Related Experiment Video

Updated: Aug 26, 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

The effect of assuming independence in applying Bayes' theorem to risk estimation and classification in diagnosis

E Russek, R A Kronmal, L D Fisher

    Computers and Biomedical Research, an International Journal
    |December 1, 1983
    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

    Probability Laws01:49

    Probability Laws

    Overview
    Introduction to Test of Independence01:21

    Introduction to Test of Independence

    In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
    The test statistic for a test of independence is similar to that of a goodness-of-fit test:
    Determination of Expected Frequency01:08

    Determination of Expected Frequency

    Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
    Biostatistics: Overview01:20

    Biostatistics: Overview

    Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
    Discrete variables are...
    Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

    Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

    Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
    Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
    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,...