Comparing Machine Learning Methods for Estimating Heterogeneous Treatment Effects by Combining Data from Multiple Randomized Controlled Trials

Individualized treatment decisions can improve health outcomes, but using data to make these decisions in a reliable, precise, and generalizable way is challenging with a single dataset. Leveraging multiple randomized controlled trials allows for the combination of datasets with unconfounded treatment assignment to improve the power to estimate heterogeneous treatment effects. This paper discusses several non-parametric approaches for estimating heterogeneous treatment effects using data from multiple trials. We extend single-study methods to a scenario with multiple trials and explore their performance through a simulation study, with data generation scenarios that have differing levels of cross-trial heterogeneity. The simulations demonstrate that methods that directly allow for heterogeneity of the treatment effect across trials perform better than methods that do not, and that the choice of single-study method matters based on the functional form of the treatment effect. Finally, we discuss which methods perform well in each setting and then apply them to four randomized controlled trials to examine effect heterogeneity of treatments for major depressive disorder

Methods for Integrating Trials and Non-Experimental Data to Examine Treatment Effect Heterogeneity

Estimating treatment effects conditional on observed covariates can improve the ability to tailor treatments to particular individuals. Doing so effectively requires dealing with potential confounding, and also enough data to adequately estimate effect moderation. A recent influx of work has looked into estimating treatment effect heterogeneity using data from multiple randomized controlled trials and/or observational datasets. With many new methods available for assessing treatment effect heterogeneity using multiple studies, it is important to understand which methods are best used in which setting, how the methods compare to one another, and what needs to be done to continue progress in this field. This paper reviews these methods broken down by data setting: aggregate-level data, federated learning, and individual participant-level data. We define the conditional average treatment effect and discuss differences between parametric and nonparametric estimators, and we list key assumptions, both those that are required within a single study and those that are necessary for data combination. After describing existing approaches, we compare and contrast them and reveal open areas for future research. This review demonstrates that there are many possible approaches for estimating treatment effect heterogeneity through the combination of datasets, but that there is substantial work to be done to compare these methods through case studies and simulations, extend them to different settings, and refine them to account for various challenges present in real data

Paradoxical family practices: LGBTQ+ young people, mental health and wellbeing

This article will explore how LGBTQ+ young people sustain, and in some cases survive, family relationships. We develop the concept of ‘paradoxical family practices’ and use this to demonstrate the ways in which LGBTQ+ young people manage family life through everyday emotion work. This highlights: (1) how families ordinarily navigate heteronormativity and ‘issues’ of gender/sexuality; (2) the efficacy of ‘paradoxical family practices’ as a conceptual tool; (3) the value of emotion-centred multiple qualitative methods to explore the lives of LGBTQ+ young people and mental health. Findings derive from a small-scale UK study funded by the Wellcome Trust (UNS39780) and were generated through a two-stage methodology comprising digital/paper emotion maps and qualitative interviews with LGBTQ+ young people aged 16–25 (n = 12) followed by diary methods and follow-up interviews (n = 9). Interviews were also completed with ‘family members’ (n = 7)

Socioeconomic inequalities in pregnancy outcome associated with Down syndrome: a population-based study.

OBJECTIVE: To investigate socioeconomic inequalities in outcome of pregnancy associated with Down syndrome (DS) compared with other congenital anomalies screened for during pregnancy. DESIGN AND SETTING: Retrospective population-based registry study (East Midlands & South Yorkshire in England). PARTICIPANTS: All registered cases of DS and nine selected congenital anomalies with poor prognostic outcome (the UK Fetal Anomaly Screening Programme (FASP)9) with an end of pregnancy date between 1 January 1998 and 31 December 2007. MAIN OUTCOME MEASURES: Poisson regression models were used to explore outcome measures, including socioeconomic variation in rates of anomaly; antenatal detection; pregnancy outcome; live birth incidence and neonatal mortality. Deprivation was measured using the Index of Multiple Deprivation 2004 at super output area level. RESULTS: There were 1151 cases of DS and 1572 cases of the nine severe anomalies combined. The overall rate of antenatal detection was 57% for DS, which decreased with increasing deprivation (rate ratio comparing the most deprived tenth with the least deprived: 0.76 (0.60 to 0.97)). Antenatal detection rates were considerably higher for FASP9 anomalies (86%), with no evidence of a trend with deprivation (0.99 95% CI (0.84 to 1.17)). The termination of pregnancy rate following antenatal diagnosis was higher for DS (86%) than the FASP9 anomalies (70%). Both groups showed wide socioeconomic variation in the termination of pregnancy rate (rate ratio: DS: 0.76 (0.58 to 0.99); FASP9 anomalies: 0.80 (0.65 to 0.97)). Consequently, socioeconomic inequalities in live birth and neonatal mortality rates associated with these anomalies arise that were not observed in utero. CONCLUSIONS: Socioeconomic inequalities exist in the antenatal detection of DS, and subsequent termination rates are much higher for DS than other anomalies. Termination rates for all anomalies are lower in more deprived areas leading to wide socioeconomic inequalities in live born infants with a congenital anomaly, particularly DS, and subsequent neonatal mortality

