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関連する概念動画

Causality in Epidemiology01:21

Causality in Epidemiology

822
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...
822
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

155
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
155
What are Estimates?01:06

What are Estimates?

5.4K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
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Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

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The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
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Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

635
The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
635
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

208
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
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関連する実験動画

Updated: Sep 10, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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推定枠組みと因果推論:相互補完的なパラダイム

Thomas Drury1, Jonathan W Bartlett2, David Wright3

  • 1GSK, London, UK.

Pharmaceutical statistics
|August 23, 2025
PubMed
まとめ

ICH E9 (R1) 評価枠組みと因果推論は,臨床試験における治療効果の定義のための補完的なアプローチを提供します. 両方を理解することで 試験の設計,分析,解釈の明確性が向上します

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Enactive Phenomenological Approach to the Trier Social Stress Test: A Mixed Methods Point of View
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Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal
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Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal

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関連する実験動画

Last Updated: Sep 10, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal
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科学分野:

  • バイオ統計学
  • 臨床試験の設計
  • 流行病学

背景:

  • E9 (R1) ガイドラインは,臨床試験における治療効果の正確な仕様に関する評価の枠組みを導入した.
  • ICH E9 (R1) 推定値の枠組みと因果推論の関係は,両方の推定値の定義にもかかわらず,不明のままである.

研究 の 目的:

  • ICH E9 (R1) 評価の枠組みと因果推論を比較して対比する.
  • 2つのフレームワークが人口ベースの治療効果をどのように定義できるかを説明します.
  • 臨床試験の方法論におけるこの2つのパラダイムの互補性を強調する.

主な方法:

  • ICH E9 (R1) 評価の枠組みと因果推論を比較するために,例示的な例を用いた.
  • 推定値の定義における類似点と違いを分析した.
  • 各フレームワークのアクセシビリティと数学的精度は議論されました.

主要な成果:

  • ICH E9 (R1) と因果推論の両方が,集団ベースの治療効果を定義することができます.
  • ICH E9 (R1) フレームワークは,コミュニケーションのための構造化され,アクセシブルなアプローチを提供します.
  • 原因推論は,因果グラフのようなツールを介して数学的な精度と明示的な仮定の表現を提供します.

結論:

  • ICH E9 (R1) 評価の枠組みと因果推論は互いを補完し,競合するものではありません.
  • 両方のアプローチを統合することで 臨床試験のコミュニケーションの明確さと信頼性が向上します
  • 両方のフレームワークの概念を評価することで 臨床試験の設計,分析,解釈が強化されます.