Related Experiment Video

Updated: Aug 18, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

An application of multivariate ratio methods for the analysis of a longitudinal clinical trial with missing data

W M Stanish, D B Gillings, G G Koch

    Biometrics
    |June 1, 1978
    PubMed

    More Related Videos

    Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index06:55

    Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

    Published on: January 8, 2020

    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

    Related Concept Videos

    Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

    Mechanistic Models: Compartment Models in Individual and Population Analysis

    Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
    Statistical Methods for Analyzing Epidemiological Data01:25

    Statistical Methods for Analyzing Epidemiological Data

    Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
    Longitudinal Studies01:26

    Longitudinal Studies

    Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
    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...
    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,...
    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...