No description was included in this Dataset collected from the OSF

This dataset includes all of the underlying data for our study, published in JAMA Internal Medicine (JAMA Intern Med. 2017;177(10):1452-1459. doi:10.1001/jamainternmed.2017.3820), along with our extraction sheets and work files.

Statistical Analysis Software Market size was valued at USD 7,963.44 Million in 2023 and is projected to reach USD 13,023.63 Million by 2030, growing at a CAGR of 7.28% during the forecast period 2024-2030.

Global Statistical Analysis Software Market Drivers

The market drivers for the Statistical Analysis Software Market can be influenced by various factors. These may include:

Growing Data Complexity and Volume: The demand for sophisticated statistical analysis tools has been fueled by the exponential rise in data volume and complexity across a range of industries. Robust software solutions are necessary for organizations to evaluate and extract significant insights from huge datasets.
Growing Adoption of Data-Driven Decision-Making: Businesses are adopting a data-driven approach to decision-making at a faster rate. Utilizing statistical analysis tools, companies can extract meaningful insights from data to improve operational effectiveness and strategic planning.
Developments in Analytics and Machine Learning: As these fields continue to progress, statistical analysis software is now capable of more. These tools’ increasing popularity can be attributed to features like sophisticated modeling and predictive analytics.
A greater emphasis is being placed on business intelligence: Analytics and business intelligence are now essential components of corporate strategy. In order to provide business intelligence tools for studying trends, patterns, and performance measures, statistical analysis software is essential.
Increasing Need in Life Sciences and Healthcare: Large volumes of data are produced by the life sciences and healthcare sectors, necessitating complex statistical analysis. The need for data-driven insights in clinical trials, medical research, and healthcare administration is driving the market for statistical analysis software.
Growth of Retail and E-Commerce: The retail and e-commerce industries use statistical analytic tools for inventory optimization, demand forecasting, and customer behavior analysis. The need for analytics tools is fueled in part by the expansion of online retail and data-driven marketing techniques.
Government Regulations and Initiatives: Statistical analysis is frequently required for regulatory reporting and compliance with government initiatives, particularly in the healthcare and finance sectors. In these regulated industries, statistical analysis software uptake is driven by this.
Big Data Analytics’s Emergence: As big data analytics has grown in popularity, there has been a demand for advanced tools that can handle and analyze enormous datasets effectively. Software for statistical analysis is essential for deriving valuable conclusions from large amounts of data.
Demand for Real-Time Analytics: In order to make deft judgments fast, there is a growing need for real-time analytics. Many different businesses have a significant demand for statistical analysis software that provides real-time data processing and analysis capabilities.
Growing Awareness and Education: As more people become aware of the advantages of using statistical analysis in decision-making, its use has expanded across a range of academic and research institutions. The market for statistical analysis software is influenced by the academic sector.
Trends in Remote Work: As more people around the world work from home, they are depending more on digital tools and analytics to collaborate and make decisions. Software for statistical analysis makes it possible for distant teams to efficiently examine data and exchange findings.

Objective: To explore the structure and content of a non-random sample of clinical study reports (CSRs) to guide clinicians and systematic reviewers. Search strategy: We searched public sources and lodged Freedom of Information requests for previously confidential CSRs primarily written by industry for regulators. Selection criteria: CSR reporting sufficient information for extraction ("adequate"). Primary outcome measures: Presence and length of essential elements of trial design and reporting and compression factor (ratio of page length for CSR compared to its published counterpart in a scientific journal). Data extraction: data were extracted on standard forms and cross-checked for accuracy. Results: We assembled a population of 78 CSRs (covering 90 RCTs; 144,610 pages total) dated 1991-2011 of 14 pharmaceuticals. Report synopses had a median length of 5 pages, efficacy evaluation 13.5 pages, safety evaluation 17 pages, attached tables 337 pages, trial protocol 62 pages, statistical analysis plan 15 pages, and individual efficacy and safety listings had a median length of 447 and 109.5 pages, respectively. While 16 (21%) of CSRs contained completed case report forms, these were accessible to us in only one case (765 pages representing 16 individuals). Compression factors ranged between 1 and 8805. Conclusions: Clinical study reports represent a hitherto mostly hidden and untapped source of detailed and exhaustive data on each trial. They should be consulted by independent parties interested in a detailed record of a clinical trial, and should form the basic unit for evidence synthesis as their use is likely to minimize the problem of reporting bias. We cannot say whether our sample is representative and whether our conclusions are generalizable to an undefined and undefineable population of CSRs.

