Descriptive statistics and distribution of PCOC SAS total scores and individual items, including by distress status.

IntroductionA required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data.MethodsThe system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality.ResultsThe system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects.DiscussionMedical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.

Note. BA = body acceptance, SP = self-protection from social stigmas, FB = feeling and believing in one's capacities, M = mean, SD = standard deviation, λ = item-factor loading, θ = error term.Brazilian Portuguese original version of the items are given in brackets.Item-factor loadings and descriptive statistics of SAS-EB item scores in Study 3.

analyze the survey of consumer finances (scf) with r the survey of consumer finances (scf) tracks the wealth of american families. every three years, more than five thousand households answer a battery of questions about income, net worth, credit card debt, pensions, mortgages, even the lease on their cars. plenty of surveys collect annual income, only the survey of consumer finances captures such detailed asset data. responses are at the primary economic unit-level (peu) - the economically dominant, financially interdependent family members within a sampled household. norc at the university of chicago administers the data collection, but the board of governors of the federal reserve pay the bills and therefore call the shots. if you were so brazen as to open up the microdata and run a simple weighted median, you'd get the wrong answer. the five to six thousand respondents actually gobble up twenty-five to thirty thousand records in the final pub lic use files. why oh why? well, those tables contain not one, not two, but five records for each peu. wherever missing, these data are multiply-imputed, meaning answers to the same question for the same household might vary across implicates. each analysis must account for all that, lest your confidence intervals be too tight. to calculate the correct statistics, you'll need to break the single file into five, necessarily complicating your life. this can be accomplished with the meanit sas macro buried in the 2004 scf codebook (search for meanit - you'll need the sas iml add-on). or you might blow the dust off this website referred to in the 2010 codebook as the home of an alternative multiple imputation technique, but all i found were broken links. perhaps it's time for plan c, and by c, i mean free. read the imputation section of the latest codebook (search for imputation), then give these scripts a whirl. they've got that new r smell. the lion's share of the respondents in the survey of consumer finances get drawn from a pretty standard sample of american dwellings - no nursing homes, no active-duty military. then there's this secondary sample of richer households to even out the statistical noise at the higher end of the i ncome and assets spectrum. you can read more if you like, but at the end of the day the weights just generalize to civilian, non-institutional american households. one last thing before you start your engine: read everything you always wanted to know about the scf. my favorite part of that title is the word always. this new github repository contains t hree scripts: 1989-2010 download all microdata.R initiate a function to download and import any survey of consumer finances zipped stata file (.dta) loop through each year specified by the user (starting at the 1989 re-vamp) to download the main, extract, and replicate weight files, then import each into r break the main file into five implicates (each containing one record per peu) and merge the appropriate extract data onto each implicate save the five implicates and replicate weights to an r data file (.rda) for rapid future loading 2010 analysis examples.R prepare two survey of consumer finances-flavored multiply-imputed survey analysis functions load the r data files (.rda) necessary to create a multiply-imputed, replicate-weighted survey design demonstrate how to access the properties of a multiply-imput ed survey design object cook up some descriptive statistics and export examples, calculated with scf-centric variance quirks run a quick t-test and regression, but only because you asked nicely replicate FRB SAS output.R reproduce each and every statistic pr ovided by the friendly folks at the federal reserve create a multiply-imputed, replicate-weighted survey design object re-reproduce (and yes, i said/meant what i meant/said) each of those statistics, now using the multiply-imputed survey design object to highlight the statistically-theoretically-irrelevant differences click here to view these three scripts for more detail about the survey of consumer finances (scf), visit: the federal reserve board of governors' survey of consumer finances homepage the latest scf chartbook, to browse what's possible. (spoiler alert: everything.) the survey of consumer finances wikipedia entry the official frequently asked questions notes: nationally-representative statistics on the financial health, wealth, and assets of american hous eholds might not be monopolized by the survey of consumer finances, but there isn't much competition aside from the assets topical module of the survey of income and program participation (sipp). on one hand, the scf interview questions contain more detail than sipp. on the other hand, scf's smaller sample precludes analyses of acute subpopulations. and for any three-handed martians in the audience, ther e's also a few biases between these two data sources that you ought to consider. the survey methodologists at the federal reserve take their job...

Output from programming code written to summarize data describing 2018 MCSP Trial monitoring sites acquired using a SOP 1 (see ServCat reference 103364) of FWS Legacy Regions 2 and 3. Monitoring sites were selected using a custom GRTS draw conducted by USGS in 2017, within monitoring areas associated with select NWRS stations. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA) NWRs and several locations near the town of Lamoni, Iowa and private lands in northern Missouri.

