Quadruply robust estimation of marginal structural models in observational studies subject to covariate-driven observations

Electronic health records and other sources of observational data are increasingly used for drawing causal inferences. The estimation of a causal effect using these data not meant for research purposes is subject to confounding and irregular covariate-driven observation times affecting the inference. A doubly-weighted estimator accounting for these features has previously been proposed that relies on the correct specification of two nuisance models used for the weights. In this work, we propose a novel consistent quadruply robust estimator and demonstrate analytically and in large simulation studies that it is more flexible and more efficient than its only proposed alternative. It is further applied to data from the Add Health study in the United States to estimate the causal effect of therapy counselling on alcohol consumption in American adolescents

Évaluation de la modélisation et des prévisions de la vitesse du vent menant à l'estimation de la production d'énergie annuelle d'une turbine éolienne

Suite à un stage avec la compagnie Hatch, nous possédons des jeux de données composés de séries chronologiques de vitesses de vent mesurées à divers sites dans le monde, sur plusieurs années. Les ingénieurs éoliens de la compagnie Hatch utilisent ces jeux de données conjointement aux banques de données d’Environnement Canada pour évaluer le potentiel éolien afin de savoir s’il vaut la peine d’installer des éoliennes à ces endroits. Depuis quelques années, des compagnies offrent des simulations méso-échelle de vitesses de vent, basées sur divers indices environnementaux de l’endroit à évaluer. Les ingénieurs éoliens veulent savoir s’il vaut la peine de payer pour ces données simulées, donc si celles-ci peuvent être utiles lors de l’estimation de la production d’énergie éolienne et si elles pourraient être utilisées lors de la prévision de la vitesse du vent long terme. De plus, comme l’on possède des données mesurées de vitesses de vent, l’on en profitera pour tester à partir de diverses méthodes statistiques différentes étapes de l’estimation de la production d’énergie. L’on verra les méthodes d’extrapolation de la vitesse du vent à la hauteur d’une turbine éolienne et l’on évaluera ces méthodes à l’aide de l’erreur quadratique moyenne. Aussi, on étudiera la modélisation de la vitesse du vent par la distributionWeibull et la variation de la distribution de la vitesse dans le temps. Finalement, l’on verra à partir de la validation croisée et du bootstrap si l’utilisation de données méso-échelle est préférable à celle de données des stations de référence, en plus de tester un modèle où les deux types de données sont utilisées pour prédire la vitesse du vent. Nous testerons la méthodologie globale présentement utilisée par les ingénieurs éoliens pour l’estimation de la production d’énergie d’un point de vue statistique, puis tenterons de proposer des changements à cette méthodologie, qui pourraient améliorer l’estimation de la production d’énergie annuelle.Following an internship with the company Hatch, we have access to datasets that are composed of wind speed time series measured at different sites accross the world and over several years. The wind speed engineers from Hatch are using these datasets jointly with Environment Canada databases in order to ascertain the wind energy potential of these sites and to know whether it is worth installing wind turbines there. For a few years, some companies are also offering mesoscale simulations of wind speed based on different environmental characteristics from the site we want to evaluate. We would like to know if it is worth paying for those mesoscale datasets and if they can be used to provide better estimations of the wind energy potential. Among other things, these data could be used to provide a better estimation of the long term mean wind speed. Since we already possess measured datasets, we will also use them to test, with statistical methods, the methodology currently used and the different steps leading to an estimation of the wind energy production. First of all, we will see what are the different methods that could be used to extrapolate wind speed to a wind turbine’s height and we will evaluate those methods with the mean squared extrapolation error. Also, we will study wind distribution modelling by the Weibull distribution and consider its variability over time. Finally, cross-validation and block bootstrap will be used to see whether we should use mesoscale data instead of wind data from Environment Canada or whether it would even be beneficial to use both kind of data to predict wind speed. In summary, the whole methodology used by wind speed engineers to estimate the energy production will be tested from a statistical point of view and we will attempt to propose changes in this methodology that could improve the estimation of the wind speed annual energy production

Using neighborhood observation to support public housing tenants’ empowerment

Although public housing is often described as a negative and stigmatized environment, tenants living in such an environment can cultivate a positive sense of community, which enhances their individual and collective well-being. The present study describes the second phase of a large action research, aiming to facilitate the empowerment of public housing\ud tenants acting as peer-researchers Following a Photovoice phase, this second phase focuses on the development and first implementation of a participatory observation method as a tool for evaluating their collective environment fit. A group of nine tenants contributed to\ud develop and later completed an observation grid. The observations were then discussed in decision-making sessions. The participatory observation method proved useful in supporting tenants in their reflection process, promoting the depiction of a nuanced portrait of their residential environment while also prioritizing capacity building. Results are currently used to inform an action phase in which tenants are taking increasingly more\ud power. Triangulating the results from multiple sites is needed to establish more firmly the added-value of this observation method in a larger research project. Key challenges and lessons learned are described in a reflective section, sharing experiential knowledge with researchers that consider using a similar method

Generating synthetic data from administrative health records for drug safety and effectiveness studies

