Trend-Following versus Cross-Sectional Momentum: A Data-Driven Statistical Factor Comparison

Abstract This research focused on developing a hybrid quality monitoring model through combining the data driven and key engineering parameters to predict the friction stir blind riveting (FSBR) joint quality. The hybrid model was formulated through utilizing the in-situ processing and joint property data. The in-situ data involved sensor fusion (force and torque signals) and key processing parameters (spindle speed, feed rate and stacking sequence) for data-driven modeling. The quality of the FSBR joints was defined by the tensile strength. Further, the joint cross-sectional analysis and failure modes in lap-shear tests were employed to confirm the efficacy of the proposed model and development of the process-structure-property relationship.

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Data-Driven Decision Making and Proactive Citizen–Scientist Communication: A Cross-Sectional Study on COVID-19 Vaccination Adherence

Vaccines ◽

10.3390/vaccines9121384 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1384

Author(s):

Emil Syundyukov ◽

Martins Mednis ◽

Linda Zaharenko ◽

Eva Pildegovica ◽

Ieva Danovska ◽

...

Keyword(s):

Public Health ◽

Logistic Regression ◽

Exploratory Study ◽

Large Scale ◽

Severe Disease ◽

Data Driven ◽

Personal Factors ◽

Cross Sectional ◽

Digital Platform ◽

Factors Associated

Due to the severe impact of COVID-19 on public health, rollout of the vaccines must be large-scale. Current solutions are not intended to promote an active collaboration between communities and public health researchers. We aimed to develop a digital platform for communication between scientists and the general population, and to use it for an exploratory study on factors associated with vaccination readiness. The digital platform was developed in Latvia and was equipped with dynamic consent management. During a period of six weeks 467 participants were enrolled in the population-based cross-sectional exploratory study using this platform. We assessed demographics, COVID-19-related behavioral and personal factors, and reasons for vaccination. Logistic regression models adjusted for the level of education, anxiety, factors affecting the motivation to vaccinate, and risk of infection/severe disease were built to investigate their association with vaccination readiness. In the fully adjusted multiple logistic regression model, factors associated with vaccination readiness were anxiety (odds ratio, OR = 3.09 [95% confidence interval 1.88; 5.09]), feelings of social responsibility (OR = 1.61 [1.16; 2.22]), and trust in pharmaceutical companies (OR = 1.53 [1.03; 2.27]). The assessment of a large number of participants in a six-week period show the potential of a digital platform to create a data-driven dialogue on vaccination readiness.

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Admixture analysis of age at onset in older patients with schizophrenia spectrum disorders

International Psychogeriatrics ◽

10.1017/s104161022000085x ◽

2020 ◽

Vol 32 (6) ◽

pp. 781-785

Author(s):

Maureen M. J. Smeets-Janssen ◽

Idan M. Aderka ◽

Paul D. Meesters ◽

Sjors Lange ◽

Sigfried Schouws ◽

...

Keyword(s):

Late Onset ◽

Age At Onset ◽

Cross Sectional Study ◽

Data Driven ◽

Admixture Analysis ◽

Schizophrenia Spectrum Disorders ◽

Sectional Study ◽

Cross Sectional ◽

Schizophrenia Spectrum ◽

Spectrum Disorders

ABSTRACTThe nature of schizophrenia spectrum disorders with an onset in middle or late adulthood remains controversial. The aim of our study was to determine in patients aged 60 and older if clinically relevant subtypes based on age at onset can be distinguished, using admixture analysis, a data-driven technique. We conducted a cross-sectional study in 94 patients aged 60 and older with a diagnosis of schizophrenia or schizoaffective disorder. Admixture analysis was used to determine if the distribution of age at onset in this cohort was consistent with one or more populations of origin and to determine cut-offs for age at onset groups, if more than one population could be identified. Results showed that admixture analysis based on age at onset demonstrated only one normally distributed population. Our results suggest that in older schizophrenia patients, early- and late-onset ages form a continuum.

