PO.PS01.05 · 人群科学
在波多黎各HIV感染者中通过贝叶斯与频率学派方法估计口腔HPV流行率的差异
Differences in oral HPV prevalence estimation via Bayesian and frequentist methodologies from people living with HIV in Puerto Rico
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摘要 Abstract
中文摘要
引言：波多黎各因HPV相关癌症的高发病率和HIV的高流行率而面临重大公共卫生挑战，其AIDS病例数在美国各州和地区中始终名列前茅。虽然波多黎各口腔HPV感染的基于人群的数据仍然稀缺，但针对高危人群的研究发现，在San Juan都市区的药物使用者中流行率为12.5%。Logistic回归作为频率学派估计的常用方法，往往无法考虑筛查方法的准确性。研究证实，贝叶斯统计模型通过纳入检测效能克服了这些局限。尽管有这些优势，贝叶斯流行率估计的应用仍然很少。具体而言，它们在以下情形中被证明有用：筛查方法欠佳、样本量有限，或频率学派模型的核心假设无法满足。这些挑战在研究部位特异性HPV流行率时常见，使贝叶斯流行率估计成为特别合适且推荐的替代方案。目的：比较由频率学派模型和贝叶斯模型得出的口腔HPV流行率点估计。
方法：使用来自一项名为Multi-omics Predictors of Oral HPV Outcomes among People Living with HIV in Puerto Rico（P20GM148324）的正在进行研究的HPV基因分型数据来估计人群流行率。该数据的HPV状态使用SPF10-LiPA25方法确定。使用R统计软件进行流行率估计。R中的rjags库提供连接以在JAGS软件中运行Markov Chain Monte Carlo（MCMC）模拟，Rogan-Gladen（RG）模型将作为频率学派模型使用，因为它可校正诊断误分类。确定最佳估计的比较指标将包括点估计的误差分布以及置信区间长度，或对贝叶斯情形而言的可信区间长度。
结果：贝叶斯估计为0.121 ± 0.0261（CrI95%：0.073–0.176），而Rogan-Gladen模型为0.117 ± 0.0465（CI95%：0.032–0.212）。虽然两种模型的估计相似，但与RG估计相比，贝叶斯估计的标准误（SE）及相应区间均略有改善。
结论：如预期所示，贝叶斯方法在SE和区间估计方面均更优。应用RG估计的一个缺点是默认函数中缺乏区间估计；区间需手动计算。同样，MCMC的迭代次数会影响贝叶斯方法的SE，需进一步研究以确定是否应考虑设定一个截断点。
查看英文原文 English abstract
Introduction: Puerto Rico faces significant public health challenges due to a high rate of HPV-related cancers and a high prevalence of HIV, consistently ranking among the U.S. states and territories with the most AIDS cases. While population-based data on oral HPV infection for Puerto Rico remains scarce, studies focusing on high-risk groups have found a 12.5% prevalence rate among drug users in the San Juan metropolitan area. Logistic regression, a common method for frequentist estimation, often fails to account for the accuracy of a screening method. Studies confirm that Bayesian statistical models overcome these limitations by incorporating test performance. Despite these advantages, the use of Bayesian prevalence estimation is rare. Specifically, they have proven useful in scenarios where screening methods are suboptimal; limited sample size, or the core assumptions of frequentist models are unable to be met. These challenges are commonly encountered in studies addressing site-specific HPV prevalence, making Bayesian prevalence estimation a particularly well-suited and recommended alternative. Objective : To compare point estimates of oral HPV prevalence derived from frequentist and Bayesian models.
Methods: HPV genotyping data from an on-going study titled Multi-omics Predictors of Oral HPV Outcomes among People Living with HIV in Puerto Rico (P20GM148324) was used to estimate population prevalence. HPV status of this data was determined using the SPF10-LiPA25 method. R-statistical software and was used for prevalence estimation. The rjags library in R will provide the connection to run Markov Chain Monte Carlo (MCMC) simulations in JAGS software, Rogan-Gladen (RG) model will be utilized as the frequentist model since it compensates for diagnostic misclassifications. The comparison metrics to determine the best estimate will include error distribution for the point estimate and length of the confidence interval, or credible interval for the Bayesian case.
Results: The Bayesian estimation was 0.121 ± 0.0261 (CrI95%: 0.073 - 0.176) whilst the Rogan-Gladen model was 0.117 ± 0.0465 (CI95%: 0.032 - 0.212). While estimates are similar for both models, there is a slight improvement for both standard errors (SE) and corresponding intervals for the Bayesian estimation compared to the RG estimation.
Conclusion: The Bayesian method outperformed in both SE and Interval estimations as expected. A disadvantage of applying RG estimation is lack of interval estimation in the default function; intervals were manually calculated. Likewise, the number of iterations in MCMC impacts the SE for the Bayesian approach, further research must be done to determine if a cut-off point should be considered.
利益披露 Disclosure
E. M. Ivanovich-Méndez, None.