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Related Concept Videos

Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
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,...
Hazard Rate01:11

Hazard Rate

The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...

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Related Experiment Video

Updated: Jun 21, 2026

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

Mortality rate and confidence interval estimation in humanitarian emergencies.

Kevin Sullivan1, S M Moazzem Hossain, Bradley A Woodruff

  • 1Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA. cdckms@sph.emory.edu

Disasters
|August 18, 2009
PubMed
Summary

Estimating mortality rates in humanitarian emergencies requires accurate confidence limits that account for complex survey designs. This study recommends the ratio command approach for precise mortality rate estimations.

Related Experiment Videos

Last Updated: Jun 21, 2026

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

Area of Science:

  • Public Health
  • Epidemiology
  • Biostatistics

Background:

  • Humanitarian emergencies necessitate frequent health status assessments using complex survey designs like cluster sampling.
  • Mortality rates are key indicators estimated in these surveys, requiring precise confidence limits.
  • Accurate confidence limits must account for complex survey designs and employ acceptable methodologies.

Purpose of the Study:

  • To describe methods for calculating confidence limits for mortality rates from complex survey designs.
  • To evaluate different software programs and statistical approaches for confidence limit calculation.
  • To recommend an optimal method for estimating mortality rates and their confidence limits.

Main Methods:

  • The study describes the calculation of confidence limits for mortality rates using complex sampling designs.
  • It demonstrates examples using SAS, SPSS, and Epi Info software.
  • Three confidence interval methods were examined: ratio command, modified rate, and modified proportion approaches.

Main Results:

  • The paper details the calculation of confidence limits for mortality rates derived from complex survey data.
  • Software programs like SAS, SPSS, and Epi Info were utilized for demonstration.
  • The ratio command approach, modified rate approach, and modified proportion approach were compared.

Conclusions:

  • The ratio command approach is recommended for estimating mortality rates with confidence limits in complex survey designs.
  • Ensuring confidence limits account for survey design is crucial for accurate mortality rate estimation.
  • This research provides practical guidance for public health professionals conducting emergency surveys.