Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Hospitals-II00:59

Hospitals-II

849
Hospitals provide inpatient and outpatient services. Inpatient services provide care to patients that stay in the hospital for an extended period, ranging from days to months. Examples of inpatient services include intensive care units, hospital wards, or surgeries. Outpatient services provide care to patients who come to a hospital for a diagnostic or treatment but do not stay overnight —for example, diagnostic tests, surgical procedures, or health education.
Nurses that work in...
849
Hospitals-I01:28

Hospitals-I

992
Hospitals offer medical and surgical care to the sick and injured, along with accommodation while they recover. At the same time, they also provide outpatient, emergency, psychiatric, and rehabilitation services to meet various community needs. In addition to providing medical care, hospitals also act as hubs for medical research and training. Hospitals use clinical procedures and evidence-based practice standards to deliver patient care. To deliver safe and efficient care, a nurse must stay up...
992
Cluster Sampling Method01:20

Cluster Sampling Method

13.1K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.1K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.3K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.3K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

600
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:
600
Sampling Plans01:23

Sampling Plans

324
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
324

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Clinician characteristics associated with CT use in children with minor blunt head trauma at very low risk for clinically important traumatic brain injuries.

Emergency medicine journal : EMJ·2026
Same author

Psychometric Evaluation of a NICU Version of the Family-Centered Care Experience Survey.

Hospital pediatrics·2026
Same author

Video vs Telephone Consultations for Pediatric Quality of Care in Emergency Departments.

Pediatrics·2026
Same author

GAIN: a multicenter pilot protocol for early initiation of enteral feeds in neonates with simple gastroschisis.

Pilot and feasibility studies·2026
Same author

Views on democracy and political violence in the United States in 2025: findings from a nationally representative survey.

Injury epidemiology·2026
Same author

Support for authoritarianism and use of force by and against the federal government in the United States in mid-2025: findings from a nationally representative survey.

Injury epidemiology·2026

Related Experiment Video

Updated: Oct 11, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.0K

Marginal indirect standardization using latent clustering on multiple hospitals.

Yifei Wang1,2, Daniel J Tancredi3, Diana L Miglioretti4

  • 1Phili R. Lee Institute for Health Policy Studies, University of California, San Francisco, California.

Statistics in Medicine
|December 6, 2021
PubMed
Summary

This study introduces a new statistical method for hospital profiling using latent classes to improve indirect standardization. The novel approach offers less biased estimates and narrower uncertainty intervals for the standardized incidence ratio.

Keywords:
hospital profilingindirect standardizationlatent class analysis

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

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

Published on: January 8, 2020

14.7K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K

Related Experiment Videos

Last Updated: Oct 11, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.0K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

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

Published on: January 8, 2020

14.7K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K

Area of Science:

  • Biostatistics
  • Health Services Research
  • Epidemiology

Background:

  • Indirect standardization is crucial for hospital profiling.
  • Existing methods rely on a synthetic comparison hospital with equal Dirichlet concentration parameters.
  • Limitations exist when reference hospital characteristics are not fully utilized.

Purpose of the Study:

  • To propose a novel indirect standardization method using latent classes.
  • To improve upon the existing method by allowing unequal Dirichlet concentration parameters.
  • To enhance the accuracy of hospital profiling and standardized incidence ratio estimation.

Main Methods:

  • Developed a latent class model for reference hospitals.
  • Incorporated hospital-level characteristics to inform latent class membership and weights.
  • Applied the method to a study on high-radiation computed tomography (CT) exams and simulations.

Main Results:

  • The novel method yields less biased point estimates for the standardized incidence ratio.
  • Achieved narrower uncertainty intervals compared to the existing approach.
  • Demonstrated superiority in both real-world data application and simulation studies.

Conclusions:

  • The proposed latent class approach enhances indirect standardization for hospital profiling.
  • This method provides more accurate and reliable standardized incidence ratios.
  • It offers a valuable advancement for comparative effectiveness research and healthcare quality assessment.