JoVE - Journal of Visualized Experiments
JoVE Visualize
Contact Us
このページは機械翻訳されています。他のページは英語で表示される場合があります。 View in English

低ナトリウム血症における死亡率の予測:ホルト・ウィンターズモデルを用いた統計的アプローチ

  • 0Department of Mathematics and Statistics, College of Science in Yanbu, Taibah University, Madinah, Saudi Arabia.
The International Journal of Biostatistics +

|

2025年9月2日

PubMedで要約を見る

まとめ

この要約は機械生成です。

関連する概念動画

Parametric Survival Analysis: Weibull and Exponential Methods 01:14

600

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...

Mechanistic Models: Compartment Models in Individual and Population Analysis 01:23

85

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...

Actuarial Approach 01:20

132

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,...

Introduction To Survival Analysis 01:18

394

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...

Assumptions of Survival Analysis 01:15

196

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.

Survival Times Are Positively Skewed
 Survival times often exhibit positive skewness, unlike the normal distribution assumed...

Kaplan-Meier Approach 01:24

258

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,...