Related Experiment Video
Updated: Aug 14, 2026
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
Prior distributions for the intracluster correlation coefficient, based on multiple previous estimates, and their
Rebecca M Turner1, Simon G Thompson, David J Spiegelhalter
1MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK. rebecca.turner@mrc-bsu.cam.ac.uk
Clinical Trials (London, England)
|November 11, 2005
Summary
Researchers can now formally construct informative prior distributions for the intracluster correlation coefficient (ICC) using existing study data. This method improves cluster randomized trial planning by better utilizing previous ICC estimates.
Area of Science:
- Biostatistics
- Clinical Trials
- Epidemiology
Background:
- Intracluster correlation coefficient (ICC) estimates are crucial for planning cluster randomized trials.
- Current methods for selecting an anticipated ICC value are informal and rely on previous estimates.
- A formal approach is needed to leverage the wealth of available ICC data.
Purpose of the Study:
- To develop a formal method for constructing informative prior distributions for the ICC.
- To improve the precision of anticipated ICC values for cluster randomized trial design.
- To acknowledge and incorporate uncertainty surrounding ICC estimates.
Main Methods:
- Utilizing multiple relevant ICC estimates from completed studies.
- Developing a preferred model that accounts for imprecision in individual ICC estimates and similarity within studies.
- Downweighting less relevant previous ICC estimates based on outcome or population type.
Main Results:
- The proposed method formally constructs informative prior distributions for the ICC.
- Downweighting less relevant estimates increases the precision of the anticipated ICC.
- The approach allows for acknowledging uncertainty in ICC values during trial design.
Conclusions:
- The developed methods provide a practical way to summarize available ICC information into an informative prior distribution.
- This aids in selecting appropriate trial designs that ensure adequate power across likely ICC ranges.
- The informative prior can also be incorporated into Bayesian analyses of trial data.
Related Concept Videos
01:20Cluster 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...
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...
01:13Randomized Experiments
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
01:26Distributions to Estimate Population Parameter
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
01:03Prediction Intervals
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...
01:23Types 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...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
01:16Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...