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JAMA Netw Open. 2023 Apr 3;6(4):e236498. doi: 10.1001/jamanetworkopen.2023.6498.
3
Quartile coefficient of variation is more robust than CV for traits calculated as a ratio.四分位变异系数比作为比值计算的性状的变异系数更稳健。
Sci Rep. 2023 Mar 22;13(1):4671. doi: 10.1038/s41598-023-31711-8.
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Biostatistics and Epidemiology for the Toxicologist: Measures of Central Tendency and Variability-Where Is the "Middle?" and What Is the "Spread?".毒理学家的生物统计学与流行病学:集中趋势与变异性的度量——“中间位置”在哪里?以及“离散程度”是多少?
J Med Toxicol. 2022 Jul;18(3):235-238. doi: 10.1007/s13181-022-00901-7. Epub 2022 May 31.
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Reveal, Don't Conceal: Transforming Data Visualization to Improve Transparency.揭示,而非隐藏:转变数据可视化以提升透明度。
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6
Biostatistics: a fundamental discipline at the core of modern health data science.生物统计学:现代健康数据科学核心的一门基础学科。
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Study validity.研究效度。
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心胸外科医生的描述性统计:第2部分 - 数据解读的基础。

Descriptive statistics for cardiothoracic surgeons: part 2 - the foundation of data interpretation.

作者信息

Ahmed H Shafeeq

机构信息

Bangalore Medical College and Research Institute, K.R Road, Bangalore, 560002 Karnataka India.

出版信息

Indian J Thorac Cardiovasc Surg. 2025 Jan;41(1):89-110. doi: 10.1007/s12055-024-01855-x. Epub 2024 Nov 8.

DOI:10.1007/s12055-024-01855-x
PMID:39679094
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11638441/
Abstract

Descriptive statistics are essential for summarizing and interpreting clinical data in cardiothoracic surgery. Understanding measures of central tendency and dispersion, such as mean, median, range, variance, and standard deviation, provides insights into patient outcomes and surgical effectiveness. Confidence intervals offer a range for population parameters, enhancing decision-making precision. Data visualization tools like histograms, box plots, and scatter plots illustrate distributions and relationships. Interpreting tables and figures accurately, recognizing biases, and evaluating statistical validity are crucial for applying research findings to clinical practice. These statistical tools ultimately support evidence-based practice and ensure informed decision-making by clinicians.

摘要

描述性统计对于总结和解释心胸外科的临床数据至关重要。理解集中趋势和离散程度的度量,如均值、中位数、范围、方差和标准差,有助于深入了解患者的治疗结果和手术效果。置信区间提供了总体参数的范围,提高了决策的精确性。诸如直方图、箱线图和散点图等数据可视化工具可以说明分布情况和关系。准确解读表格和图表、识别偏差以及评估统计有效性对于将研究结果应用于临床实践至关重要。这些统计工具最终支持循证实践,并确保临床医生做出明智的决策。