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相关概念视频

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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Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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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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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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...
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Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Updated: Jul 23, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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在开发和选择基于线性回归的映射算法时,使用样本大小计算框架用于临床预测模型.

Yasuhiro Hagiwara1

  • 1Department of Biostatistics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Medical decision making : an international journal of the Society for Medical Decision Making
|July 20, 2023
PubMed
概括
此摘要是机器生成的。

计算适当的样本大小对于开发可靠的与健康相关的生活质量 (HRQOL) 映射算法至关重要. 这项研究提出了一个框架,发现十项评估的映射研究中有四项缺乏足够的样本大小以进行准确的预测.

关键词:
医疗保健公用事业公司线性回归是一种线性回归.绘制地图,绘制地图.基于偏好措施的措施.样本的大小 样本大小

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科学领域:

  • 卫生经济学 卫生经济学
  • 生物统计学 生物统计学
  • 临床预测建模临床预测建模

背景情况:

  • 映射算法将与健康相关的生活质量 (HRQOL) 措施转化为基于偏好的措施 (PBMs) 用于经济评估.
  • 对于在开发这些映射算法时确定必要的样本大小,还没有确定的指导方针.
  • 线性回归是一种常见的方法,用于在不同患者报告的结果仪器之间创建这些交叉路口.

研究的目的:

  • 提出一个框架来计算开发和选择映射算法所需的样本大小.
  • 用线性回归来评估现有映射研究中样本大小的充分性.
  • 为卫生经济学研究人员提供有关绘制研究中样本大小考虑的指导.

主要方法:

  • 基于临床预测模型的标准,开发了一个样本大小计算框架.
  • 使用了四个具体标准:全球收缩系数 (≥0.9),调整后的R平方差 (≤0.05),剩余标准偏差误差 (≤1.1),模型截取误差 (≤0.025).
  • 根据这些标准评估了10项使用线性回归的已发表的映射研究.

主要成果:

  • 从十项映射研究中成功地提取了样本大小计算所需的信息.
  • 在十项评估的映射研究中,有四项不符合拟议的样本大小标准.
  • 这表明当前绘图研究中所需的样本大小可能被低估了.

结论:

  • 样本大小是一个关键因素,必须在开发和选择映射算法时考虑.
  • 拟议的框架提供了一种方法来评估现有和未来的绘图研究中样本大小的充分性.
  • 进一步的研究应该将这个框架扩展到用于映射的其他回归技术.