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

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.5K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.5K
Clinical Trials01:16

Clinical Trials

10.4K
Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
10.4K
Clinical Trials: Overview01:11

Clinical Trials: Overview

4.9K
Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
4.9K
Cluster Sampling Method01:20

Cluster Sampling Method

14.5K
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...
14.5K
Longitudinal Research02:20

Longitudinal Research

13.2K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
13.2K
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

403
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
403

You might also read

Related Articles

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

Sort by
Same author

Jiedu Huoxue decoction inhibits cardiomyocyte apoptosis via PTEN/AKT/GSK3β-mediated mitochondrial dynamics in myocardial infarction: an integrative study of network pharmacology, transcriptomics and molecular docking.

Chinese medicine·2026
Same author

Semi-automatic mask guidance enhances 3D tumor segmentation in medical imaging.

Communications medicine·2026
Same author

Prognostic value of PIVKA-II and routine perioperative laboratory indices for recurrence prediction after curative resection of hepatocellular carcinoma.

Clinica chimica acta; international journal of clinical chemistry·2026
Same author

A Novel Secondary-Outcome Approach to Estimating Primary Causal Effects With Unmeasured Confounders.

Biometrical journal. Biometrische Zeitschrift·2026
Same author

HSPA5 induces autophagy targeting VP2 through the PERK-eIF2α signaling pathway to inhibit SVA replication.

Journal of virology·2026
Same author

Comments on "Feasibility of Viscosity Imaging and Shear Wave Elastography for Diagnosing Diabetic Peripheral Neuropathy".

Korean journal of radiology·2026

Related Experiment Video

Updated: Jan 28, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

8.9K

Profile clustering in clinical trials with longitudinal and functional data methods.

Hangjun Gong1, Xiaolei Xun2, Yingchun Zhou1

  • 1a Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics , East China Normal University , Shanghai , P.R. China.

Journal of Biopharmaceutical Statistics
|February 28, 2019
PubMed
Summary

This study compares clustering methods for repeated measurement data in clinical trials. It found specific longitudinal and functional data analysis methods effectively cluster patient profiles for better interpretation.

Keywords:
Clustering analysisfunctional datalongitudinal datanonparametricprofile data

More Related Videos

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

2.2K
Characterizing Exon Skipping Efficiency in DMD Patient Samples in Clinical Trials of Antisense Oligonucleotides
05:16

Characterizing Exon Skipping Efficiency in DMD Patient Samples in Clinical Trials of Antisense Oligonucleotides

Published on: May 7, 2020

7.3K

Related Experiment Videos

Last Updated: Jan 28, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

8.9K
In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

2.2K
Characterizing Exon Skipping Efficiency in DMD Patient Samples in Clinical Trials of Antisense Oligonucleotides
05:16

Characterizing Exon Skipping Efficiency in DMD Patient Samples in Clinical Trials of Antisense Oligonucleotides

Published on: May 7, 2020

7.3K

Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Repeated measurements are common in medical and pharmaceutical research.
  • Analyzing continuous data with multiple measurement points requires specialized methods.

Purpose of the Study:

  • To provide an overview of clustering methods for repeated measurement data.
  • To compare the performance of longitudinal and functional data analysis approaches for clustering patient profiles in clinical trials.

Main Methods:

  • Overview of clustering techniques for repeated measurement data.
  • Comparison of three longitudinal data analysis methods.
  • Comparison of two functional data analysis methods.
  • Extensive simulation studies to evaluate method performance.

Main Results:

  • Simulation studies identified methods with appropriate properties for clustering repeated measurement data.
  • The selected methods were applied to real clinical trial data.
  • Interpretable results were achieved by applying appropriate clustering methods.

Conclusions:

  • Clustering methods for repeated measurement data are crucial for analyzing clinical trial data.
  • Both longitudinal and functional data analysis offer valuable approaches for patient profile clustering.
  • The choice of method depends on data characteristics and desired interpretability.