SAS code used to produce descriptive statistics for Low-wage Atlas. These include demographics of low-wage workers by state. Program is run on American Community Survey 1% sample data.

Output from programming code written to summarize data describing 2017 MCSP Trial monitoring sites acquired using a SOP 1 (see ServCat reference 103364) of FWS Legacy Regions 2 and 3. 2017 monitoring sites were selected using a custom GRTS draw conducted by USGS, within monitoring areas associated with select NWRS stations. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA), Necedah (WI) NWRs and several locations near the town of Lamoni, Iowa and private lands in northern Missouri.

The Healthcare Descriptive Analytics market is booming, projected to reach $18.36M in 2025 with a 23.5% CAGR. Discover key trends, drivers, and leading companies shaping this rapidly evolving sector, including software, cloud solutions and applications in clinical, financial and research areas. Explore regional market share data for North America, Europe and more. Recent developments include: In November 2022, Ursa Health updated Ursa Studio, its healthcare analytics development platform, to help organizations meet the requirements of the Centers for Medicare and Medicaid Services (CMS)., In November 2022, Hartford HealthCare entered a long-term partnership with Google Cloud to advance the healthcare digital transformation, improve data analytics, and enhance care delivery and access.. Key drivers for this market are: Need for Comprehensive Analytics, Integration of Big Data into Healthcare. Potential restraints include: Data Privacy and Security Concerns. Notable trends are: Cloud-based Segment Expected to Hold a Significant Share of the Market During the Forecast Period.

This dataset contains de-identified data and analysis code from a study using the 20-item Short Form Health Survey (SF-20). The data were collected to evaluate the validity and reliability of the SF-20 instrument. Included files: - De-identified data (sf20.sas7bdat) - Analysis code (iscience_code.sas) Variables in the dataset correspond to survey domains such as physical functioning, mental health, general health, pain, role functioning, and social functioning. The analysis code includes scripts for descriptive statistics, internal consistency, and test-retest reliability. All information is de-identified to protect participant privacy. Further details can be found in the accompanying README file and manuscript.

Note: n: The sample sizes for cognition, demand, and use. N/A: not applicable. (1). Descriptive statistics, (2). t-test, (3). ANOVA, (4). Chi-squared test, (5). Nonparametric test, (6). Correlation and regression, (7). Statistical graphs and tables, (8). Statistical design, (9). Multiple ANOVA, (10). Analysis of covariance, (11). Multiple linear regression, (12). Logistic regression, (13). Survival analysis, (14). Discriminant analysis, (15). Clustering analysis, (16). Principal components analysis and Factor analysis (PCA & FA),(17). SPSS, (18). SAS, (19). Overall cognition of and demand for medical statistics, (20). Overall cognition of and demand for software.Basic Demographic Characteristics of the Included Studies.

This item has been replaced by the one which can be found at https://doi.org/10.7488/ds/3411 ## GWAS summary statistics from 7,491 critically ill patients from COVID-19 and 48,400 population controls: European(EUR) 5,989/42,891; South Asian(SAS) 788/3,793; African(AFR) 440/1,350; East Asian(EAS) 274/366. GWAS models were calculated with SAIGE using a logisitic mixed-model regression. A trans-ancestry meta-analysis was performed using inverse-variant weighted fixed-effects. Ancestry-specific and trans-ancestry summary statistics are available. TWAS was performed using GTEx v8 gene expression data for lung and blood and an all-tissue meta-analysis. Summary statistics for tissue-specific and meta-TWAS are available.

The Data Analysis Services market is experiencing robust growth, driven by the exponential increase in data volume and the rising demand for data-driven decision-making across various industries. The market, estimated at $150 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an impressive $450 billion by 2033. This expansion is fueled by several key factors, including the increasing adoption of cloud-based analytics platforms, the growing need for advanced analytics techniques like machine learning and AI, and the rising focus on data security and compliance. The market is segmented by service type (e.g., predictive analytics, descriptive analytics, prescriptive analytics), industry vertical (e.g., healthcare, finance, retail), and deployment model (cloud, on-premise). Key players like IBM, Accenture, Microsoft, and SAS Institute are investing heavily in research and development, expanding their service portfolios, and pursuing strategic partnerships to maintain their market leadership. The competitive landscape is characterized by both large established players and emerging niche providers offering specialized solutions. The market's growth trajectory is influenced by various trends, including the increasing adoption of big data technologies, the growing prevalence of self-service analytics tools empowering business users, and the rise of specialized data analysis service providers catering to specific industry needs. However, certain restraints, such as the lack of skilled data analysts, data security concerns, and the high cost of implementation and maintenance of advanced analytics solutions, could potentially hinder market growth. Addressing these challenges through investments in data literacy programs, enhanced security measures, and flexible pricing models will be crucial for sustaining the market's momentum and unlocking its full potential. Overall, the Data Analysis Services market presents a significant opportunity for companies offering innovative solutions and expertise in this rapidly evolving landscape.