What turns galaxies off? The different morphologies of star-forming and quiescent galaxies since z~2 from CANDELS

We use HST/WFC3 imaging from the CANDELS Multicycle Treasury Survey, in conjunction with the Sloan Digital Sky Survey, to explore the evolution of galactic structure for galaxies with stellar masses >3e10M_sun from z=2.2 to the present epoch, a time span of 10Gyr. We explore the relationship between rest-frame optical color, stellar mass, star formation activity and galaxy structure. We confirm the dramatic increase from z=2.2 to the present day in the number density of non-star-forming galaxies above 3e10M_sun reported by others. We further find that the vast majority of these quiescent systems have concentrated light profiles, as parametrized by the Sersic index, and the population of concentrated galaxies grows similarly rapidly. We examine the joint distribution of star formation activity, Sersic index, stellar mass, inferred velocity dispersion, and stellar surface density. Quiescence correlates poorly with stellar mass at all z<2.2. Quiescence correlates well with Sersic index at all redshifts. Quiescence correlates well with `velocity dispersion' and stellar surface density at z>1.3, and somewhat less well at lower redshifts. Yet, there is significant scatter between quiescence and galaxy structure: while the vast majority of quiescent galaxies have prominent bulges, many of them have significant disks, and a number of bulge-dominated galaxies have significant star formation. Noting the rarity of quiescent galaxies without prominent bulges, we argue that a prominent bulge (and perhaps, by association, a supermassive black hole) is an important condition for quenching star formation on galactic scales over the last 10Gyr, in qualitative agreement with the AGN feedback paradigm.Comment: The Astrophysical Journal, in press; 20 pages with 13 figure

Magnetic Graphene Nanohole Superlattices

We investigate the magnetic properties of nano-holes (NHs) patterned in graphene using first principles calculations. We show that superlattices consisting of a periodic array of NHs form a new family of 2D crystalline "bulk" magnets whose collective magnetic behavior is governed by inter-NH spin-spin interaction. They exhibit long-range magnetic order well above room temperature. Furthermore, magnetic semiconductors can be made by doping magnetic NHs into semiconducting NH superlattices. Our findings offer a new material system for fundamental studies of spin-spin interaction and magnetic ordering in low dimensions, and open up the exciting opportunities of making engineered magnetic materials for storage media and spintronics applications

Family Trouble: Heteronormativity, emotion work and queer youth mental health

Conflict with the family about sexual orientation and gender diversity is a key risk factor associated with poor mental health in youth populations. Findings presented here derive from a UK study that employed an interdisciplinary critical mental health approach that de-pathologized emotional distress and conceptualised families as social and affective units that are created through everyday practices. Our aim was to explore how family relationships foster, maintain or harm the mental health and wellbeing of LGBTQ+ youth. Data were generated through exploratory visual, creative and digital qualitative methods in two phases. Phase 1 involved digital/paper emotion maps and interviews with LGBTQ+ youth aged 16-25 (n=12) and family member/mentor interviews (n=7). Phase 2 employed diary methods and follow-up interviews (n=9). The data analytic strategy involved three stages: individual case analysis; cross-sectional thematic analysis; and metainterpretation. We found that family relationships impacted on queer youth mental health in complex ways that were related to the establishment of their autonomous queer selves, the desire to remain belonging to their family and the need to maintain a secure environment. The emotion work involved in navigating identity, belonging and security was made difficult because of family heteronormativity, youth autonomy and family expectations and had a stark impact on queer youth mental health and wellbeing. Improving the mental health of LGBTQ+ youth requires a much deeper understanding of the emotionality of family relationships and the difficulties negotiating these as a young person

Woman-Centered Design through Humanity, Activism, and Inclusion

Women account for over half of the global population, however, continue to be subject to systematic and systemic disadvantage, particularly in terms of access to health and education. At every intersection, where systemic inequality accounts for greater loss of life or limitations on full and healthy living, women are more greatly impacted by those inequalities. The design of technologies is no different, the very definition of technology is historically cast in terms of male activities, and advancements in the field are critical to improve women's quality of life. This article views HCI, a relatively new field, as well positioned to act critically in the ways that technology serve, refigure, and redefine women's bodies. Indeed, the female body remains a contested topic, a restriction to the development of women's health. On one hand, the field of women's health has attended to the medicalization of the body and therefore is to be understood through medical language and knowledge. On the other hand, the framing of issues associated with women's health and people's experiences of and within such system(s) remain problematic for many. This is visible today in, e.g., socio-cultural practices in disparate geographies or medical devices within a clinic or the home. Moreover, the biological body is part of a great unmentionable, i.e., the perils of essentialism. We contend that it is necessary, pragmatically and ethically, for HCI to turn its attention toward a woman-centered design approach. While previous research has argued for the dangers of gender-demarcated design work, we advance that designing for and with women should not be regarded as ghettoizing, but instead as critical to improving women's experiences in bodily transactions, choices, rights, and access to and in health and care. In this article, we consider how and why designing with and for woman matters. We use our design-led research as a way to speak to and illustrate alternatives to designing for and with women within HCI.QC 20200930</p

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders