Regression modeling of longitudinal binary outcomes with outcome-dependent observation times

Conventional longitudinal data analysis methods assume that outcomes are independent of the data-collection schedule. However, the independence assumption may be violated, for example, when adverse events trigger additional physician visits in between prescheduled follow-ups. Observation times may therefore be associated with outcome values, which may introduce bias when estimating the eect of covariates on outcomes using standard longitudinal regression methods. Existing semi-parametric methods that accommodate outcome-dependent observation times are limited to the analysis of continuous outcomes. We develop new methods for the analysis of binary outcomes, while retaining the exibility of semi-parametric models. Our methods are based on counting process approaches, rather than relying on possibly intractable likelihood-based or pseudo-likelihood-based approaches, and provide marginal, population-level inference. In simulations, we evaluate the statistical properties of our proposed methods. Comparisons are made to \u27naive\u27 GEE approaches that either do not account for outcome-dependent observation times or incorporate weights based on the observation-time process. We illustrate the utility of our proposed methods using data from a randomized controlled trial of interventions designed to improve adherence to warfarin therapy. We show that our method performs well in the presence of outcome-dependent observation times, and provide identical inference to \u27naive\u27 approaches when observation times are not associated with outcomes

The potential of crowdsourcing and mobile technology to support flood disaster risk reduction

The last decade has seen a rise in citizen science and crowdsourcing for carrying out a variety of tasks across a number of different fields, most notably the collection of data such as the identification of species (e.g. eBird and iNaturalist) and the classification of images (e.g. Galaxy Zoo and Geo-Wiki). Combining human computing with the proliferation of mobile technology has resulted in vast amounts of geo-located data that have considerable value across multiple domains including flood disaster risk reduction. Crowdsourcing technologies, in the form of online mapping, are now being utilized to great effect in post-disaster mapping and relief efforts, e.g. the activities of Humanitarian OpenStreetMap, complementing official channels of relief (e.g. Haiti, Nepal and New York). Disaster event monitoring efforts have been further complemented with the use of social media (e.g. twitter for earthquakes, flood monitoring, and fire detection). Much of the activity in this area has focused on ex-post emergency management while there is considerable potential for utilizing crowdsourcing and mobile technology for vulnerability assessment, early warning and to bolster resilience to flood events. This paper examines the use of crowdsourcing and mobile technology for measuring and monitoring flood hazards, exposure to floods, and vulnerability, drawing upon examples from the literature and ongoing projects on flooding and food security at IIASA

Technologies to Support Community Flood Disaster Risk Reduction

Floods affect more people globally than any other type of natural hazard. Great potential exists for new technologies to support flood disaster risk reduction. In addition to existing expert-based data collection and analysis, direct input from communities and citizens across the globe may also be used to monitor, validate, and reduce flood risk. New technologies have already been proven to effectively aid in humanitarian response and recovery. However, while ex-ante technologies are increasingly utilized to collect information on exposure, efforts directed towards assessing and monitoring hazards and vulnerability remain limited. Hazard model validation and social vulnerability assessment deserve particular attention. New technologies offer great potential for engaging people and facilitating the coproduction of knowledge

A systematic review of mental disorder, suicide, and deliberate self harm in lesbian, gay and bisexual people

Background: Lesbian, gay and bisexual (LGB) people may be at higher risk of mental disorders than heterosexual people.Method: We conducted a systematic review and meta-analysis of the prevalence of mental disorder, substance misuse, suicide, suicidal ideation and deliberate self harm in LGB people. We searched Medline, Embase, PsycInfo, Cinahl, the Cochrane Library Database, the Web of Knowledge, the Applied Social Sciences Index and Abstracts, the International Bibliography of the Social Sciences, Sociological Abstracts, the Campbell Collaboration and grey literature databases for articles published January 1966 to April 2005. We also used Google and Google Scholar and contacted authors where necessary. We searched all terms related to homosexual, lesbian and bisexual people and all terms related to mental disorders, suicide, and deliberate self harm. We included papers on population based studies which contained concurrent heterosexual comparison groups and valid definition of sexual orientation and mental health outcomes.Results: Of 13706 papers identified, 476 were initially selected and 28 (25 studies) met inclusion criteria. Only one study met all our four quality criteria and seven met three of these criteria. Data was extracted on 214,344 heterosexual and 11,971 non heterosexual people. Meta-analyses revealed a two fold excess in suicide attempts in lesbian, gay and bisexual people [ pooled risk ratio for lifetime risk 2.47 (CI 1.87, 3.28)]. The risk for depression and anxiety disorders (over a period of 12 months or a lifetime) on meta-analyses were at least 1.5 times higher in lesbian, gay and bisexual people (RR range 1.54-2.58) and alcohol and other substance dependence over 12 months was also 1.5 times higher (RR range 1.51-4.00). Results were similar in both sexes but meta analyses revealed that lesbian and bisexual women were particularly at risk of substance dependence (alcohol 12 months: RR 4.00, CI 2.85, 5.61; drug dependence: RR 3.50, CI 1.87, 6.53; any substance use disorder RR 3.42, CI 1.97-5.92), while lifetime prevalence of suicide attempt was especially high in gay and bisexual men (RR 4.28, CI 2.32, 7.88).Conclusion: LGB people are at higher risk of mental disorder, suicidal ideation, substance misuse, and deliberate self harm than heterosexual people

Fine-Scale Mapping of the 5q11.2 Breast Cancer Locus Reveals at Least Three Independent Risk Variants Regulating MAP3K1

Peer reviewe

Breast cancer polygenic risk score and contralateral breast cancer risk

Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS313) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS313 was quantified using Cox regression analyses. We assessed PRS313 interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS313 was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS313 was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18–1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02–1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10th percentile and 20.5% at the 90th percentile of PRS313. We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS313 alone was 0.563 (95%CI = 0.547–0.586). In conclusion, PRS313 is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies