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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

360
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
360
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

491
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
491
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

161
Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
161
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

121
Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
121
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

157
Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
157
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

149
The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A...
149

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

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same author

Early mechanisms of diabetes development in pediatric pancreatitis: A pilot study.

Journal of pediatric gastroenterology and nutrition·2025
Same author

Cost-effectiveness of spinal manipulation, exercise, and self-management for spinal pain.

Chiropractic & manual therapies·2025
Same author

The conclusiveness of trial sequential analysis varies with estimation of between-study variance: a case study.

BMC medical research methodology·2025
Same author

Evaluation of Sleep Health in Children With Congenital Adrenal Hyperplasia Due to 21-Hydroxylase Deficiency.

The Journal of clinical endocrinology and metabolism·2024
Same author

Causally interpretable meta-analysis combining aggregate and individual participant data.

American journal of epidemiology·2024

関連する実験動画

Updated: May 5, 2026

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.3K

ロードのパラドックスと2つのネットワークメタアナリシスモデル

Yu-Kang Tu1,2, James S Hodges1,3

  • 1Institute of Health Data Analytics & Statistics, College of Public Health, https://ror.org/05bqach95National Taiwan University, Taipei, Taiwan.

Research synthesis methods
|February 2, 2026
PubMed
まとめ
この要約は機械生成です。

ネットワークメタアナリシス（NMA）における対比ベースモデル（CBM）とベースラインモデル（BM）は、ベースライン効果の処理方法が異なります。CBMとBM間の結果の違いは、推移律の仮定の問題を示す可能性があります。

キーワード:
ロードのパラドックスベースラインモデル対比ベースモデル直接非巡回グラフネットワークメタアナリシス

さらに関連する動画

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

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.1K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.7K

関連する実験動画

Last Updated: May 5, 2026

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.3K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.1K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.7K

科学分野:

  • 生物統計学
  • 医学研究方法論

背景:

  • ネットワークメタアナリシス（NMA）では、対比ベースモデル（CBM）が一般的に採用されています。
  • ベースラインモデル（BM）のような代替方法は、あまり利用されていません。
  • CBMとBMの違いを理解することは、NMAの正確な解釈にとって重要です。

研究 の 目的:

  • NMAにおけるCBMとBMの仮定と適用の違いを解明すること。
  • CBMとBMが異なる結果をもたらす条件を特定すること。
  • ロードのパラドックスの類推を用いてこれらの違いの意味を探求すること。

主な方法:

  • CBMとBMの仮定を比較するための代数的および図解的分析。
  • NMAモデルとロードのパラドックス（t検定対ANCOVA）との類似性を描くこと。
  • ベースライン効果モデリングがNMAの結果に与える影響を調査すること。

主要な成果:

  • CBMはベースライン転帰レベルを固定効果として扱い、交換可能な治療効果の差を仮定します。
  • BMはベースライン転帰レベルをランダム効果として扱い、交換可能なベースライン転帰を仮定します。
  • CBMとBMの乖離は、ロードのパラドックスにおけるt検定（観察された変化）対ANCOVA（調整された変化）に類似しています。

結論:

  • CBMとBMの選択は、ベースライン効果と治療効果の差に関する仮定に依存します。
  • CBMとBMの結果の間に実質的な乖 discrepancy がある場合、NMAにおける推移律の仮定の違反を示す可能性があります。
  • 特にモデルが著しく異なる結果をもたらす場合、NMAの結果の解釈には注意が必要です。