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

Cluster Sampling Method01:20

Cluster Sampling Method

15.1K
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...
15.1K
Sample Size Calculation01:19

Sample Size Calculation

6.8K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
6.8K
Sampling Plans01:23

Sampling Plans

1.1K
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
1.1K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.9K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.9K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.4K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.4K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.2K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
4.2K

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Use of Prokinetic Agents in Adult ICU Patients: An International Inception Cohort Study (PATIENCE).

Acta anaesthesiologica Scandinavica·2026
Same author

Developing a Core Outcome Measurement Set for Adult ICU Patients, the CoreMS-ICU-A Protocol.

Acta anaesthesiologica Scandinavica·2026
Same author

Preferences for Blood Glucose Management in Adult Intensive Care Unit Patients-An International Survey.

Acta anaesthesiologica Scandinavica·2026
Same author

ICU Clinicians' View on Platelet Transfusion Thresholds for a Future Trial-Protocol for an International Survey.

Acta anaesthesiologica Scandinavica·2026
Same author

Key Features of Contemporary Pilot and Feasibility Trials: Protocol for a Methodological Study.

Acta anaesthesiologica Scandinavica·2026
Same author

Key considerations for planning adaptive platform trials: part 1.

Journal of clinical epidemiology·2026
Same journal

Using an Open Science Checklist in Grant Proposal Reviews to Predict Reproducibility of Funded Publications.

Journal of clinical epidemiology·2026
Same journal

A comparison of five statistical methods used to analyse longitudinal EORTC QLQ-C30 quality of life scores in randomised controlled trials: a simulation study.

Journal of clinical epidemiology·2026
Same journal

Sample Size Determination for Decision-centered Pragmatic Trials.

Journal of clinical epidemiology·2026
Same journal

Many multicenter randomized controlled trials do not account for center effect: a methodological review.

Journal of clinical epidemiology·2026
Same journal

Patient Acceptability of the Modified Zelen Approach to Randomized Trials - A Survey of the CAPS THA Cohort.

Journal of clinical epidemiology·2026
Same journal

Corrigendum to SPICE-GRADE: simultaneous processing of indirect causal evidence in complex pathways using GRADE - an exploratory case study. [Journal of Clinical Epidemiology, 194C (2026) 112219].

Journal of clinical epidemiology·2026
Ver todos los artículos relacionados

Video Experimental Relacionado

Updated: Feb 25, 2026

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

36.0K

Estimación de tamaños de muestra efectivos utilizando errores estándar de análisis apropiados de ensayos

Anders Granholm1

  • 1Department of Intensive Care, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.

Journal of clinical epidemiology
|February 23, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Nuevos métodos estiman el tamaño de muestra efectivo (ESS) para ensayos aleatorizados por conglomerados (CRTs) sin necesidad del coeficiente de correlación intraconcurrental (ICC). Estos enfoques mejoran la interpretación y la síntesis de evidencia de los CRTs.

Palabras clave:
análisisensayos clínicosensayos aleatorizados por conglomeradosagrupacióntamaños de muestra efectivosmeta-análisis

Más Videos Relacionados

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.5K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.3K

Videos de Experimentos Relacionados

Last Updated: Feb 25, 2026

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

36.0K
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.5K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.3K

Área de la Ciencia:

  • Bioestadística
  • Metodología de Ensayos Clínicos

Sus antecedentes:

  • Los ensayos aleatorizados por conglomerados (CRTs) a menudo producen menos información que los ensayos aleatorizados individualmente debido a la correlación intraconcurrental, lo que lleva a un tamaño de muestra efectivo (ESS) menor.
  • Ignorar la agrupación en los análisis puede resultar en estimaciones sesgadas y resultados estadísticos demasiado precisos (por ejemplo, valores p pequeños, intervalos de confianza estrechos).
  • La estimación del ESS utilizando coeficientes de correlación intraconcurrental (ICCs) es común, pero los ICCs con frecuencia se desconocen y deben asumirse.

Objetivo del estudio:

  • Presentar y evaluar dos métodos novedosos para estimar el ESS en CRTs.
  • Proporcionar herramientas prácticas para analizar datos de CRT sin requerir ICCs predefinidos.

Principales métodos:

  • Se desarrollaron dos enfoques distintos para estimar el ESS utilizando errores estándar de análisis conscientes de los conglomerados.
  • El Método 1 escala los recuentos por la relación de varianzas de análisis conscientes de los conglomerados frente a análisis simples.
  • El Método 2 emplea un procedimiento de optimización para ajustar las proporciones de eventos o las medias de grupo.

Principales resultados:

  • Ambos métodos presentados evitan con éxito la sobreprecisión en los análisis de datos resumidos a nivel de grupo de los CRT.
  • El segundo método corrige adicionalmente el posible sesgo introducido al ignorar la agrupación en el análisis.
  • Se realizaron comparaciones utilizando datos de tres CRT de ejemplo frente a análisis que no tuvieron en cuenta la agrupación.

Conclusiones:

  • Los métodos propuestos ofrecen herramientas valiosas para interpretar y sintetizar la evidencia de los CRTs.
  • Estos enfoques son aplicables incluso cuando no se informan los ICCs, siempre que se realicen análisis apropiados conscientes de los conglomerados.