Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Data Reporting and Recording01:24

Data Reporting and Recording

5.5K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
5.5K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

45.2K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
45.2K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

38.4K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
38.4K
Data Validation01:15

Data Validation

2.3K
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
2.3K
Data Validation01:03

Data Validation

7.0K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
7.0K
Data Collection II01:29

Data Collection II

10.2K
The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
10.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Ditch the drapes: Non-sterile substitution in proctology, a population-based cost and carbon life cycle analysis with early implementation audit.

Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland·2026
Same author

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026
Same author

Integrating oral health screening into general practice: validation study of the Oral Health Screener.

Scientific reports·2026
Same author

Time-Scale Target Parameters and Two-Step Estimation in Longitudinal Trials for Progressive Diseases.

Statistics in medicine·2026
Same author

Does Everyone Need CEA? A Case for Selective Omission After Colorectal Cancer Resection.

ANZ journal of surgery·2026
Same author

Target product profiles for treatments to delay or prevent symptomatic Alzheimer's disease.

Nature medicine·2026

相关实验视频

Updated: Feb 14, 2026

Gathering Self-Initiated Rat Behavioral Data to Characterize Post-Stroke Deficits
05:08

Gathering Self-Initiated Rat Behavioral Data to Characterize Post-Stroke Deficits

Published on: March 15, 2024

1.6K

在具有基线但没有基线后数据的参与者中处理缺失的数据.

Craig Mallinckrodt1, Ilya Lipkovich2, Samuel Dickson1

  • 1Pentara Corporation, Millcreek, Utah, USA.

Pharmaceutical statistics
|February 13, 2026
PubMed
概括

在临床试验中,处理没有基线后数据的参与者至关重要. 使用基线作为共变量,变化设置为零,有效控制错误和维持治疗效果估计功率的策略.

关键词:
临床试验是指临床试验中的临床试验.估计 估计 估计 估计有意治疗的意图.缺失的数据 缺失的数据

更多相关视频

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
06:05

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time

Published on: February 19, 2021

1.8K
Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

12.8K

相关实验视频

Last Updated: Feb 14, 2026

Gathering Self-Initiated Rat Behavioral Data to Characterize Post-Stroke Deficits
05:08

Gathering Self-Initiated Rat Behavioral Data to Characterize Post-Stroke Deficits

Published on: March 15, 2024

1.6K
The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
06:05

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time

Published on: February 19, 2021

1.8K
Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

12.8K

科学领域:

  • 生物统计学 生物统计学
  • 临床试验方法论 临床试验方法论
  • 数据分析 数据分析

背景情况:

  • 随机分配给治疗但缺乏基线后数据的参与者在临床试验中构成了重大挑战.
  • 保持随机化完整性需要将这些参与者纳入分析.
  • 估计停止参与者的假设结果是必不可少的,因为缺乏关于事件和结果的数据.

研究的目的:

  • 评估临床试验中处理没有基线后数据的参与者的各种分析策略.
  • 确定保留随机化的方法,同时提供公正的治疗效果估计.
  • 为了比较基于归算和基于概率的分析在模拟和现实数据中的性能.

主要方法:

  • 不同的统计模型的比较,包括使用基线作为共变量,限制基线值和不受约束的分析.
  • 使用模拟研究和对真实临床试验数据的分析.
  • 专注于一种策略,将零的变化分配到基线后的第一个访问,并将基线作为共变量.

主要成果:

  • 与无约束模型相比,将基线作为共变量或约束基线值纳入的模型显示出类似的,优异的结果.
  • 将变化设置为零,并使用基线作为共变量的策略有效控制了I型错误,并显示出强大的力量.
  • 当失踪是随机的或与治疗相关的时,治疗对比仍然是公正的,但与结果相关的失踪引入了群体内偏差,尽管它在各支队伍之间平衡.

结论:

  • 对于管理临床试验中没有基线后数据的参与者来说,没有单一的分析方法是普遍最佳的.
  • 选择方法应根据临床试验的具体特征和缺失数据的性质进行调整.
  • 使用基线作为共同变量的拟议策略为处理这些参与者提供了一个强大的方法,平衡统计能力和错误控制.