Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Bias01:22

Bias

7.2K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
7.2K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

6.1K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
6.1K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.2K
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:  
1.2K
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

5.5K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
5.5K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

545
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%...
545
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.0K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.0K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Episodic memory trajectories of older adults with and without HIV: A longitudinal population-based study in rural South Africa.

PLOS global public health·2026
Same author

How should covariates be handled in randomized trials? Empirical evidence from 50 trials and recommendations for practice.

Journal of clinical epidemiology·2026
Same author

Doubly Robust Estimators of the Restricted Mean Time in Favor Estimands in Individual- and Cluster-Randomized Trials.

Statistics in medicine·2026
Same author

Test-Negative Designs With Multiple Testing Sources.

Statistics in medicine·2026
Same author

EFFICIENT AND MULTIPLY ROBUST RISK ESTIMATION UNDER GENERAL FORMS OF DATASET SHIFT.

Annals of statistics·2026
Same author

Unpacking sources of transmission in HIV prevention trials with deep-sequence pathogen data.

Nature communications·2026

関連する実験動画

Updated: Jan 7, 2026

Measuring Attentional Biases for Threat in Children and Adults
08:25

Measuring Attentional Biases for Threat in Children and Adults

Published on: October 19, 2014

15.8K

様々な検査理由を持つテスト陰性デザイン：統計的バイアスと解決策

Mengxin Yu1, Tom Hongyi Liu2, Kendrick Qijun Li3

  • 1The Statistics and Data Science Department of the Wharton School, University of Pennsylvania.

Epidemiology (Cambridge, Mass.)
|December 31, 2025
PubMed
まとめ

ワクチンの有効性を評価する修正されたテスト陰性デザインは、バイアスを生じる可能性があります。本研究では、様々な検査理由を考慮に入れるための層化推定量を導入し、市場投入後のワクチン評価における精度を向上させ、バイアスを低減します。

キーワード:
COVID-19精度層化ワクチン有効性

さらに関連する動画

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing
06:58

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing

Published on: January 24, 2020

7.7K
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

4.3K

関連する実験動画

Last Updated: Jan 7, 2026

Measuring Attentional Biases for Threat in Children and Adults
08:25

Measuring Attentional Biases for Threat in Children and Adults

Published on: October 19, 2014

15.8K
Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing
06:58

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing

Published on: January 24, 2020

7.7K
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

4.3K

科学分野:

  • 疫学
  • 生物統計学
  • ワクチン学

背景:

  • ランダム化試験が実行不可能な場合、市場投入後のワクチン有効性評価にはテスト陰性デザインが不可欠です。
  • 最近の適応には多様な検査理由を持つ個人が含まれており、バイアスを導入する可能性があります。
  • これらの修正されたデザインには、正式な統計的検討が必要です。

研究 の 目的:

  • 修正されたテスト陰性デザインにおける潜在的なバイアスを統計的に検証すること。
  • ワクチン有効性の偏りのない推定のための方法を開発すること。
  • 複数の検査理由を組み込むことによって精度を向上させること。

主な方法:

  • バイアスを分析するために、統計的導出と因果グラフが使用されました。
  • 検査理由は、症状、必須スクリーニング、接触者追跡に分類されました。
  • 一貫した推定とバイアス排除のために層化が採用されました。
  • 新しい層化推定量が提案され、評価されました。

主要な成果:

  • 標準的なオッズ比推定量は、多様な検査理由を考慮しない場合、バイアスを生じる可能性があります。
  • 層化はバイアスを効果的に排除し、ワクチン有効性の一貫した推定を可能にします。
  • 提案された層化推定量は、複数の検査理由を組み込むことで精度を向上させることができます。

結論:

  • 修正されたテスト陰性デザインでは、検査理由の統計的考慮が慎重に必要です。
  • 提案された層化方法は、ワクチン有効性推定のための堅牢なアプローチを提供します。
  • この研究は、市場投入後のワクチン安全性と有効性サーベイランスの信頼性を高めます。