Introduction Administrative health records (AHRs) are used to conduct population-based post-market drug safety and comparative effectiveness studies to inform healthcare decision making. However, the cost of data extraction, and the challenges associated with privacy and securing approvals can make it challenging for researchers to conduct methodological research in a timely manner using real data. Generating synthetic AHRs that reasonably represent the real-world data are beneficial for developing analytic methods and training analysts to rapidly implement study protocols. We generated synthetic AHRs using two methods and compared these synthetic AHRs to real-world AHRs. We described the challenges associated with using synthetic AHRs for real-world study. Methods The real-world AHRs comprised prescription drug records for individuals with healthcare insurance coverage in the Population Research Data Repository (PRDR) from Manitoba, Canada for the 10-year period from 2008 to 2017. Synthetic data were generated using the Observational Medical Dataset Simulator II (OSIM2) and a modification (ModOSIM). Synthetic and real-world data were described using frequencies and percentages. Agreement of prescription drug use measures in PRDR, OSIM2 and ModOSIM was estimated with the concordance coefficient. Results The PRDR cohort included 169,586,633 drug records and 1,395 drug types for 1,604,734 individuals. Synthetic data for 1,000,000 individuals were generated using OSIM2 and ModOSIM. Sex and age group distributions were similar in the real-world and synthetic AHRs. However, there were significant differences in the number of drug records and number of unique drugs per person for OSIM2 and ModOSIM when compared with PRDR. For the average number of days of drug use, concordance with the PRDR was 16% (95% confidence interval [CI]: 12%-19%) for OSIM2 and 88% (95% CI: 87%-90%) for ModOSIM. Conclusions ModOSIM data were more similar to PRDR than OSIM2 data on many measures. Synthetic AHRs consistent with those found in real-world settings can be generated using ModOSIM. Synthetic data will benefit rapid implementation of methodological studies and data analyst training

Core-binding factor acute myeloid leukemia with t(8;21) Risk factors and a novel scoring system (I-CBFit)

Background: Although the prognosis of core-binding factor (CBF) acute myeloid leukemia (AML) is better than other subtypes of AML, 30% of patients still relapse and may require allogeneic hematopoietic cell transplantation (alloHCT). However, there is no validated widely accepted scoring system to predict patient subsets with higher risk of relapse. Methods: Eleven centers in the US and Europe evaluated 247 patients with t(8;21) (q22;q22). Results: Complete remission (CR) rate was high (92.7%), yet relapse occurred in 27.1% of patients. A total of 24.7% of patients received alloHCT. The median diseasefree (DFS) and overall (OS) survival were 20.8 and 31.2 months, respectively. Age, KIT D816V mutated (11.3%) or nontested (36.4%) compared with KIT D816V wild type (52.5%), high white blood cell counts (WBC), and pseudodiploidy compared with hyper- or hypodiploidy were included in a scoring system (named I-CBFit). DFS rate at 2 years was 76% for patients with a low-risk I-CBFit score compared with 36% for those with a high-risk I-CBFit score (P <0.0001). Low- vs high-risk OS at 2 years was 89% vs 51% (P <0.0001). Conclusions: I-CBFit composed of readily available risk factors can be useful to tailor the therapy of patients, especially for whom alloHCT is not need in CR1 (ie, patients with a low-risk score)

An intervention strategy for improving residential environment and positive mental health among public housing tenants: rationale, design and methods of Flash on my neighborhood!

Abstract Background In Canada, public housing programs are an important part of governmental strategies to fight poverty and public exclusion. The Flash on my neighborhood! project is a four-year multiphase community-based participatory action research strategy currently implemented in six public housing developments (n = 1009 households) across the province of Québec, Canada. The goal is to reduce the mental health disparities faced by these public housing tenants compared to the general population, while identifying which environmental and policy changes are needed to turn public housing settings into healthier environments. Methods The protocol involves three successive, interconnected phases: 1) Strengths and needs assessment, including community outreach and recruitment of tenants to collaborate as peer researchers, an exploratory qualitative component (photovoice), a systematic neighborhood observation, and a household survey; 2) Action plan development, including a community forum and interactive capacity-building and discussion sessions; 3) Action plan implementation and monitoring. The entire intervention is evaluated using a mixed-method design, framed within a multiple case study perspective. Throughout the project and particularly in the evaluation phase, data will be collected to record a) contextual factors (tenants’ previous experience of participation, history of public housing development, etc.); b) activities that took place and elements from the action plan that were implemented; and c) short- and medium-term outcomes (objective and perceived improvements in the quality of the residential setting, both physically and in terms of mental health and social capital). Discussion The study will provide unprecedented evidence-based information on the key ingredients of a collective intervention process associated with the increased collective empowerment and positive mental health of public housing tenants

Self-Management Strategies in Recovery From Mood and Anxiety Disorders

Mood and anxiety disorders are the most prevalent mental disorders. People with such disorders implement self-management strategies to reduce or prevent their symptoms and to optimize their health and well-being. Even though self-management strategies are known to be essential to recovery, few researchers have examined them. The aim of this study is to explore strategies used by people recovering from depressive, anxiety, and bipolar disorders by asking 50 of them to describe their own strategies. Strategies were classified according to dimensions of recovery: social, existential, functional, physical, and clinical. Within these themes, 60 distinct strategies were found to be used synergistically to promote personal recovery as well as symptom reduction. Findings highlight the diversity of strategies used by people, whether they have depressive, anxiety, or bipolar disorders. This study underscores the importance of supporting self-management in a way that respects individual experience