Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

4.5K
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.
4.5K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

282
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%...
282
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

174
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,...
174
Bonferroni Test01:10

Bonferroni Test

2.8K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
2.8K
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

26.8K
There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
26.8K
Randomized Experiments01:13

Randomized Experiments

7.2K
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...
7.2K

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Safety-Driven Response Adaptive Randomization: An Application in Noninferiority Oncology Trials.

Statistics in medicine·2026
Same author

Sotatercept reduces bone morphogenetic protein signaling in patients with pulmonary arterial hypertension.

Science translational medicine·2026
Same author

A burn-in(g) question: How long should an initial equal randomization stage be before Bayesian response-adaptive randomization?

Statistical methods in medical research·2026
Same author

Drug Development for Pulmonary Arterial Hypertension: Unleashing the Potential of Single-Patient Studies Using Continuous Monitoring.

Pulmonary circulation·2025
Same author

Implementing response-adaptive randomisation in stratified rare-disease trials: Design challenges and practical solutions.

Statistical methods in medical research·2025
Same author

Implementing response-adaptive designs when responses are missing: Impute or ignore?

Statistical methods in medical research·2025

Video Experimental Relacionado

Updated: Sep 9, 2025

Errors as a Means of Reducing Impulsive Food Choice
07:07

Errors as a Means of Reducing Impulsive Food Choice

Published on: June 5, 2016

8.8K

Revisando las asignaciones óptimas para respuestas binarias: ideas de considerar el control de la tasa de error de

Lukas Pin1, Sofía S Villar1, William F Rosenberger2

  • 1MRC Biostatistics Unit, University of Cambridge, Robinson Way, Cambridge, CB2 0SR, United Kingdom.

Biometrics
|August 29, 2025
PubMed
Resumen

Este estudio introduce nuevos métodos de asignación óptimos para ensayos clínicos para controlar la tasa de error de tipo I. Estos métodos mejoran los resultados de los pacientes mediante la gestión robusta de la inflación estadística en los diseños de ensayos adaptativos.

Palabras clave:
La asignación de NeymanAsignación de RSHIRPrueba de Waldbeneficio para el pacientePrueba de puntuación

Más Videos Relacionados

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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

14.6K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Videos de Experimentos Relacionados

Last Updated: Sep 9, 2025

Errors as a Means of Reducing Impulsive Food Choice
07:07

Errors as a Means of Reducing Impulsive Food Choice

Published on: June 5, 2016

8.8K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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

14.6K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Área de la Ciencia:

  • Diseño del ensayo clínico
  • Estadísticas biológicas
  • Inferencia estadística

Sus antecedentes:

  • Los diseños adaptativos a la respuesta pueden inflar la tasa de error de tipo I, un problema que no está bien documentado.
  • Los métodos existentes para reducir la inflación de la tasa de error de tipo I en diseños adaptativos no son sólidos.

Objetivo del estudio:

  • Desarrollar nuevas proporciones óptimas de asignación para diseños adaptativos a la respuesta que controlen la inflación de la tasa de error de tipo I.
  • Abordar las limitaciones de los métodos existentes para gestionar la inflación estadística en ensayos clínicos.

Principales métodos:

  • Se obtuvieron dos proporciones óptimas de asignación utilizando la prueba de puntuación y los estimadores de muestras finitas.
  • Incorporó pruebas y estimadores estadísticos robustos en la formulación del problema de optimización.
  • Diseños evaluados mediante simulaciones utilizando datos de ensayos de fase inicial y de confirmación.

Principales resultados:

  • Las proporciones óptimas de asignación propuestas controlan efectivamente la inflación de la tasa de error de tipo I.
  • Los nuevos diseños ofrecen ventajas sustanciales en los resultados de los pacientes en comparación con los métodos existentes.
  • La prueba de puntuación y los estimadores de muestras finitas proporcionan una solución más robusta.

Conclusiones:

  • Los nuevos diseños de proporción óptima proporcionan un método sólido para controlar las tasas de error de tipo I en ensayos adaptativos.
  • Estos diseños pueden conducir a mejores resultados para los pacientes mientras se mantiene la integridad estadística.
  • El marco es adaptable a diversos tipos de resultados y estructuras de ensayos.