Abstract (en): The main objectives of this data collection effort were to assemble a set of cross-nationally comparable microdata samples for Economic Commission for Europe (ECE) countries based on the 1990 national population and housing censuses in countries of Europe and North America, and to use these samples to study the social and economic conditions of older persons. The samples are designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. Included in the Czech Republic dataset are questions on the type and characteristics of buildings/dwellings, available utility systems, and demographic information such as age, sex, marital status, number of children, education, income, religion, and occupation. Also included are questions concerning the presence of household amenities such as telephones, toilets, automobiles, baths/showers, washers, and television sets. All persons and housing units in the Czech Republic. Individual-based sample of 1,029,471 persons with progressive oversampling with age, while retaining information on all persons co-residing in the sampled person's dwelling unit (N = 1,574,936). 2013-09-27 This study was previously distributed on CD-ROM only. The contents of the CD-ROM are now available for public download from ICPSR as a zipped package.2008-09-24 The confidentiality agreement is now available as a downloadable PDF document. Funding insitution(s): United Nations Population Fund. United Nations Economic Commission for Europe. United States Department of Health and Human Services. National Institutes of Health. National Institute on Aging. In addition to the SAS data file provided by the principal investigator, ICPSR is distributing an ASCII data file extracted from the SAS file. Analysis of the ASCII file may be facilitated by dividing it.Erroneously coded missing values on age have been corrected, resulting in 1,650 households being dropped from the sample. The principal investigator has provided a corrected version of the data, in one file instead of four, a revised codebook, descriptive statistics, and SAS and SPSS data definition statements.

The GAPs Data Repository provides a comprehensive overview of available qualitative and quantitative data on national return regimes, now accessible through an advanced web interface at https://data.returnmigration.eu/. This updated guideline outlines the complete process, starting from the initial data collection for the return migration data repository to the development of a comprehensive web-based platform. Through iterative development, participatory approaches, and rigorous quality checks, we have ensured a systematic representation of return migration data at both national and comparative levels. The Repository organizes data into five main categories, covering diverse aspects and offering a holistic view of return regimes: country profiles, legislation, infrastructure, international cooperation, and descriptive statistics. These categories, further divided into subcategories, are based on insights from a literature review, existing datasets, and empirical data collection from 14 countries. The selection of categories prioritizes relevance for understanding return and readmission policies and practices, data accessibility, reliability, clarity, and comparability. Raw data is meticulously collected by the national experts. The transition to a web-based interface builds upon the Repository’s original structure, which was initially developed using REDCap (Research Electronic Data Capture). It is a secure web application for building and managing online surveys and databases.The REDCAP ensures systematic data entries and store them on Uppsala University’s servers while significantly improving accessibility and usability as well as data security. It also enables users to export any or all data from the Project when granted full data export privileges. Data can be exported in various ways and formats, including Microsoft Excel, SAS, Stata, R, or SPSS for analysis. At this stage, the Data Repository design team also converted tailored records of available data into public reports accessible to anyone with a unique URL, without the need to log in to REDCap or obtain permission to access the GAPs Project Data Repository. Public reports can be used to share information with stakeholders or external partners without granting them access to the Project or requiring them to set up a personal account. Currently, all public report links inserted in this report are also available on the Repository’s webpage, allowing users to export original data. This report also includes a detailed codebook to help users understand the structure, variables, and methodologies used in data collection and organization. This addition ensures transparency and provides a comprehensive framework for researchers and practitioners to effectively interpret the data. The GAPs Data Repository is committed to providing accessible, well-organized, and reliable data by moving to a centralized web platform and incorporating advanced visuals. This Repository aims to contribute inputs for research, policy analysis, and evidence-based decision-making in the return and readmission field. Explore the GAPs Data Repository at https://data.returnmigration.eu/.

Mortality rates were calculated as defined in the text.Summary statistics Black cervical cancer mortality by year in thirteen U.S. states.

