Addressing selection bias in the UK Biobank neurological imaging cohort

The UK Biobank is a national prospective study of half a million participants between the ages of 40 and 69 at the time of recruitment between 2006 and 2010, established to facilitate research on diseases of aging. The imaging cohort is a subset of UK Biobank participants who have agreed to undergo extensive additional imaging assessments. However, Fry et al (2017) find evidence of "healthy volunteer bias" in the UK Biobank -- participants are less likely to smoke, be obese, consume alcohol daily than the target population of UK adults. Here we examine selection bias in the UK Biobank imaging cohort. We address two common misconceptions: first, that study size can compensate for bias in data collection, and second that selection bias does not affect estimates of associations, which are the primary interest of the UK Biobank. We introduce inverse probability weighting (IPW) as an approach commonly used in survey research that can be used to address selection bias in volunteer health studies like the UK Biobank. We discuss 6 such methods -- five existing and one novel --, assess relative performance in simulation studies, and apply them to the UK Biobank imaging cohort. We find that our novel method, BART for predicting the probability of selection combined with raking, performs well relative to existing methods, and helps alleviate selection bias in the UK Biobank imaging cohort.

A Regression Discontinuity Design Framework for Controlling Selection Bias in Evaluations of Differential Item Functioning

Differential item functioning (DIF) is often used to examine validity evidence of alternate form test accommodations. Unfortunately, traditional approaches for evaluating DIF are prone to selection bias. This article proposes a novel DIF framework that capitalizes on regression discontinuity design analysis to control for selection bias. A simulation study was performed to compare the new framework with traditional logistic regression, with respect to Type I error and power rates of the uniform DIF test statistics and bias and root mean square error of the corresponding effect size estimators. The new framework better controlled the Type I error rate and demonstrated minimal bias but suffered from low power and lack of precision. Implications for practice are discussed.

Wearable Devices and Intelligent Application in Women’s Postpartum Repair for Smart Healthcare

Wearable devices are more and more widely used in the field of smart healthcare. The purpose of this study was to explore the effect of contraceptive counseling and education on contraceptive behavior of women after induced abortion. The investigators will explain the situation of this topic to the respondents and select the respondents in strict accordance with the framework requirements of sampling design. All the data are from the induced abortion women in the first-, second-, and third-level hospitals, which reduces the selection bias of the respondents. It is found that the proportion of induced abortion among college students is the highest, reaching 66.03%. This study is helpful to reduce the incidence of unwanted pregnancy, induced abortion, and repeated abortion and improve the reproductive health of women.

Revisiting the existence of the global warming slowdown during the early 21st century

There are heated debates on the existence of the global warming slowdown during the early 21st century. Although efforts have been made to clarify or reconcile the controversy over the issue, it is not explicitly addressed, restricting the understanding of global temperature change particularly under the background of increasing greenhouse-gas concentrations. Here, using extensive temperature datasets, we comprehensively reexamine the existence of the slowdown under all existing definitions during all decadal-scale periods spanning 1990-2017. Results show that the short-term linear-trend dependent definitions of slowdown make its identification severely suffer from the period selection bias, which largely explains the controversy over its existence. Also, the controversy is further aggravated by the significant impacts of the differences between various datasets on the recent temperature trend and the different baselines for measuring slowdown prescribed by various definitions. However, when the focus is shifted from specific periods to the probability of slowdown events, we find the probability is significantly higher in the 2000s than in the 1990s, regardless of which definition and dataset are adopted. This supports a slowdown during the early 21st century relative to the warming surge in the late 20th century, despite higher greenhouse-gas concentrations. Furthermore, we demonstrate that this decadal-scale slowdown is not incompatible with the centennial-scale anthropogenic warming trend, which has been accelerating since 1850 and never pauses or slows. This work partly reconciles the controversy over the existence of the warming slowdown and the discrepancy between the slowdown and anthropogenic warming.