Dual Estimation of Prevalence and Disease Incidence in Pool-Testing Strategy

Pool-testing is a cost effective method over individual testing. When imperfect tests are used in pool testing strategy, there tend to be loss of sensitivity. The loss in sensitivity can be recovered by re-testing the pools classified initially. Apart from improving the sensitivity of the testing scheme, it also improves the efficiency of the estimator. This article proposes a pool-testing scheme with re-testing, and estimate the prevalence based on the proposed testing scheme.     

Attrition rate in infant fNIRS research: A meta-analysis

Understanding the trends and predictors of attrition rate, or the proportion of collected data that is excluded from the final analyses, is important for accurate research planning, assessing data integrity, and ensuring generalizability. In this pre-registered meta-analysis, we reviewed 182 publications in infant (0–24 months) functional near-infrared spectroscopy (fNIRS) research published from 1998 to April 9, 2020, and investigated the trends and predictors of attrition. The average attrition rate was 34.23% among 272 experiments across all 182 publications. Among a subset of 136 experiments that reported the specific reasons for subject exclusion, 21.50% of the attrition was infant-driven, while 14.21% was signal-driven. Subject characteristics (e.g., age) and study design (e.g., fNIRS cap configuration, block/trial design, and stimulus type) predicted the total and subject-driven attrition rates, suggesting that modifying the recruitment pool or the study design can meaningfully reduce the attrition rate in infant fNIRS research. Based on the findings, we established guidelines for reporting the attrition rate for scientific transparency and made recommendations to minimize the attrition rates. This research can facilitate developmental cognitive neuroscientists in their quest toward increasingly rigorous and representative research.