Jove
Visualize
Contact Us

Related Concept Videos

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

407
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,...
407
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

733
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:
733
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

412
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...
412
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

259
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.
259
Applications of Life Tables01:22

Applications of Life Tables

185
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
185
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

833
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...
833

You might also read

Related Articles

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

Sort by
Same author

Understanding smart home automation acceptance through users' lifestyles and perceived difficulty of use: Evidence from South Korea.

PloS one·2026
Same author

Evaluating Respiratory Muscle Strength in Sarcopenia Screening among Older Men in South Korea: A Retrospective Analysis.

The world journal of men's health·2024
Same author

Ethical Issues of Digital Twins for Personalized Health Care Service: Preliminary Mapping Study.

Journal of medical Internet research·2022
See all related articles
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 Experiment Video

Updated: Nov 25, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

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

Published on: January 8, 2020

14.9K

Missing-Data Handling Methods for Lifelogs-Based Wellness Index Estimation: Comparative Analysis With Panel Data.

Ki-Hun Kim1,2, Kwang-Jae Kim3

  • 1Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands.

JMIR Medical Informatics
|December 17, 2020
PubMed
Summary
This summary is machine-generated.

Low-rank approximation-based imputation effectively handles missing health behavior data in wellness index development. This method minimizes bias in coefficient estimation, outperforming other techniques across various missing data proportions.

Keywords:
health behavior lifelogslifelogs-based wellness indexmissing-data handlingpanel datasmart wellness service

More Related Videos

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.5K
The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan
11:58

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Published on: June 29, 2018

9.8K

Related Experiment Videos

Last Updated: Nov 25, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

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

Published on: January 8, 2020

14.9K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.5K
The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan
11:58

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Published on: June 29, 2018

9.8K

Area of Science:

  • Health Informatics
  • Biostatistics
  • Data Science

Background:

  • Lifelogs-based wellness index (LWI) uses health behavior data (e.g., steps, sleep) to calculate wellness scores.
  • Panel data enables LWI estimation, controlling for unobserved variables to reduce bias.
  • Missing data in lifelogs, common with smart devices, can introduce bias into LWI coefficients.

Purpose of the Study:

  • To identify the most suitable method for handling missing data in the estimation of LWIs using panel data.
  • To compare the performance of various missing-data handling techniques in the context of LWI development.

Main Methods:

  • Six methods were evaluated: listwise deletion, mean imputation, EM-based multiple imputation, PMM-based multiple imputation, k-NN imputation, and low-rank approximation.
  • Simulated missing data (1%–80%) was introduced into a 4-week, 41-student panel dataset of health behavior lifelogs.
  • Bias was measured by comparing coefficients estimated from completed datasets with those from a complete reference dataset.

Main Results:

  • Low-rank approximation, PMM-based multiple imputation, and EM-based multiple imputation excelled at 1%–30% missing data.
  • Low-rank approximation and PMM-based multiple imputation performed best at 31%–60% missing data.
  • Only low-rank approximation was acceptable for over 60% missing data.

Conclusions:

  • Low-rank approximation-based imputation is the superior method for handling missing data in LWI estimation, irrespective of missing data proportion.
  • Its effectiveness is generalizable to other low-rank panel datasets of health behavior lifelogs.
  • This finding provides guidance for reducing coefficient bias in future LWI development.