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

Randomized Experiments01:13

Randomized Experiments

9.3K
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...
9.3K
Cluster Sampling Method01:20

Cluster Sampling Method

15.6K
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...
15.6K
Study Design in Statistics01:15

Study Design in Statistics

10.3K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
10.3K
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

1.5K
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
1.5K
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

368
Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
368
Blinding01:11

Blinding

4.1K
Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
4.1K

You might also read

Related Articles

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

Sort by
Same author

Ecological assessment of transdiagnostic clinical symptoms in serious mental illness with daily smartphone surveys.

Translational psychiatry·2026
Same author

Using smartphone surveys to predict next-week suicide attempts.

Journal of psychopathology and clinical science·2026
Same author

Screening for diabetes mellitus in the US population using neural network-based modeling and complex survey designs.

Statistical methods in medical research·2026
Same author

Describe Where You Are: Improving Noise-Robustness for Speech Emotion Recognition with Text Description of the Environment.

IEEE transactions on affective computing·2026
Same author

Associations between self-reported personal care products use and menstrual cycle length and regularity in a US digital cohort.

Environment international·2026
Same author

Low-Burden Detection of Clinical Worsening in Body Dysmorphic Disorder Using Smartphone Sensor and Demographic Data.

Behavior therapy·2026

Related Experiment Video

Updated: Mar 29, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

855

Incorporating Contact Network Structure in Cluster Randomized Trials.

Patrick C Staples1, Elizabeth L Ogburn2, Jukka-Pekka Onnela1

  • 1Department of Biostatistics, Harvard University, Boston, MA 02115, USA.

Scientific Reports
|December 4, 2015
PubMed
Summary
This summary is machine-generated.

New infectious disease trials need accurate power calculations. Ignoring how individuals mix between clusters can lead to underpowered studies, potentially missing effective treatments.

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K

Related Experiment Videos

Last Updated: Mar 29, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

855
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Randomized controlled trials (RCTs) are standard for evaluating treatment efficacy.
  • Cluster randomized trials (CRTs) are often used for infectious diseases, assigning groups (clusters) to interventions.
  • Trial power, the ability to detect a true effect, can be compromised by cluster structure and interactions.

Purpose of the Study:

  • To investigate the impact of within- and between-cluster interactions on statistical power in infectious disease CRTs.
  • To compare simulation-based power assessments with traditional formula-based methods.
  • To highlight the importance of accounting for network structure and cross-contamination in trial design.

Main Methods:

  • Simulated an infectious process across a network of clusters.
  • Varied parameters including within-cluster network structure, between-cluster mixing (cross-contamination), and infectivity.
  • Compared power calculations derived from simulations versus standard formulas.

Main Results:

  • Formula-based power calculations can be overly conservative with low between-cluster mixing.
  • Failure to account for moderate to high between-cluster mixing significantly reduces study power.
  • Within-cluster network structure influences power, particularly for infections spreading through highly connected individuals.

Conclusions:

  • Current formula-based power calculations may underestimate power in some scenarios and overestimate it in others if mixing is not considered.
  • Simulation-based approaches incorporating network information are crucial for accurate power assessment in CRTs for infectious diseases.
  • Empirical data on mixing patterns can improve the design and power estimations of future trials.