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Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications

Cuadernos de Economía ◽

10.15446/cuad.econ.v37n75.69832 ◽

2018 ◽

Vol 37 (75) ◽

pp. 779-808 ◽

Cited By ~ 1

Author(s):

Alex Coad ◽

Dominik Janzing ◽

Paul Nightingale

Keyword(s):

Causal Inference ◽

Learning Community ◽

Firm Growth ◽

Additive Noise ◽

Public Funding ◽

Data Driven ◽

Cross Sectional ◽

Innovation Surveys ◽

Noise Models ◽

D Investment

This paper presents a new statistical toolkit by applying three techniques for data-driven causal inference from the machine learning community that are little-known among economists and innovation scholars: a conditional independence-based approach, additive noise models, and non-algorithmic inference by hand. We include three applications to CIS data to investigate public funding schemes for R&D investment, information sources for innovation, and innovation expenditures and firm growth. Preliminary results provide causal interpretations of some previously-observed correlations. Our statistical 'toolkit' could be a useful complement to existing techniques.

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Providing Data-Driven Equitable Palliative and End-of-Life Care for Structurally Vulnerable Populations: A Pilot Survey of Information Management Strategies

American Journal of Hospice and Palliative Medicine® ◽

10.1177/1049909119872756 ◽

2019 ◽

Vol 37 (4) ◽

pp. 244-249

Author(s):

Akshay Rajaram ◽

Trevor Morey ◽

Sonam Shah ◽

Naheed Dosani ◽

Muhammad Mamdani

Keyword(s):

Palliative Care ◽

North America ◽

Data Collection ◽

Information Management ◽

Vulnerable Populations ◽

Management Strategies ◽

Data Driven ◽

Cross Sectional ◽

Convenience Sample ◽

Data Infrastructure

Background: Considerable gains are being made in data-driven efforts to advance quality improvement in health care. However, organizations providing hospice-oriented palliative care for structurally vulnerable persons with terminal illnesses may not have the enabling data infrastructure or framework to derive such benefits. Methods: We conducted a pilot cross-sectional qualitative study involving a convenience sample of hospice organizations across North America providing palliative care services for structurally vulnerable patients. Through semistructured interviews, we surveyed organizations on the types of data collected, the information systems used, and the challenges they faced. Results: We contacted 13 organizations across North America and interviewed 9. All organizations served structurally vulnerable populations, including the homeless and vulnerably housed, socially isolated, and HIV-positive patients. Common examples of collected data included the number of referrals, the number of admissions, length of stay, and diagnosis. More than half of the organizations (n = 5) used an electronic medical record, although none of the record systems were specifically designed for palliative care. All (n = 9) the organizations used the built-in reporting capacity of their information management systems and more than half (n = 6) augmented this capacity with chart reviews. Discussion: A number of themes emerged from our discussions. Present data collection is heterogeneous, and storage of these data is highly fragmented within and across organizations. Funding appeared to be a key enabler of more robust data collection and use. Future work should address these gaps and examine opportunities for innovative ways of analysis and reporting to improve care for structurally vulnerable populations.

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Health-Behaviors Associated With the Growing Risk of Adolescent Suicide Attempts: A Data-Driven Cross-Sectional Study

American Journal of Health Promotion ◽

10.1177/0890117120977378 ◽

2020 ◽

pp. 089011712097737

Author(s):

Zhiyuan Wei ◽

Sayanti Mukherjee

Keyword(s):

Health Behaviors ◽

Goodness Of Fit ◽

Adolescent Suicide ◽

Suicide Attempts ◽

Predictive Accuracy ◽

Warning Signs ◽

Machine Learning Algorithms ◽

Data Driven ◽

Cross Sectional ◽

Increased Risk

Purpose: Identify and examine the associations between health behaviors and increased risk of adolescent suicide attempts, while controlling for socio-economic and demographic differences. Design: A data-driven analysis using cross-sectional data. Setting: Communities in the state of Montana from 1999 to 2017. Selected Montana as it persistently ranks among the top 3 vulnerable states in the U.S. over the past years. Subjects: Selected 22,447 adolescents of whom 1,631 adolescents attempted suicide at least once. Measures: Overall 29 variables (predictors) accounting for psychological behaviors, illegal substances consumption, daily activities at schools and demographic backgrounds were considered. Analysis: A library of machine learning algorithms along with the traditionally-used logistic regression were used to model and predict suicide attempt risk. Model performances—goodness-of-fit and predictive accuracy—were measured using accuracy, precision, recall and F-score metrics. Additionally, χ2 analysis was used to evaluate the statistical significance of each variable. Results: The non-parametric Bayesian tree ensemble model outperformed all other models, with 80.0% accuracy in goodness-of-fit (F-score: 0.802) and 78.2% in predictive accuracy (F-score: 0.785). Key health-behaviors identified include: being sad/hopeless ( p < 0.0001), followed by safety concerns at school ( p < 0.0001), physical fighting ( p < 0.0001), inhalant usage ( p < 0.0001), illegal drugs consumption at school ( p < 0.0001), current cigarette usage ( p < 0.0001), and having first sex at an early age (below 15 years of age). Additionally, the minority groups (American Indian/Alaska Natives, Hispanics/Latinos) ( p < 0.0001), and females ( p < 0.0001) are also found to be highly vulnerable to attempting suicides. Conclusion: Significant contribution of this work is understanding the key health-behaviors and health disparities that lead to higher frequency of suicide attempts among adolescents, while accounting for the non-linearity and complex interactions among the outcome and the exposure variables. Findings provide insights on key health-behaviors that can be viewed as early warning signs/precursors of suicide attempts among adolescents.

