Related Experiment Video
Updated: May 28, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Variance estimation and confidence intervals for the standardized mortality ratio with application to the assessment
Denis Talbot1, Thierry Duchesne, Jacques Brisson
1Département de mathématiques et de statistique, Université Laval, Canada.
Abstract:
The effect of a cancer screening program can be measured through the standardized mortality ratio (SMR) statistic. The numerator of the SMR is the observed number of deaths from the screened disease among participants in the screening program, whereas the denominator of the SMR is an estimate of the expected number of deaths in these participants under the assumption that the screening program has no effect. In this article, we propose a variance estimator for the denominator of the SMR when this expected number of deaths is estimated with Sasieni's method. We give both a general formula for this variance as well as formulas for specific disease incidence and survival estimators. We show how this new variance estimator can be used to build confidence intervals for the SMR. We investigate the coverage properties of various types of confidence intervals by simulation and find that intervals that make use of the proposed variance estimator perform well. We illustrate the method by applying it to the Québec Breast Cancer Screening program.
Related Concept Videos
Statistical Methods for Analyzing Epidemiological Data
Estimating Population Standard Deviation
Estimating Population Mean with Unknown Standard Deviation
William S. Gosset (1876–1937) of the Guinness...
Cancer Survival Analysis
Estimating Population Mean with Known Standard Deviation
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...
Variability: Analysis
The range is a simple measure of variability, indicating the difference between the highest and...

