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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

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...
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...
Hazard Ratio01:12

Hazard Ratio

The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial evaluating a...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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:

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Bayesian hierarchical models for cost-effectiveness analyses that use data from cluster randomized trials.

Richard Grieve1, Richard Nixon, Simon G Thompson

  • 1Health Services Research Unit, London School of Hygiene and Tropical Medicine, London, UK. richard.grieve@lshtm.ac.uk

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|August 14, 2009
PubMed
Summary
This summary is machine-generated.

Ignoring cluster effects in cost-effectiveness analyses (CEA) alongside cluster randomized trials (CRTs) leads to incorrect inferences. New Bayesian hierarchical models (BHMs) account for clustering, providing more accurate cost-effectiveness estimates and uncertainty measures.

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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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Last Updated: Jun 21, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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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

Area of Science:

  • Health economics
  • Biostatistics
  • Clinical trial methodology

Background:

  • Cost-effectiveness analyses (CEA) are often conducted alongside cluster randomized trials (CRTs).
  • Standard regression models assume independent observations, which is violated due to correlated costs and outcomes within clusters in CRTs.
  • This violates assumptions of standard bivariate regression models, potentially leading to biased results.

Purpose of the Study:

  • To develop a flexible Bayesian hierarchical modeling (BHM) framework for CEA in CRTs.
  • To extend existing Bayesian bivariate models to specifically address CRT clustering.
  • To compare estimates from BHMs with standard regression models that ignore clustering.

Main Methods:

  • Developed new Bayesian hierarchical models (BHMs) allowing for heterogeneity in means and variances across clusters.
  • Applied BHMs to data from a large CRT evaluating interventions for postnatal depression.
  • Compared cost-effectiveness estimates derived from BHMs versus standard bivariate regression models.

Main Results:

  • BHMs revealed significant cost heterogeneity across clusters (intracluster correlation coefficient = 0.17).
  • BHMs produced substantially different point estimates and increased uncertainty in cost-effectiveness compared to standard models.
  • Ignoring clustering led to altered and potentially misleading cost-effectiveness inferences.

Conclusions:

  • Failure to account for clustering in CRTs can result in incorrect cost-effectiveness conclusions.
  • The proposed BHMs offer a flexible and robust framework for CEA in CRTs.
  • These methods are broadly applicable to CEA utilizing CRT designs.