関連する実験動画
Updated: Dec 21, 2025

04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
10.7K
COVID-19 パンデミックに関連した1年間の過剰死亡率を基礎的な状態と年齢に応じて推定する:人口ベースのコホート研究
Amitava Banerjee1, Laura Pasea2, Steve Harris3
1Institute of Health Informatics, University College London, London, UK; University College London Hospitals NHS Trust, London, UK; Barts Health NHS Trust, The Royal London Hospital, London, UK.
Lancet (London, England)
|May 15, 2020
まとめ
COVID-19 パンデミックは,特に基礎疾患を持つ人々の死亡率に大きな影響を及ぼします. この研究は 過剰な死亡率をモデル化し 持続的な公衆衛生の介入と 高いリスクのある個人を対象とした支援の必要性を強調しています
科学分野:
- 流行病学
- 公衆衛生
- 健康 経済
背景:
- COVID-19 パンデミックが人口の死亡率に及ぼす完全な影響は不明である.
- 既存の死亡モデルでは 既にある疾患や長期的な影響は考慮されていません
- 過剰な死亡率を理解するには 高いリスク要因と ベースラインの死亡率を考慮する必要があります
研究 の 目的:
- 各種シナリオで1年にわたるCOVID-19による過度の死亡数を推定する.
- リスク要因に基づく過剰死亡率を計算するためのモデルとオンラインツールを開発する.
- パンデミック制御と予防に関する政策決定を伝える.
主な方法:
- イギリスのリンクされた電子医療記録 (Health Data Research UK-CALIBER) を利用し,人口ベースのコホート研究を行った.
- 1997年から2017年の間に登録された30歳以上の380万人以上を含む.
- 異なる感染シナリオ (0. 001%から80%の感染率) で相対的なリスク (1. 5, 2. 0, 3. 0) を用いて推定された1年死亡率と過剰死亡率.
主要な成果:
- 研究集団の20%以上は,基礎疾患により高リスクと特定された.
- 推定される死亡率はシナリオによって大きく変化し,完全抑制の場合は2人から"何もしない"シナリオの場合は58万8千人近く (RR 3.0) であった.
- オンラインリスク計算機のプロトタイプが開発されました.
結論:
- この研究は,COVID-19の過剰な死亡率を理解するためのモデルとツールを提供します.
- 結果は,持続的で厳格な抑制措置の必要性を強調しています.
- パンデミックの影響を国内で評価するうえで 高いリスクのある個人を対象とした予防介入が不可欠です
関連する概念動画
Actuarial Approach
237
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
237
Assumptions of Survival Analysis
318
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
318
Bias in Epidemiological Studies
1.1K
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:
1.1K
Confounding in Epidemiological Studies
496
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
496
Kaplan-Meier Approach
484
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
484
Cancer Survival Analysis
580
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
580

