Adjusting Incidence Estimates with Laboratory Test Performances: A Pragmatic Maximum Likelihood Estimation-Based Approach.

IF 4.7 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Epidemiology Pub Date : 2024-05-01 Epub Date: 2024-03-07 DOI:10.1097/EDE.0000000000001725
Yingjie Weng, Lu Tian, Derek Boothroyd, Justin Lee, Kenny Zhang, Di Lu, Christina P Lindan, Jenna Bollyky, Beatrice Huang, George W Rutherford, Yvonne Maldonado, Manisha Desai
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Abstract

Understanding the incidence of disease is often crucial for public policy decision-making, as observed during the COVID-19 pandemic. Estimating incidence is challenging, however, when the definition of incidence relies on tests that imperfectly measure disease, as in the case when assays with variable performance are used to detect the SARS-CoV-2 virus. To our knowledge, there are no pragmatic methods to address the bias introduced by the performance of labs in testing for the virus. In the setting of a longitudinal study, we developed a maximum likelihood estimation-based approach to estimate laboratory performance-adjusted incidence using the expectation-maximization algorithm. We constructed confidence intervals (CIs) using both bootstrapped-based and large-sample interval estimator approaches. We evaluated our methods through extensive simulation and applied them to a real-world study (TrackCOVID), where the primary goal was to determine the incidence of and risk factors for SARS-CoV-2 infection in the San Francisco Bay Area from July 2020 to March 2021. Our simulations demonstrated that our method converged rapidly with accurate estimates under a variety of scenarios. Bootstrapped-based CIs were comparable to the large-sample estimator CIs with a reasonable number of incident cases, shown via a simulation scenario based on the real TrackCOVID study. In more extreme simulated scenarios, the coverage of large-sample interval estimation outperformed the bootstrapped-based approach. Results from the application to the TrackCOVID study suggested that assuming perfect laboratory test performance can lead to an inaccurate inference of the incidence. Our flexible, pragmatic method can be extended to a variety of disease and study settings.

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用实验室检测结果调整发病率估计值:基于最大似然估计的实用方法。
了解疾病的发病率往往对公共政策决策至关重要,正如在 COVID-19 大流行期间所观察到的那样。然而,当发病率的定义依赖于对疾病进行不完全测量的检测时,对发病率的估算就具有挑战性,例如在检测 SARS-CoV-2 病毒时使用了性能不稳定的检测方法。据我们所知,目前还没有实用的方法来解决实验室检测病毒的性能所带来的偏差。在一项纵向研究中,我们开发了一种基于最大似然估计(MLE)的方法,利用期望最大化算法估计实验室性能调整后的发病率。我们使用基于引导的方法和大样本区间估计法构建了置信区间(CI)。我们通过大量模拟对我们的方法进行了评估,并将其应用于一项真实世界研究(TrackCOVID),该研究的主要目标是确定 2020 年 7 月至 2021 年 3 月期间旧金山湾区 SARS-CoV-2 感染的发病率和风险因素。模拟结果表明,在各种情况下,我们的方法都能迅速收敛,得出准确的估计值。基于 Bootstrapped 的置信区间(CIs)与大样本估计值的置信区间(CIs)相当,且有合理的发病病例数。在更极端的模拟场景中,大样本区间估计的覆盖率优于基于引导的方法。应用于 TrackCOVID 研究的结果表明,假设实验室测试性能完美会导致对发病率的推断不准确。我们灵活、实用的方法可扩展到各种疾病和研究环境中。
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来源期刊
Epidemiology
Epidemiology 医学-公共卫生、环境卫生与职业卫生
CiteScore
6.70
自引率
3.70%
发文量
177
审稿时长
6-12 weeks
期刊介绍: Epidemiology publishes original research from all fields of epidemiology. The journal also welcomes review articles and meta-analyses, novel hypotheses, descriptions and applications of new methods, and discussions of research theory or public health policy. We give special consideration to papers from developing countries.
期刊最新文献
Characterization of Additive Gene-environment Interactions For Colorectal Cancer Risk. Doubly Marginalized: The Interplay of Racism and Disability in Outcomes for Minoritized People With Down Syndrome. Geospatial Data Aggregation Methods for Novel Geographies: Validating Congressional District Life Expectancy Estimates. Interactive Effects of Long-term Exposure to Air Pollutants on SARS-CoV-2 Infection and Severity: A Northern Italian Population-based Cohort Study. Potential of a Second Screening Test for Alloimmunization in Pregnancies of Rhesus-positive Women: A Swedish Population-based Cohort Study.
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