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Related Concept Videos

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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,...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
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,...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...

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Related Experiment Video

Updated: Jul 4, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Estimating mean sojourn time and screening sensitivity using questionnaire data on time since previous screening.

Harald Weedon-Fekjaer1, Bo H Lindqvist, Lars J Vatten

  • 1Kreftregisteret, Institute of Population-based Cancer Research, Montebello, N-0310 Oslo, Norway. harald.weedon-fekjaer@kreftregisteret.no

Journal of Medical Screening
|June 25, 2008
PubMed
Summary

This study introduces a new method to estimate mean sojourn time (MST) and screening test sensitivity (STS) using questionnaire data. The findings confirm previous results of long MST and low STS in breast cancer screening.

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Published on: August 1, 2019

Area of Science:

  • Oncology
  • Biostatistics
  • Public Health

Background:

  • Estimating mean sojourn time (MST) and screening test sensitivity (STS) typically relies on Markov models and incidence data.
  • Challenges exist in cancer registration and opportunistic screening, impacting the accuracy of interval cancer data.
  • Accurate estimation of MST and STS is crucial for evaluating screening program effectiveness.

Purpose of the Study:

  • To develop and apply a novel approach for estimating MST and STS using questionnaire data.
  • To address limitations in traditional methods for calculating these key screening metrics.
  • To assess the reliability of questionnaire data for evaluating breast cancer screening programs.

Main Methods:

  • A modified Markov model was used to derive formulas for expected cases based on time since the last screening.
  • Mean square regression was employed to estimate MST and STS.
  • Data from 336,533 women in the Norwegian Breast Cancer Screening Programme (NBCSP) were analyzed.

Main Results:

  • The new approach demonstrated a satisfactory model fit compared to previous methods.
  • Estimated MST was 5.6 years for women aged 50-59 and 6.9 years for women aged 60-69.
  • Estimated STS was 55% for the younger group and 60% for the older group.

Conclusions:

  • Questionnaire data on time since previous screening can effectively estimate MST and STS.
  • The study confirmed previously reported findings of long MST and low STS.
  • The model's sensitivity to assumptions about breast cancer incidence and constant STS over time was noted.