Percentage of participants rating items as critical and as not important.

Introduction: Recruitment is complete in the fourth INTEnsive ambulance-delivered blood pressure Reduction in hyper-ACute stroke Trial (INTERACT4) is a multicenter, prospective, randomized, open-label, blinded endpoint assessed trial of pre-hospital blood pressure (BP) lowering initiated in the ambulance for patients with a suspected acute stroke and elevated BP in China. According to the registered and published trial protocol and developed by the blinded trial Steering Committee and Operations team, this manuscript outlines a detailed statistical analysis plan for the trial prior to database lock. Methods: Patients were randomized (1:1) to intensive (target systolic BP [SBP] 130-140 mmHg within 30 minutes) or guideline-recommended BP management (BP lowering only considered if SBP >220 mmHg) group. Primary outcome is an ordinal analysis of the full range of scores on the modified Rankin scale at 90 days. A modified sample size of 2320 was estimated to provide 90% power to detect a 22% reduction in the odds (common odds ratio of 0.78) of a worse functional outcome using ordinal logistic regression, on the assumption of 5% patients with missing outcome and 6% patients with a stroke mimic. Conclusion: The statistical analysis plan for the trial has been developed to ensure transparent, verifiable, and prespecified analysis, and to avoid potential bias in the evaluation of the trial intervention.

Clinical Trial Management System Market size was valued at USD 1009.73 Million in 2023 and is projected to reach USD 2310.23 Million by 2031, growing at a CAGR of 10.90% from 2024 to 2031.

Clinical Trial Management Market: Definition/ Overview

Clinical trials are meticulously overseen through a process known as clinical trial management. This process ensures the safety and efficacy of new medical interventions in human subjects by planning, organizing, conducting, monitoring, analyzing, and reporting on clinical trials. Strict regulations govern the entire process to safeguard participants and guarantee the integrity of the collected data. The clinical trial management journey begins with pre-trial activities. A detailed protocol outlining the study design, participant selection criteria, procedures, data collection methods, and statistical analysis plan is first developed. This protocol is then submitted to regulatory bodies for review and approval. Research sites with qualified investigators and staff are identified and trained on the protocol once approval is granted. Additionally, financial resources required for the trial are estimated and contracts are established with involved parties.

aThis table summarizes data presented in Tables 4 and 5 and Text S1's table 1. Along the top row in this table, we show every type of efficacy analysis described in the protocols, SAPs, and publications across all nine trials for which we compared these documents. The first column on the left lists the criteria used to define the types of analysis across all studies. For each type of analysis, an “X” denotes that the criterion was applied in at least one of the documents for any of the nine trials we examined. For example, the second column summarizes the five criteria used across all documents and trials to define ITT: in Table 4, four criteria were used in different combinations to define ITT analysis; in Text S1's table 1, one additional criterion was used in the SAP.bThis type of analysis was specified protocols, SAPs, and publications for the trials we examined (Tables 4 and 5 and Text S1's table 1).cThis type of analysis was specified only in the protocol and publications for some of the trials we examined (see Table 5).dThis type of analysis specified only in SAPs for some of the trials we examined (see Text S1's table 1).CGIS, clinical global impression of severity; HAM-D, Hamilton Depression Rating Scale; YMRS, Young Mania Rating Scale.