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Comparing dietary patterns derived by two methods and their associations with obesity in Polish girls aged 13–21 years: the cross-sectional GEBaHealth study

Perspectives in Public Health ◽

10.1177/1757913916679859 ◽

2016 ◽

Vol 137 (3) ◽

pp. 182-189 ◽

Cited By ~ 9

Author(s):

Lidia Wadolowska ◽

Joanna Kowalkowska ◽

Jolanta Czarnocinska ◽

Marzena Jezewska-Zychowicz ◽

Ewa Babicz-Zielinska

Keyword(s):

Cluster Analysis ◽

Dietary Patterns ◽

Central Obesity ◽

Reference Group ◽

A Priori ◽

Multiple Logistic Regression Analysis ◽

Total Sample ◽

Data Driven ◽

Low Fat ◽

Cross Sectional

Aims: To compare dietary patterns (DPs) derived by two methods and their assessment as a factor of obesity in girls aged 13–21 years. Methods: Data from a cross-sectional study conducted among the representative sample of Polish females ( n = 1,107) aged 13–21 years were used. Subjects were randomly selected. Dietary information was collected using three short-validated food frequency questionnaires (FFQs) regarding fibre intake, fat intake and overall food intake variety. DPs were identified by two methods: a priori approach (a priori DPs) and cluster analysis (data-driven DPs). The association between obesity and DPs and three single dietary characteristics was examined using multiple logistic regression analysis. Results: Four data-driven DPs were obtained: ‘Low-fat-Low-fibre-Low-varied’ (21.2%), ‘Low-fibre’ (29.1%), ‘Low-fat’ (25.0%) and ‘High-fat-Varied’ (24.7%). Three a priori DPs were pre-defined: ‘Non-healthy’ (16.6%), ‘Neither-pro-healthy-nor-non-healthy’ (79.1%) and ‘Pro-healthy’ (4.3%). Girls with ‘Low-fibre’ DP were less likely to have central obesity (adjusted odds ratio (OR) = 0.36; 95% confidence interval (CI): 0.17, 0.75) than girls with ‘Low-fat-Low-fibre-Low-varied’ DP (reference group, OR = 1.00). No significant associations were found between a priori DPs and overweight including obesity or central obesity. The majority of girls with ‘Non-healthy’ DP were also classified as ‘Low-fibre’ DP in the total sample, in girls with overweight including obesity and in girls with central obesity (81.7%, 80.6% and 87.3%, respectively), while most girls with ‘Pro-healthy’ DP were classified as ‘Low-fat’ DP (67.8%, 87.6% and 52.1%, respectively). Conclusion: We found that the a priori approach as well as cluster analysis can be used to derive opposite health-oriented DPs in Polish females. Both methods have provided disappointing outcomes in explaining the association between obesity and DPs. The cluster analysis, in comparison with the a priori approach, was more useful for finding any relationship between DPs and central obesity. Our study highlighted the importance of method used to derive DPs in exploring associations between diet and obesity.

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pySuStaIn: a Python implementation of the Subtype and Stage Inference algorithm

10.1101/2021.06.09.447713 ◽

2021 ◽

Author(s):

Leon M Aksman ◽

Peter A Wijeratne ◽

Neil P Oxtoby ◽

Arman Eshaghi ◽

Cameron Shand ◽

...

