The analytical methods provided on this site all fall into one of two broad categories of statistical methods: frequentist or Bayesian.
Frequentist methods use conventional statistical techniques to calculate maximum-likelihood estimates of true prevalence and confidence limits, in a similar manner to standard techniques used to analyse conventional survey data. Frequentist methods are conceptually simpler to understand and are usually computationally easier to implement (and take less computer time to run). They do not take account of any existing knowledge of the likely prevalence, although some methods do allow for adjustment of estimates for imperfect sensitivity and specificity of the tests used.
On the other hand, Bayesian analysis uses simulation (using a Gibbs sampler) to derive a posterior probability distribution(s) for the parameter(s) of interest - usually true prevalence but distributions for sensitivity, specificity and other parameters are also generated.
贝叶斯方法也具有以下优点:
Briefly, a Bayesian approach allows the combination of any prior information available on test sensitivity and specificity and estimated prevalence of disease with the results of testing, to produce a posterior probability distribution of the estimated true prevalence (and other measures such as test sensitivity and specificity) that best fits the combination of prior distributions and observed testing results. Bayesian methods were initially developed for estimating prevalence from individual testing and were subsequently extended for use with pooled testing strategies.
However, Bayesian estimates can be seriously affected by the use of inappropriate prior distributions (inaccurate estimates and/or overconfidence in the values) for prevalence, sensitivity or specificity and therefore must be used with care. Wherever possible prior estimates should be based on real data and should be appropriately weighted (wide probability limits) to ensure that any errors do not dominate the data, causing inaccurate results. See the Glossary for more details on Bayesian methods and the Beta distribution and its parameters.
Bayesian also rely on simulation rather than analytical methods, and therefore can take some time to run, depending on the number of iterations used. It is also that sufficient iterations are run to allow convergence of the Bayesian model and to support inference from the results.
Outputs from the Gibbs sampler are revised estimates of prevalence, test sensitivity, test specificity and other parameters as posterior probability distributions.
内容 | |
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1 | 介绍 |
2 | 概观 |
3 | 贝叶斯与Frequentist方法 |
4 | 固定泳池大小和完美测试 |
5 | 固定的游泳池大小和已知的Se&Sp |
6 | 固定的游泳池大小和不确定的Se&Sp |
7 | 可变池大小和完美测试 |
8 | 使用Gibbs采样器汇集流行率 |
9 | 使用一次测试确实流行 |
10 | 使用Gibbs采样器进行两次测试估计真实患病率 |
11 | 估计先前Beta分布的参数 |
12 | 样本大小,完美的游泳池大小和完美的测试 |
13 | 固定池的样本大小和已知的测试灵敏度和特异性 |
14 | 固定池大小的样本大小和不确定的测试灵敏度和特异性 |
15 | 模拟固定池大小的采样 |
16 | 模拟可变池大小的采样 |
17 | 重要假设 |
18 | 汇总流行率估计有偏见! |