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

Introduction To Survival Analysis01:18

Introduction To Survival Analysis

414
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...
414
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

573
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:
573
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

8.0K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
8.0K
Study Design in Statistics01:15

Study Design in Statistics

9.3K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
9.3K
Survival Tree01:19

Survival Tree

170
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
170
Time-Series Graph00:54

Time-Series Graph

4.6K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.6K

You might also read

Related Articles

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

Sort by
Same author

Extreme heat and cause-specific risk of hospital admission in the adult population in England: a case time series analysis.

BMJ open·2026
Same author

Interaction Between Air Pollution and Genetic Predisposition to Blood Pressure and LDL-C on Cardiovascular Events.

European journal of preventive cardiology·2026
Same author

Minimum mortality temperature by cause of death and age group: A multi-country observational study (1990-2019).

Environmental research·2026
Same author

Temporal changes in mortality risk associated with PM<sub>10</sub> across 143 cities in 26 countries: a multicountry, multicity time-series study.

The Lancet. Planetary health·2026
Same author

Projecting climate change impacts on health: A tutorial integrating the latest climate and demographic scenarios.

Environmental epidemiology (Philadelphia, Pa.)·2026
Same author

The burden of premature births attributed to heat across 13 countries.

Environment international·2026
Same journal

Methods for incorporating test result information within the high-dimensional propensity score framework: application in UK electronic health record data.

BMC medical research methodology·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
See all related articles

Related Experiment Video

Updated: Sep 24, 2025

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

3.4K

A tutorial on the case time series design for small-area analysis.

Antonio Gasparrini1,2

  • 1Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine (LSHTM), 15-17 Tavistock Place, London, WC1H 9SH, UK. antonio.gasparrini@lshtm.ac.uk.

BMC Medical Research Methodology
|May 3, 2022
PubMed
Summary
This summary is machine-generated.

This study extends the case time series design for small-area epidemiological analyses. The flexible and efficient method effectively models complex temporal relationships using fine-resolution health data.

Keywords:
Case time seriesDistributed lag modelsSmall-areaStudy designTemperatureTime series

More Related Videos

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
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K

Related Experiment Videos

Last Updated: Sep 24, 2025

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

3.4K
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
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Geospatial Health

Background:

  • Growing availability of fine-resolution health and risk factor data drives small-area epidemiological analyses.
  • Methodological challenges include complex modeling, computational demands, and data linkage/harmonization across geographical levels.

Purpose of the Study:

  • To extend the case time series design for epidemiological analyses using small-area aggregated data.
  • To provide a flexible and computationally efficient analytical tool for fine-geographical-level data.

Main Methods:

  • Extension of the case time series design for aggregated small-area data.
  • Embedding longitudinal data structure within a self-matched case-only framework.
  • Application demonstrated using R code for real-data analysis.

Main Results:

  • The case time series design effectively models complex temporal relationships.
  • The method offers an efficient computational scheme for large, fine-geographical-level datasets.
  • A case study on London mortality risks from high temperatures is presented.

Conclusions:

  • The extended case time series design is a valuable tool for small-area analysis.
  • It combines modeling flexibility with computational efficiency.
  • Enables addressing diverse epidemiological questions with increasing fine-scale data.