Related Experiment Video
Updated: Jun 2, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
[Application of zero-inflated models on regression analysis of count data: a study of sub-health symptoms]
1Cardiovascular Institute and Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Abstract:
To explore the goodness of fit about the zero-inflated (ZI) models in analyzing data related to sub-health symptoms in which the counts are non-negative integers. ZI models are conducted with Stata 11.0. The coefficient of a, Vuong test, O test and likelihood test are used to compare the goodness of fit for ZI models with the common used models such as passion model, negative binomial model. When α is 0.939, and the Z statistic of Vuong test is 32.08, P < 0.0001, which shows that there are too many zeros. The mean number of sub-health symptoms is 2.90, s = 3.85, O = 308.011, P < 0.001, s(2)>x, indicating that the data are over-dispersed. In addition, the optimum goodness of fit is found in zero-inflated negative binomial (ZINB) model with the largest log likelihood and the smallest AIC. ZINB seems the optimal model to study those over-dispersed count data with too many zeros.
Related Concept Videos
Regression Toward the Mean
Statistical Methods for Analyzing Epidemiological Data
Mechanistic Models: Compartment Models in Individual and Population Analysis
Truncation in Survival Analysis
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
Steps in Outbreak Investigation
Classification of Illness
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...