covariates

简明释义

英[kəʊˈveəriəts]美[koʊˈvɛriˌeɪts]

协变量

英英释义

单词用法

同义词

反义词

例句

1.It is possible that these links are not causal, but rather covariates of the common factor of family.

这是可能的，这些链接不因果关系，而是家庭共同因素协变量。

2.These covariates included family characteristics and previous problems with thinking, learning and memory.

这些影响因素包括家庭情况和先前思维、学习记忆能力问题。

3.It is so called "time-dependent covariates" that the values of covariates change over time.

共变数的值会随著时间而改变时，我们称之为时间相依之共变数。

4.Using an intention-to-treat analysis controlling for covariates, data from the 2 prevention groups were analyzed separately.

以意向分析的方式控制共变项，来自这两个预防组的数据分开分析。

5.This association persisted after adjustment for APACHE II, Multiple Organ Failure score, or the combined covariates cirrhosis, sepsis, oliguria, and mechanical ventilation.

在通过APACHEII，多器官衰竭评分或与硬化，脓毒血症，少尿和机械通气协同变异校正后，这种关联性仍持续存在。

6.Multivariable linear regression models were then used to assess the joint contributions of the top SNP associations and interactions to ABI after adjustment for covariates.

多变量线性回归模型，然后用来评估联合的贡献顶端的SNP协会和互动，以abi的调整后变。

7.The regression model was adjusted for covariates 协变量 like education level and marital status.

回归模型调整了教育水平和婚姻状况等covariates 协变量。

8.When analyzing the data, it is important to account for covariates 协变量 that might influence the outcome.

在分析数据时，考虑可能影响结果的covariates 协变量是很重要的。

9.In our study, we included several covariates 协变量 such as age and income to control for their effects.

在我们的研究中，我们纳入了几个covariates 协变量，例如年龄和收入，以控制它们的影响。

10.We used multiple covariates 协变量 to improve the accuracy of our predictive model.

我们使用了多个covariates 协变量来提高预测模型的准确性。

11.The researchers found that certain covariates 协变量 significantly affected the treatment results.

研究人员发现某些covariates 协变量显著影响了治疗结果。

作文

In the field of statistics and research methodology, the term covariates refers to variables that are possibly predictive of the outcome under study. Understanding covariates is crucial for researchers as they design their studies and interpret their results. By incorporating covariates into their analyses, researchers can control for potential confounding factors that may distort the relationship between the independent and dependent variables. For instance, in a study examining the impact of a new teaching method on student performance, covariates such as age, previous academic achievement, and socio-economic status might be included to ensure that the effects observed are truly attributable to the teaching method itself rather than other influences.The importance of covariates cannot be overstated. They allow researchers to refine their analyses and make more accurate predictions. In clinical trials, for example, adjusting for covariates like baseline health status and demographic factors can lead to a clearer understanding of how a treatment works across different populations. Without considering covariates, the findings of a study could be misleading, leading to incorrect conclusions that may affect policy decisions or clinical practices.Moreover, the choice of which covariates to include in a study can significantly influence the results. Researchers must carefully consider the theoretical framework of their study and the existing literature to identify relevant covariates. This process often involves statistical techniques such as regression analysis, where covariates are used to adjust the model and isolate the effect of the main independent variable.In addition to improving the validity of research findings, the inclusion of covariates also enhances the generalizability of the results. When studies account for various covariates, the conclusions drawn can be more reliably applied to broader populations. This is particularly important in social sciences, where individual differences can significantly impact outcomes. For instance, when studying the effects of a public health intervention, controlling for covariates like age, gender, and pre-existing conditions helps ensure that the findings are applicable to diverse groups.However, it is essential to approach the use of covariates with caution. Including too many covariates can lead to overfitting, where the model becomes overly complex and captures noise rather than the true signal. Researchers must strike a balance between including enough covariates to account for confounding factors while avoiding unnecessary complexity in their models.In conclusion, covariates play a vital role in the design and analysis of research studies. They help researchers control for confounding variables, improve the accuracy of their findings, and enhance the generalizability of their results. By thoughtfully selecting and incorporating covariates, researchers can contribute to more reliable and impactful scientific knowledge. As the field of research continues to evolve, the understanding and application of covariates will remain a fundamental aspect of rigorous study design and analysis.

在统计学和研究方法论的领域中，术语协变量指的是可能对所研究结果具有预测能力的变量。理解协变量对研究人员至关重要，因为他们在设计研究和解释结果时需要考虑这一点。通过将协变量纳入分析中，研究人员可以控制可能扭曲自变量和因变量之间关系的混杂因素。例如，在一项研究中，检验新教学方法对学生表现的影响时，可能会加入年龄、之前的学业成就和社会经济地位等协变量，以确保观察到的效果确实是归因于教学方法本身，而不是其他影响因素。协变量的重要性不容小觑。它们使研究人员能够细化分析并做出更准确的预测。在临床试验中，例如，调整基线健康状态和人口统计因素等协变量可以使我们更清楚地了解一种治疗在不同人群中的作用。如果不考虑协变量，研究的发现可能会产生误导，导致错误的结论，从而影响政策决策或临床实践。此外，选择在研究中包含哪些协变量可能会显著影响结果。研究人员必须仔细考虑其研究的理论框架和现有文献，以识别相关的协变量。这个过程通常涉及统计技术，例如回归分析，其中使用协变量来调整模型并孤立主要自变量的影响。除了提高研究发现的有效性之外，纳入协变量还增强了结果的普遍适用性。当研究考虑到各种协变量时，得出的结论可以更可靠地应用于更广泛的人群。这在社会科学中尤为重要，因为个体差异可能会显著影响结果。例如，在研究公共卫生干预措施的影响时，控制年龄、性别和既往病史等协变量有助于确保研究结果适用于不同群体。然而，在使用协变量时必须谨慎。包含过多的协变量可能导致过拟合，即模型变得过于复杂，捕捉噪音而不是真实信号。研究人员必须在包含足够的协变量以考虑混杂因素与避免模型不必要复杂性之间找到平衡。总之，协变量在研究设计和分析中发挥着至关重要的作用。它们帮助研究人员控制混杂变量，提高研究发现的准确性，并增强结果的普遍适用性。通过深思熟虑地选择和纳入协变量，研究人员可以为更可靠和有影响力的科学知识做出贡献。随着研究领域的不断发展，对协变量的理解和应用将始终是严谨研究设计和分析的基本方面。