2026
Same author

BARTSIMP: Flexible spatial covariate modeling and prediction using Bayesian Additive Regression Trees.

Spatial and spatio-temporal epidemiology·2025
Same author

SPACE-TIME SMOOTHING MODELS FOR SUBNATIONAL MEASLES ROUTINE IMMUNIZATION COVERAGE ESTIMATION WITH COMPLEX SURVEY DATA.

The annals of applied statistics·2025
Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 17, 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

10.7K

Adaptive Gaussian Markov random fields for child mortality estimation.

Serge Aleshin-Guendel1, Jon Wakefield2,3

  • 1Center for Statistical Research and Methodology, U.S. Census Bureau, 4600 Silver Hill Road, Washington, DC 20233, United States.

Biostatistics (Oxford, England)
|August 5, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model to improve under-5 mortality rate (U5MR) estimates in areas with expected mortality shocks. The enhanced model provides more accurate U5MR data for public health planning.

Keywords:
Gaussian Markov random fieldschild mortalityspatio-temporal smoothingunder-5 mortality rate

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
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

10.1K

Related Experiment Videos

Last Updated: Jun 17, 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

10.7K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
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

10.1K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Demography

Background:

  • Under-5 mortality rate (U5MR) is a key health indicator, often derived from household surveys in low- and middle-income countries.
  • Spatio-temporal disaggregation of survey data can yield unstable U5MR estimates, requiring smoothing models.
  • Existing models may oversmooth U5MR, failing to capture localized mortality shocks.

Purpose of the Study:

  • To develop an advanced spatial and temporal smoothing model for U5MR estimation.
  • To incorporate knowledge of expected mortality shocks into Gaussian Markov random field models.
  • To improve the accuracy of U5MR estimates, particularly in regions experiencing unusual mortality events.

Main Methods:

  • Development of a spatial and temporal smoothing approach using Gaussian Markov random field models.
  • Incorporation of expected mortality shocks into the statistical framework.
  • Simulation studies to compare the new model against traditional methods.
  • Application of the model to estimate U5MR in Rwanda from 1985 to 2019.

Main Results:

  • The proposed model demonstrates potential to outperform existing methods that do not account for mortality shocks.
  • Simulation results indicate improved accuracy when shocks are present.
  • The model was successfully applied to estimate U5MR in Rwanda, a period including significant historical events.

Conclusions:

  • The novel Gaussian Markov random field model offers a more realistic approach to U5MR estimation.
  • Accounting for expected mortality shocks enhances the precision of U5MR estimates.
  • This methodology can improve public health surveillance and intervention strategies in vulnerable populations.