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関連する概念動画

Sample Size Calculation01:19

Sample Size Calculation

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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...
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Data Collection by Experiments01:13

Data Collection by Experiments

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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
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Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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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...
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
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One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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トリートメント効果スケールで借りる前の有効サンプルサイズ

Hongtao Zhang1, Keaven M Anderson1, Zachary Zimmer1

  • 1Biostatistics and Research Decision Sciences, Merck & Co. Inc., North Wales, PA, USA.

Statistics in medicine
|August 22, 2025
PubMed
まとめ

バイアスの外部データの借り入れには,事前の有効なサンプルサイズ (ESS) が重要です. この研究は,期待される局所情報比 (ELIR) ESSの定義を治療効果スケールに拡張し,改善された試験設計のための重要な方法論的ギャップに対処します.

キーワード:
ベイジアン法外部データ小児のエキストラポレーション以前の配給

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科学分野:

  • バイオ統計学
  • 臨床試験
  • ベイズ推論

背景:

  • ベイジアンの外部データ借入は,臨床試験でますます使用されています.
  • 精確な事前の有効なサンプルサイズ (ESS) は,借りた情報を制御するために非常に重要です.
  • 既存のESS方法は主に治療効果のスケールではなく,借入制御に焦点を当てています.

研究 の 目的:

  • 予想される局所情報比 (ELIR) ESSの定義を処理効果スケールに拡張する.
  • 一般的な枠組みを提供し,様々なエンドポイントと治療効果の測定値のためのESSを導き出す.
  • 提案されたELIR ESSの予測一致性特性を評価する.

主な方法:

  • 予想されるローカル情報比率 (ELIR) ESSの定義の拡張
  • 様々なエンドポイントタイプと治療効果の測定のためのESSの導出.
  • 異なるエンドポイントと治療効果の組み合わせに対する予測的一貫性の評価

主要な成果:

  • ELIR ESSの定義は,治療効果スケールに成功しました.
  • 以前のESSの公式は,複数のエンドポイントと治療効果タイプのために導出されました.
  • 予測的な一貫性は,2つの正常なエンドポイントの違いのみに保たれた.

結論:

  • 開発された方法は,治療効果スケール上の以前のESSの計算におけるギャップを埋めます.
  • この結果は,ELIR ESSを適用する際のエンドポイントと治療効果のタイプを考慮することの重要性を強調しています.
  • Rの実装は,これらの新しいESSの方法の実践的適用を容易にするために利用できます.