The Life Science Analytics market is booming, projected to reach $57.73 billion by 2033 with a 10.2% CAGR. Discover key trends, drivers, and leading companies shaping this data-driven revolution in healthcare and pharmaceuticals. Explore market segmentation by application and region for insightful analysis.

Existing vital health statistics registries in India have been unable to provide reliable estimates of maternal and newborn mortality and morbidity, and region-specific health estimates are essential to the planning and monitoring of health interventions. This study was designed to assess baseline rates as the precursor to a community-based cluster randomized control trial (cRCT)–Community Level Interventions for Pre-eclampsia (CLIP) Trial (NCT01911494; CTRI/2014/01/004352). The objective was to describe baseline demographics and health outcomes prior to initiation of the CLIP trial and to improve knowledge of population-level health, in particular of maternal and neonatal outcomes related to hypertensive disorders of pregnancy, in northern districts the state of Karnataka, India. The prospective population-based survey was conducted in eight clusters in Belgaum and Bagalkot districts in Karnataka State from 2013–2014. Data collection was undertaken by adapting the Maternal and Newborn Health registry platform, developed by the Global Network for Women’s and Child Health Studies. Descriptive statistics were completed using SAS and R. During the period of 2013–2014, prospective data was collected on 5,469 pregnant women with an average age of 23.2 (+/-3.3) years. Delivery outcomes were collected from 5,448 completed pregnancies. A majority of the women reported institutional deliveries (96.0%), largely attended by skilled birth attendants. The maternal mortality ratio of 103 (per 100,000 livebirths) was observed during this study, neonatal mortality ratio was 25 per 1,000 livebirths, and perinatal mortality ratio was 50 per 1,000 livebirths. Despite a high number of institutional deliveries, rates of stillbirth were 2.86%. Early enrollment and close follow-up and monitoring procedures established by the Maternal and Newborn Health registry allowed for negligible lost to follow-up. This population-level study provides regional rates of maternal and newborn health in Belgaum and Bagalkot in Karnataka over 2013–14. The mortality ratios and morbidity information can be used in planning interventions and monitoring indicators of effectiveness to inform policy and practice. Comprehensive regional epidemiologic data, such as that provided here, is essential to gauge improvements and challenges in maternal health, as well as track disparities found in rural areas.

Mortality rates were calculated as defined in the text.Summary statistics White cervical cancer mortality by year in thirteen U.S. states.

Unlock explosive growth opportunities in the booming Operational Analytics market! Our comprehensive analysis reveals key trends, drivers, and restraints influencing this multi-billion dollar industry. Discover market size projections, regional breakdowns, and competitive landscapes to make informed strategic decisions. Learn more about predictive, prescriptive, and descriptive analytics solutions.

BackgroundDespite improvements in treatment for rheumatoid arthritis (RA), psoriatic arthritis (PsA) and spondyloarthritis (axSpA), several key unmet needs remain, such as fatigue. The objective of this study was to describe the severity of fatigue, disease characteristics and socioeconomic factors in people with RA, PsA and axSpA.MethodsThe study was designed as a cross-sectional survey collecting patient characteristics such as disease characteristics, socioeconomic factors and fatigue in people with RA, PsA and axSpA in Denmark. Respondents were consecutively recruited for the study over a six-month period in 2018 via routine visits to outpatient rheumatology clinics. Study nurses collected information on diagnosis, current disease-related treatment and disease activity from medical journals. People were invited to complete a questionnaire related to socioeconomic factors and containing the FACIT-Fatigue subscale. Descriptive statistics were analyzed using SAS.ResultsWe invited 633 people to participate, and 488 (77%) completed the questionnaire. Women constituted 62% of respondents, and the mean age was 53.5 years. Respondents had on average been diagnosed between 11 and 15 years ago. Overall, 79% had no changes to their disease-related treatment during the past year, and the average disease activity as indicated by DAS28 for RA and PsA was 2.48 and 2.36, respectively, and BASDAI for axSpA was 28.40. Fatigue was present in all three diagnoses (mean: 34.31). The mean fatigue score varied from respondents answering that they suffered from no or little fatigue (mean: 45.48) to extreme fatigue (mean: 10.11). Analyses demonstrated that the respondents were not considerably different from nonrespondents, and the study population is considered representative compared with Danish RA and axSpA patients in the Danish National Rheumatology Registry, the DANBIO database.ConclusionWe found that the majority of the study population were fatigued (61%). They had low disease activity and few disease-related treatment changes.