Keyword(s):

Disease Progression ◽

Data Driven ◽

Simple Extension ◽

Clinical Classification ◽

High Demand ◽

Cross Sectional ◽

Widespread Application ◽

Treatment Development ◽

Python Package

Progressive disorders are highly heterogeneous. Symptom-based clinical classification of these disorders may not reflect the underlying pathobiology. Data-driven subtyping and staging of patients has the potential to disentangle the complex spatiotemporal patterns of disease progression. Tools that enable this are in high demand from clinical and treatment-development communities. Here we describe the pySuStaIn software package, a Python-based implementation of the Subtype and Stage Inference (SuStaIn) algorithm. SuStaIn unravels the complexity of heterogeneous diseases by inferring multiple disease progression patterns (subtypes) and individual severity (stages) from cross-sectional data. The primary aims of pySuStaIn are to enable widespread application and translation of SuStaIn via an accessible Python package that supports simple extension and generalization to novel modelling situations within a single, consistent architecture.

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Assessment of presentation patterns of repetitive behaviors in Autism: a cross-sectional video-recording study. Preliminary report

10.1101/2021.02.20.21252055 ◽

2021 ◽

Author(s):

Enzo Grossi ◽

Elisa Caminada ◽

Beatrice Vescovo ◽

Tristana Castrignano ◽

Daniele Piscitelli ◽

...

Keyword(s):

Expression Patterns ◽

Video Recording ◽

Repetitive Behaviors ◽

Data Driven ◽

Body Parts ◽

Autism Severity ◽

Cross Sectional ◽

Pattern Complexity ◽

Data Driven Approach

AbstractTwenty expert caregivers wearing a body cam recorded 1868 videoclips in 67 autistic subjects along a 3 months close follow-up. A team consisting of a senior child neuro-psychiatrist and a senior psychologist selected 780 of them as expressing repetitive behaviors (RB) and made an empirical classification according to components, complexity, body parts and sensory channels involved, with the aim to understand better the pattern complexity and correlate with autism severity. The RB spectrum for each subject ranged from 1 to 33 different patterns (average= 11.6; S.D.= 6.82). Forty subjects expressed prevalent simple pattern and 27 prevalent complex patterns. No significant differences are found between the two groups according to ADOS score severity. This study represents a first attempt to systematically document expression patterns of RB with a data driven approach. This may provide a better understanding of the pathophysiology, diagnosis, and treatment of RB.

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Implications of data-driven analyses for personalized therapy in psychosis: a systematic review of cluster- and trajectory-based modelling studies

10.1101/599498 ◽

2019 ◽

Cited By ~ 2

Author(s):

Tesfa Dejenie Habtewold ◽

Lyan H. Rodijk ◽

Edith J. Liemburg ◽

Grigory Sidorenkov ◽

H. Marike Boezen ◽

...

Keyword(s):

Systematic Review ◽

Longitudinal Studies ◽

Cognitive Deficits ◽

Negative Symptoms ◽

Clinical Symptoms ◽

Data Driven ◽

Schizophrenia Spectrum Disorders ◽

Cross Sectional ◽

Modelling Studies ◽

Positive And Negative Symptoms

AbstractIntroductionTo tackle the phenotypic heterogeneity of schizophrenia, data-driven methods are often applied to identify subtypes of its (sub)clinical symptoms though there is no systematic review.AimsTo summarize the evidence from cluster- and trajectory-based studies of positive, negative and cognitive symptoms in patients with schizophrenia spectrum disorders, their siblings and healthy people. Additionally, we aimed to highlight knowledge gaps and point out future directions to optimize the translatability of cluster- and trajectory-based studies.MethodsA systematic review was performed through searching PsycINFO, PubMed, PsycTESTS, PsycARTICLES, SCOPUS, EMBASE, and Web of Science electronic databases. Both cross-sectional and longitudinal studies published from 2008 to 2019, which reported at least two statistically derived clusters or trajectories were included. Two reviewers independently screened and extracted the data.ResultsOf 2,285 studies retrieved, 50 studies (17 longitudinal and 33 cross-sectional) conducted in 30 countries were selected for review. Longitudinal studies discovered two to five trajectories of positive and negative symptoms in patient, and four to five trajectories of cognitive deficits in patient and sibling. In cross-sectional studies, three clusters of positive and negative symptoms in patient, four clusters of positive and negative schizotypy in sibling, and three to five clusters of cognitive deficits in patient and sibling were identified. These studies also reported multidimensional predictors of clusters and trajectories.ConclusionsOur findings indicate that (sub)clinical symptoms of schizophrenia are more heterogeneous than currently recognized. Identified clusters and trajectories can be used as a basis for personalized psychiatry.

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