Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models.

Purpose: The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results. We performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) with genetic data to understand which decisions may affect performance.

Large-scale longitudinal analysis of the progression of alcohol use among members of a social media platform: an observational study.

The large-scale identification of people at risk of transitioning from relatively lower-risk to higher-risk alcohol use (e.g. problem drinking) remains a public health challenge despite advances in the identification of risk and protective factors.

Pre-pandemic activity on a myalgic encephalomyelitis/chronic fatigue syndrome support forum is highly associated with later activity on a long COVID support forum for a variety of reasons: A mixed methods study.

Encephalomyelitis/chronic fatigue syndrome (ME/CFS) and long COVID share some clinical and social characteristics. We predicted that this would lead to an increased interaction between pre-pandemic members of an ME/CFS online support community and a long COVID community. We performed a mixed-methods retrospective observational study of the Reddit activity of 7,544 users active on Reddit's long COVID forum.

Use of Quantile Treatment Effects Analysis to Describe Antidepressant Response in Randomized Clinical Trials Submitted to the US Food and Drug Administration: A Secondary Analysis of Pooled Trial Data.

Importance: Major depressive disorder (MDD) is a leading cause of global distress and disability. Earlier studies have indicated that antidepressant therapy confers a modest reduction in depressive symptoms on average, but the distribution of this reduction requires more research.

Objective: To estimate the distribution of antidepressant response by depression severity.

The association between evening social media use and delayed sleep may be causal: Suggestive evidence from 120 million Reddit timestamps.

Public health officials and clinicians routinely advise social media users to avoid nighttime social media use due to the perception that this delays the onset of sleep and predisposes to the health risks of insufficient sleep. With some exceptions, the evidence behind this advice mostly derives from surveys identifying an association between self-reported social media usage and self-reported sleep patterns. In principle, these associations could alternatively be explained by users turning to social media to pass the time when they are otherwise having difficulty sleeping, or by individual differences that draw some people to frequent social media use, or by offline activities that overlap with both social media use and delayed sleep.

Estimation of Bedtimes of Reddit Users: Integrated Analysis of Time Stamps and Surveys.

Background: Individuals with later bedtimes have an increased risk of difficulties with mood and substances. To investigate the causes and consequences of late bedtimes and other sleep patterns, researchers are exploring social media as a data source. Pioneering studies inferred sleep patterns directly from social media data.

Author Correction: Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples.

Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20128-w.

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples.

The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations.