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

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.1K
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
1.1K
Study Design in Statistics01:15

Study Design in Statistics

10.1K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
10.1K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

628
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
628
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.4K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.4K
Causality in Epidemiology01:21

Causality in Epidemiology

1.8K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.8K
Biostatistics: Overview01:20

Biostatistics: Overview

937
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
937

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Mapping oral-systemic health relationships: a data-driven analysis of co-occurrence patterns in biomedical literature.

JAMIA open·2026
Same author

Digital health and consumer health informatics: past and future.

Medical research archives·2026
Same author

Evaluation Framework for Bruise Detection: Systematic ALS/White-Light Training and Skin-Tone Balancing with Deep Learning.

Sensors (Basel, Switzerland)·2026
Same author

Optimal insurance coverage and pricing of outpatient drugs in Iran: a cost- and chronicity-based adaptation of the vertical equity model.

International journal for equity in health·2026
Same author

The association of prenatal adiposity characteristics with early childhood overweight and obesity: findings from a large and diverse mother-child cohort.

International journal of obesity (2005)·2026
Same author

An Interoperable Vaccine Record: A Roadmap to Realization.

Vaccines·2026

Video Experimental Relacionado

Updated: Feb 24, 2026

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

10.9K

¿Qué decisiones afectan la distribución de cohortes en el análisis de datos de COVID-19?

Atefehsadat Haghighathoseini1, Janusz Wojtusiak1, Lemba Priscille Ngana1

  • 1George Mason University, Fairfax, VA, USA.

AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
PubMed
Resumen

El análisis de datos para la investigación de COVID-19 requiere cohortes de pacientes diversas. La toma de decisiones estratégicas en el preprocesamiento de datos es crucial para garantizar la representatividad de la cohorte y lograr resultados equitativos en salud.

Palabras clave:
Distribución de cohortesProcesamiento de datosToma de decisionesColaboración Nacional de Cohortes COVID (N3C)

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

15.4K
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

2.7K

Videos de Experimentos Relacionados

Last Updated: Feb 24, 2026

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

10.9K
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

15.4K
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

2.7K

Área de la Ciencia:

  • Salud Pública; Ciencia de Datos; Epidemiología

Sus antecedentes:

  • El análisis de datos es vital para comprender los impactos de COVID-19, pero los resultados contradictorios resaltan problemas con los datos.
  • La representatividad de la cohorte es esencial para obtener información precisa sobre diversas poblaciones de pacientes.
  • La investigación existente a menudo carece de transparencia sobre cómo la selección de cohortes influye en la distribución demográfica.

Objetivo del estudio:

  • Investigar cómo los procesos de toma de decisiones durante el preprocesamiento de datos afectan la diversidad de cohortes.
  • Analizar el impacto de estas decisiones en la representación demográfica (sexo, raza, etnia).
  • Subrayar la necesidad de estrategias informadas en el análisis de datos para resultados equitativos en salud.

Principales métodos:

  • Análisis de puntos de toma de decisiones en el preprocesamiento de datos y la construcción de cohortes.
  • Cuantificación de los cambios en la distribución demográfica en función de opciones específicas de manejo de datos.
  • Examen de la influencia de factores aparentemente no relacionados en la distribución de pacientes.

Principales resultados:

  • Las decisiones de preprocesamiento de datos aumentan significativamente la variabilidad en la representación demográfica.
  • Las variaciones observadas incluyen: representación femenina (0.77%-2.68%), raza negra (1.17%-5.15%) y etnia hispana/latina (5.84%-8.21%).
  • El momento y la selección del proveedor también impactan la distribución de pacientes y los resultados, independientemente de la demografía.

Conclusiones:

  • Las decisiones arbitrarias de datos pueden conducir a resultados sesgados y afectar la equidad en salud.
  • Es necesaria una toma de decisiones estratégica y basada en evidencia para un análisis de datos consistente y confiable.
  • Las estrategias informadas mejoran la utilización de recursos y promueven políticas de salud pública más equitativas.