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相关概念视频

Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

133
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,...
133
What are Estimates?01:06

What are Estimates?

5.0K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
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Retrovirus Life Cycles01:10

Retrovirus Life Cycles

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Retroviruses have a single-stranded RNA genome that undergoes a special form of replication. Once the retrovirus has entered the host cell, an enzyme called reverse transcriptase synthesizes double-stranded DNA from the retroviral RNA genome. This DNA copy of the genome is then integrated into the host’s genome inside the nucleus via an enzyme called integrase. Consequently, the retroviral genome is transcribed into RNA whenever the host’s genome is transcribed, allowing the...
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相关实验视频

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An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings
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使用无响应的基于人口的调查估计艾滋病毒: 一个部分识别方法.

Oyelola A Adegboye1, Tomoki Fujii2, Denis Heng-Yan Leung2

  • 1Menzies School of Health Research, Charles Darwin University, Casuarina, Australia.

Statistics in medicine
|May 17, 2024
PubMed
概括

从调查数据中估计艾滋病毒的患病率是具有挑战性的,因为没有回应. 本研究引入了使用仪器变量强大的部分识别方法,提供了更具信息性和可靠的HIV估计,没有严格的假设.

关键词:
艾滋病病毒 艾滋病病毒 艾滋病病毒人口和健康调查调查.这是一个仪器变量.没有回复的情况.部分识别部分识别

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Amplifying and Quantifying HIV-1 RNA in HIV Infected Individuals with Viral Loads Below the Limit of Detection by Standard Clinical Assays
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科学领域:

  • 流行病学 流行病学
  • 生物统计学 生物统计学
  • 公共卫生 公共卫生

背景情况:

  • 使用人口和健康调查 (DHS) 数据估计艾滋病毒患病率,因没有反应和拒绝测试而复杂化.
  • 像归算这样的标准调整方法假设数据是随机丢失的,但通常情况并非如此.
  • 仪表变量方法可以解释非随机缺失,但严重依赖仪器有效性.

研究的目的:

  • 开发和评估一种可靠的艾滋病毒流行率估计方法,以解决调查数据中的非响应和检测拒绝问题.
  • 提高基于人口调查的艾滋病毒流行率估计的信息性和可信度.
  • 将拟议方法的性能与传统的归算和最坏情况的边界进行比较.

主要方法:

  • 利用曼斯基的部分识别方法来构建HIV流行率的仪器变量边界.
  • 使用了一组候选仪器,不要求所有仪器都是有效的.
  • 通过模拟研究评估了该方法,并将其应用于赞比亚,马拉维和肯尼亚的DHS数据.

主要成果:

  • 推算方法在轻微的非随机失踪的情况下产生了明显偏差的艾滋病毒流行率估计.
  • 最坏情况的识别界限是强大的,但缺乏信息性.
  • 拟议的工具变量界限联盟平衡了信息性和稳定性,即使有一些无效的工具.

结论:

  • 部分识别边界为艾滋病毒流行率估计提供了一种可信和可靠的方法,没有严格的关于非响应机制的假设.
  • 工具变量边界的结合提供了比最坏情况下的边界更有信息的替代方案,同时保持了稳定性.
  • 这种方法对于使用受非响应影响的基于人口的调查数据进行准确的HIV监测至关重要.