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関連する概念動画

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

391
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.
391
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

738
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
738
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

548
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
548
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

556
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,...
556
Actuarial Approach01:20

Actuarial Approach

284
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,...
284
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.0K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
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Updated: Jan 14, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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ShinyEvents：リアルワールド生存期間推定のための縦断データの調和

Alyssa Obermayer1,2, Joshua Davis3, Divya Priyanka Talada3

  • 1H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. Alyssa.Obermayer@Moffitt.org.

NPJ precision oncology
|January 12, 2026
PubMed
まとめ
この要約は機械生成です。

ShinyEventsは、患者の治療データを経時的に分析する新しいWebツールです。治療イベントと生存結果をリンクし、臨床研究とデータ分析を支援します。

キーワード:
縦断データ分析生存期間分析腫瘍学リアルワールドデータWebフレームワーク患者の旅治療ライン臨床研究データサイエンスバイオインフォマティクス

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Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
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関連する実験動画

Last Updated: Jan 14, 2026

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Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
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Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
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科学分野:

  • 腫瘍学
  • バイオインフォマティクス
  • データサイエンス

背景:

  • 縦断データ分析は、治療結果を理解するために重要です。
  • 既存のツールは、多層時系列データの統合と治療と生存期間のリンクに苦労しています。
  • このギャップは、患者の治療経過の包括的な分析を妨げます。

研究 の 目的:

  • 複雑な縦断データ分析のためのWebベースフレームワークであるShinyEventsを開発すること。
  • 多層時系列データを生存期間分析と統合できるようにすること。
  • 臨床医とデータサイエンティスト間の透明で再現可能なコラボレーションを促進すること。

主な方法:

  • 縦断データ分析のためのWebベースフレームワークであるShinyEventsを開発しました。
  • 臨床イベントとコホートの視覚化（サンキー、スイマーダイアグラム）のためのインタラクティブタイムラインを実装しました。
  • リアルワールド無増悪生存期間（rwPFS）と生存期間分析（カプランマイヤー、コックス回帰）の推論を可能にしました。

主要な成果:

  • ShinyEventsは、治療クラスタリングやエンドポイント割り当てを含むコホートレベルの分析を可能にします。
  • このツールは、患者のジャーニーと治療ラインを効果的に視覚化します。
  • 筋肉内浸潤性膀胱がん患者のケーススタディでは、シスプラチンとゲムシタビンがrwPFSと全生存期間を改善することが示されました。

結論:

  • ShinyEventsは、縦断的なリアルワールドデータと生存期間分析を統合するための統一されたフレームワークを提供します。
  • このツールは、治療ラインと臨床結果の関連付けをサポートします。
  • ShinyEventsは、腫瘍学とデータサイエンスにおける共同研究を強化します。