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

Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

6.1K
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
6.1K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

615
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
615
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

285
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...
285
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

7.0K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
7.0K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.1K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.1K
Multimachine Stability01:25

Multimachine Stability

587
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
587

You might also read

Related Articles

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

Sort by
Same author

Affect and self-control outcomes following DLPFC and medial orbitofrontal rTMS in tobacco use disorder: Secondary analyses from a randomized sham-controlled trial.

Brain stimulation·2026
Same author

Lower leg EMG and H-reflex activity during double-leg hopping and landing.

Journal of neurophysiology·2026
Same author

Transcranial Direct Current Stimulation for Stroke Motor Recovery What the TRANSPORT2 Trial Taught Us.

Stroke·2026
Same author

Unblinded by the Night: Predictive Power for Complex Bayesian Adaptive Trials When Sight Privileges Vary.

Pharmaceutical statistics·2026
Same author

DLPFC rTMS is more effective than sham or orbitofrontal stimulation for smoking cessation and alters frontal brain activity: A double-blind, sham-controlled randomized clinical trial.

Journal of psychiatric research·2026
Same author

Extracranial vertebral artery stenosis patients that may benefit from stent placement: post-hoc analysis from randomized controlled trials.

Frontiers in neurology·2026

Related Experiment Video

Updated: Feb 17, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K

Assessing type I error and power of multistate Markov models for panel data-A simulation study.

Christy Cassarly1, Renee' H Martin1, Marc Chimowitz1

  • 1Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC.

Communications in Statistics: Simulation and Computation
|December 12, 2017
PubMed
Summary

Multistate Markov models effectively analyze ordinal outcomes in clinical trials, preserving statistical power and type I error rates even with limited non-adjacent state transitions in panel data.

Keywords:
Multistate modelspanel datapowerstroketype I error

More Related Videos

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

3.8K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.4K

Related Experiment Videos

Last Updated: Feb 17, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
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

3.8K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.4K

Area of Science:

  • Clinical Trials Methodology
  • Statistical Modeling
  • Longitudinal Data Analysis

Background:

  • Ordinal outcomes are frequent in clinical trials but often analyzed using methods that lead to information loss.
  • Dichotomizing scales at a single visit for primary analysis sacrifices valuable data from multiple follow-up visits.

Purpose of the Study:

  • To evaluate the performance of multistate Markov models for analyzing ordinal outcomes in clinical trials with panel data.
  • To investigate the type I error and power characteristics of these models, particularly with limited non-adjacent state transitions.

Main Methods:

  • Simulation studies were conducted to assess the statistical properties of multistate Markov models.
  • The models were applied to simulated panel data representing processes with limited transitions between non-adjacent states.

Main Results:

  • Multistate Markov models demonstrated appropriate control of type I error rates.
  • Adequate statistical power was achieved with modest sample sizes for panel data featuring limited non-adjacent state transitions.

Conclusions:

  • Multistate Markov models offer a robust approach for analyzing ordinal outcomes in clinical trials with longitudinal data.
  • These models preserve information and maintain statistical validity, even when state transitions are infrequent or non-adjacent.