Related Experiment Video

Updated: Aug 15, 2026

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

Sample size estimation in occupational mortality studies with use of confidence interval theory

I Gordon

    American Journal of Epidemiology
    |January 1, 1987
    PubMed

    More Related Videos

    Establishing a Competing Risk Regression Nomogram Model for Survival Data04:57

    Establishing a Competing Risk Regression Nomogram Model for Survival Data

    Published on: October 23, 2020

    An R-Based Landscape Validation of a Competing Risk Model05:37

    An R-Based Landscape Validation of a Competing Risk Model

    Published on: September 16, 2022

    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...
    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...
    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...
    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,...
    Kaplan-Meier Approach01:24

    Kaplan-Meier Approach

    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,...