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

Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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 Guinness...
Probability Laws01:49

Probability Laws

Overview
Actuarial Approach01:20

Actuarial Approach

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,...
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...

You might also read

Related Articles

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

Sort by
Same author

Perfluorooctanoic acid and cancer incidence: an updated investigation of a cohort in the mid-Ohio Valley.

Environment international·2026
Same author

Circulating pre-diagnostic metabolites and risk of hepatocellular carcinoma and intrahepatic cholangiocarcinoma: a population-based study of 12 cohorts.

Journal of the National Cancer Institute·2026
Same author

Artificially Sweetened and Sugar-Sweetened Beverage Intake and Risk of Liver Cancer.

JAMA network open·2026
Same author

Cessation of Betel Quid Chewing, Smoking, and Alcohol Drinking and Risk of Oral Precancer and Oral Cancer.

JCO global oncology·2026
Same author

Sex differences in cancer incidence persist across race and ethnicity.

Biology of sex differences·2026
Same author

Sociodemographic characteristics of populations living near industrial land disposals of known and suspected carcinogens across the United States.

Journal of exposure science & environmental epidemiology·2026

Related Experiment Video

Updated: May 9, 2026

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

Estimating sibling recurrence risk in population sample surveys.

Barry I Graubard1, Monroe G Sirken

  • 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Md., USA.

Human Heredity
|August 8, 2013
PubMed
Summary

Network sampling offers a way to estimate sibling recurrence risk (SRR) for diseases like diabetes. This method helps provide more accurate, unbiased estimates of familial disease aggregation from household survey data.

More Related Videos

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

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

Related Experiment Videos

Last Updated: May 9, 2026

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

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

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

Area of Science:

  • Genetic epidemiology
  • Survey research methodology
  • Public health

Background:

  • Sibling recurrence risk (SRR) measures familial disease aggregation, crucial for identifying disease susceptibility genes.
  • Traditional SRR estimation from family studies can be biased due to non-random sampling.
  • Network sampling in household surveys provides a novel approach to unbiased SRR estimation.

Purpose of the Study:

  • To introduce and evaluate network sampling methods for estimating sibling recurrence risk (SRR) and SRR ratio.
  • To address the bias inherent in traditional SRR estimation methods from family-based studies.
  • To demonstrate the utility of network sampling for population-based disease aggregation studies.

Main Methods:

  • Two network sampling approaches for sibship ascertainment were analyzed: self-reporting and affected-individual reporting.
  • Development of network estimators for SRR and SRR ratio, including standard error estimation.
  • Application of methods using 1976 National Health Interview Survey data on sibling diabetes status.

Main Results:

  • The SRR ratio for diabetes among living siblings was estimated at 5.79% (RSE 5.12%).
  • Including deceased siblings, the SRR ratio for diabetes increased to 7.66% (RSE 3.76%).
  • These results demonstrate the feasibility of network sampling for disease recurrence studies.

Conclusions:

  • Network sampling estimators are effective for obtaining population-level estimates of SRR and SRR ratio.
  • This methodology is applicable to various diseases, including diabetes, for assessing familial aggregation.
  • Network sampling enhances the accuracy of familial disease risk assessment in epidemiological studies.