Systematic errors in estimates of $R_t$ from symptomatic cases in the presence of observation bias

BETS: The dangers of selection bias in early analyses of the coronavirus disease (COVID-19) pandemic

The coronavirus disease 2019 (COVID-19) has quickly grown from a regional outbreak in Wuhan, China to a global pandemic. Early estimates of the epidemic growth and incubation period of COVID-19 may have been biased due to sample selection. Using detailed case reports from 14 locations in and outside mainland China, we obtained 378 Wuhan-exported cases who left Wuhan before an abrupt travel quarantine. We developed a generative model we call BETS for four key epidemiological events---Beginning of exposure, End of exposure, time of Transmission, and time of Symptom onset (BETS)---and derived exp…

Understanding and adjusting the selection bias from a proof-of-concept study to a more confirmatory study

It has long been noticed that the efficacy observed in small early phase studies is generally better than that observed in later larger studies. Historically, the inflation of the efficacy results from early proof-of-concept studies is either ignored, or adjusted empirically using a frequentist or Bayesian approach. In this article, we systematically explained the underlying reason for the inflation of efficacy results in small early phase studies from the perspectives of measurement error models and selection bias. A systematic method was built to adjust the early phase study results from bot…

数据（不）平等在数据科学中的探讨：剖析脉搏血氧仪中系统性和系统性偏见

Data (in)equities in data science: Dissecting systemic and systematic biases in pulse oximetry

本文探讨数据公平性在数据科学中的应用，通过脉搏血氧仪中种族差异的测量误差案例，展示如何将数据公平性转化为可测试的数学形式，揭示数据公平性、预测公平性和决策公平性的不同需求。

Evidence of Systematic Bias in 2008 Presidential Polling (preliminary report)

Political polls achieve their results by sampling a small number of potential voters rather than the population as a whole. This leads to sampling error which most polling agencies dutifully report. But factors such as nonrepresentative samples, question wording and nonresponse can produce non-sampling errors. While pollsters are aware of such errors, they are difficult to quantify and seldom reported. When a polling agency, whether by intention or not, produces results with non-sampling errors that systematically favor one candidate over another, then that agency's poll is biased. We analyzed…

The effect of short-term exposure to the natural environment on depressive mood: A systematic review and meta-analysis

Research suggests that exposure to the natural environment can improve mood, however, current reviews are limited in scope and there is little understanding of moderators. We aimed to conduct a comprehensive systematic review and meta-analysis of the evidence for the effect of short-term exposure to the natural environment on depressive mood. Five databases were systematically searched for relevant studies published up to March 2018. Risk of bias was evaluated using the Cochrane Risk of Bias (ROB) tool 1.0 and the Risk of Bias in Non-Randomised Studies of Interventions (ROBINS-I) tool where ap…

一种用于测量和评估手机人口数据中偏差的系统性机器学习方法

A systematic machine learning approach to measure and assess biases in mobile phone population data

针对手机人口数据因数字鸿沟存在偏差、缺乏标准评估框架的问题，本研究提出结合覆盖指标与可解释机器学习的可复现框架，基于英国数据验证发现其覆盖度优于全国调查但仍存偏差，为相关数据的公平可靠使用提供工具。