From 25 to 29 April 2020, the state of Indiana undertook testing of 3,658 randomly chosen state residents for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, the agent causing COVID-19 disease. This was the first statewide randomized study of COVID-19 testing in the United States. Both PCR and serological tests were administered to all study participants. This paper describes statistical methods used to address nonresponse among various demographic groups and to adjust for testing errors to reduce bias in the estimates of the overall disease prevalence in Indiana. These adjustments were implemented through Bayesian methods, which incorporated all available information on disease prevalence and test performance, along with external data obtained from census of the Indiana statewide population. Both adjustments appeared to have significant impact on the unadjusted estimates, mainly due to upweighting data in study participants of non-White races and Hispanic ethnicity and anticipated false-positive and false-negative test results among both the PCR and antibody tests utilized in the study.

2020 年 4 月 25 日至 29 日，印第安纳州对 3,658 名随机选择的州居民进行了新型严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 病毒检测，该病毒是导致 COVID-19 疾病的病原体。这是美国第一项针对 COVID-19 检测的全州随机研究。对所有研究参与者进行了 PCR 和血清学检测。本文描述了用于解决不同人口群体之间的无反应问题并调整测试误差以减少印第安纳州总体疾病流行率估计中的偏差的统计方法。这些调整是通过贝叶斯方法实施的，该方法纳入了有关疾病患病率和测试表现的所有可用信息，以及从印第安纳州全州人口普查中获得的外部数据。这两项调整似乎对未调整的估计值产生了重大影响，主要是由于非白人种族和西班牙裔研究参与者的数据权重增加，以及研究中使用的 PCR 和抗体测试中预期的假阳性和假阴性测试结果。