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

Cancer Survival Analysis01:21

Cancer Survival Analysis

532
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
532
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

410
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...
410
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

726
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:
726
Cluster Sampling Method01:20

Cluster Sampling Method

13.7K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.7K
Sampling Plans01:23

Sampling Plans

691
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
691
Actuarial Approach01:20

Actuarial Approach

199
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,...
199

You might also read

Related Articles

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

Sort by
Same author

Heart Disease in Survivors of Childhood Cancer: Data From the German Childhood Cancer Registry and Statutory Health Insurance Carriers (the VersKiK Study).

Deutsches Arzteblatt international·2026
Same author

Endocrine and metabolic late effects in childhood cancer survivors in Germany: the VersKiK study.

European journal of endocrinology·2026
Same author

Endocrine and metabolic late-effects in childhood cancer survivors in Germany: the VersKiK-Study.

European journal of endocrinology·2026
Same author

Incidence patterns and temporal trends of childhood cancer in Germany, 1980-2019: Forty years of childhood cancer registration in Germany.

International journal of cancer·2025
Same author

Anthracycline-Induced Cardiomyopathy After Nephro-/Neuroblastoma in Childhood: The Importance of Cardiological Reference Assessment.

Cancer medicine·2025
Same author

Broadband frequency-doubling of a swept-source laser from 1550 nm to 775 nm using a fan-out crystal and application in 2kHz LiDAR ranging.

Optics express·2025

Related Experiment Video

Updated: Nov 24, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

358

Statistical methods for spatial cluster detection in childhood cancer incidence: A simulation study.

Michael M Schündeln1, Toni Lange2, Maximilian Knoll3

  • 1Pediatric Hematology and Oncology, Department of Pediatrics III, University Hospital Essen and the University of Duisburg-Essen, Essen, Germany.

Cancer Epidemiology
|December 28, 2020
PubMed
Summary

Detecting childhood cancer clusters is challenging. Evaluating multiple statistical methods is recommended for reliable identification of high-risk areas.

Keywords:
BayesianBesag York MolliéBesag-NewellChildhood cancerSpatial clusterSpatial scan statistic

More Related Videos

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

507
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.4K

Related Experiment Videos

Last Updated: Nov 24, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

358
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

507
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.4K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Pediatric Oncology

Background:

  • The existence of spatial clusters in childhood cancer incidence is debated.
  • Identifying such clusters is crucial for understanding disease etiology and developing preventive strategies.
  • This study evaluates statistical approaches for childhood cancer cluster detection.

Purpose of the Study:

  • To evaluate the performance of widely used statistical methods for detecting spatial clusters in childhood cancer incidence.
  • To systematically describe the operating characteristics of different cluster detection methods under various simulated scenarios.

Main Methods:

  • Simulated incidence data for childhood cancer and nephroblastoma in Germany.
  • Randomly assembled clusters with varying sizes and relative risks (RR).
  • Analysis using Besag-Newell method, spatial scan statistic, and Bayesian Besag-York-Mollié (BYM) with Integrated Nested Laplace Approximation (INLA).

Main Results:

  • Method performance varied significantly based on the simulated setting (cluster size, incidence, RR).
  • Higher sensitivity was observed with increasing cluster size, incidence, and RR.
  • The BYM method demonstrated higher specificity for minimally increased RR in most scenarios.
  • Performance was lower for the specific nephroblastoma scenario compared to all childhood cancers.

Conclusions:

  • Reliable inference on spatial childhood cancer clusters is challenging using single statistical approaches.
  • Applying multiple methods with known operating characteristics is recommended.
  • Critical discussion of joint evidence is essential for identifying high-risk clusters.