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

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

175
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
175
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

6.5K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
6.5K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

259
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...
259
Confidence Intervals01:21

Confidence Intervals

7.1K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
7.1K
Critical Values01:31

Critical Values

7.2K
A critical value is a definite value obtained from a particular probability distribution at a predecided confidence level (or a predecided significance level) for a given population parameter. The critical value provides demarcation that separates the sample statistics that are likely to occur from the ones that are unlikely to occur based on the given probability distribution and the population parameter to be estimated. The critical value for normal distribution is obtained from the z...
7.2K
Confidence Coefficient01:24

Confidence Coefficient

7.8K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
7.8K

You might also read

Related Articles

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

Sort by
Same author

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same author

Maximized sequential probability ratio test regression.

Biometrics·2025
Same author

Matching ratio and sample size for optimal sequential testing with binomial data.

Statistical methods in medical research·2023
Same author

The person-time ratio distribution for the exact monitoring of adverse events: Historical vs surveillance Poisson data.

Statistics in medicine·2023
Same author

Maximum precision estimation for a step-stress model using two-stage methodologies.

Journal of applied statistics·2022
Same author

Exact sequential test for clinical trials and post-market drug and vaccine safety surveillance with Poisson and binary data.

Statistics in medicine·2021
Same journal

A joint model for a longitudinal outcome and a progressive multistate model under a mixed observation scheme.

Statistical methods in medical research·2026
Same journal

Efficient semi-supervised estimation of optimal individualized treatment regimes with survival outcome.

Statistical methods in medical research·2026
Same journal

Asymptotic online FWER control for dependent test statistics.

Statistical methods in medical research·2026
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
See all related articles

Related Experiment Video

Updated: Aug 28, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K

Bounded-width confidence interval following optimal sequential analysis of adverse events with binary data.

Ivair R Silva1, Yan Zhuang2

  • 1Department of Statistics, 28115Federal University of Ouro Preto, Ouro Preto, MG, Brazil.

Statistical Methods in Medical Research
|September 19, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a linear programming method for optimizing sequential testing in drug safety surveillance. It minimizes the time to detect adverse events, improving vaccine safety monitoring.

Keywords:
Estimate precisionclinical trialscoverage probabilityexpected time to signallinear programmingpost-market safety surveillance

More Related Videos

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

201
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K

Related Experiment Videos

Last Updated: Aug 28, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

201
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacovigilance

Background:

  • Optimizing sample size and detection time is crucial in sequential testing, especially for post-market drug and vaccine safety surveillance of adverse events.
  • Precision of the relative risk estimator at analysis termination is a key design consideration.

Purpose of the Study:

  • To present a linear programming framework for optimal alpha spending in sequential testing.
  • To minimize expected time to signal or expected sample size for adverse event detection.

Main Methods:

  • Developed a linear programming framework to determine optimal alpha spending strategies.
  • Applied the framework to sequential testing with binary data, considering confidence interval width and signaling thresholds.
  • Utilized real-world data from H1N1 vaccination adverse event monitoring.

Main Results:

  • The proposed framework enables optimal alpha spending to minimize expected time to signal or sample size.
  • The method supports designs with outer signaling and inner non-signaling thresholds.
  • Demonstrated the framework's utility using H1N1 vaccination safety data.

Conclusions:

  • The linear programming approach provides an effective method for optimizing sequential testing designs in pharmacovigilance.
  • This framework enhances the efficiency and precision of adverse event detection during post-market surveillance.
  • The study offers a valuable tool for improving drug and vaccine safety monitoring protocols.