No description was included in this Dataset collected from the OSF

Pre-analysis plan for an experimental study on the effect of anger on persuasion within interpersonal political discussions.

This zip file contains files of data to support FHWA-JPO-16-370, Analysis, Modeling, and Simulation (AMS) Testbed Development and Evaluation to Support Dynamic Mobility Applications (DMA) and Active Transportation and Demand Management (ATDM) Programs - San Mateo Testbed Analysis Plan : Final Report. Zip size is 1.5 GB. The files have been uploaded as-is; no further documentation was supplied by NTL. All located .docx files were copied to .pdf document files which are an archival format. These .pdfs were then added to the zip file alongside the original .docx files. The attached zip files can be unzipped using any zip compression/decompression software. These zip file contains files in the following formats: .pdf document files which can be read using any pdf reader; .docx document files which may be opened with Microsoft Word or some other open source document editors; .xlsx spreadsheet files which may be opened with Microsoft Excel or some other open source spreadsheet editors; .syn files are a proprietary file format for signal timing plans which are provided in the Synchro Model given as “El Camino Real Synchro.syn” and can be opened using Trafficware Synchro, which may require users to purchase a license or software (for more information go to http://www.trafficware.com/); .csv data files, an open format, which may be opened with any text editor or in many spreadsheet applications; .db generic database files, often associated with thumbnail images in the Windows operating environment; .rbc files, which are scripts written in Rembo-C, which can be opened in a text editor, but require a server with Rembo installed to run the scripts; .vap audio files which will require special audio editing software to manipulate; .dll dynamically linked files for Windows program operations; .layx, a file type on which we could not locate reliable information; and .inpx files, a file type on which we could not locate reliable information [software requirements]. These files were last accessed in 2017. Files were accessed in 2017. Data will be preserved as is.

The public sharing of primary research datasets potentially benefits the research community but is not yet common practice. In this pilot study, we analyzed whether data sharing frequency was associated with funder and publisher requirements, journal impact factor, or investigator experience and impact. Across 397 recent biomedical microarray studies, we found investigators were more likely to publicly share their raw dataset when their study was published in a high-impact journal and when the first or last authors had high levels of career experience and impact. We estimate the USA's National Institutes of Health (NIH) data sharing policy applied to 19% of the studies in our cohort; being subject to the NIH data sharing plan requirement was not found to correlate with increased data sharing behavior in multivariate logistic regression analysis. Studies published in journals that required a database submission accession number as a condition of publication were more likely to share their data, but this trend was not statistically significant. These early results will inform our ongoing larger analysis, and hopefully contribute to the development of more effective data sharing initiatives. Earlier version presented at ASIS&T and ISSI Pre-Conference: Symposium on Informetrics and Scientometrics 2009

The analysis plan is provided to guide interested readers through the stages of our study. We outline the research methods, statistical tools, and data sources undertaken in our study. All decisions were solidified before analysis work begun.

In accordance with NYC Administrative Code section 19-182.3, the Serious Injury Response, Tracking & Analysis Program (SIRTA) is a program at the New York City Department of Transportation (NYC DOT) that investigates, analyzes and reports on serious vehicular crashes, review street design, infrastructure and driver behavior at each crash location, and makes recommendations for safety street design or infrastructure. This dataset provides information on serious vehicular crashes that were analyzed and reported on as part of SIRTA. For more information, please visit the NYC DOT website: https://www.nyc.gov/html/dot/html/about/dotlibrary.shtml#data

The Gap Analysis Program (GAP) produces data and tools that help meet critical national challenges such as biodiversity conservation, renewable energy development, climate change adaptation, and infrastructure investment. The GAP national land cover includes data on the vegetation and land-use patterns of the United States, including Alaska, Hawaii, and Puerto Rico. This national dataset combines land cover data generated by regional GAP projects with Landscape Fire and Resource Management Planning Tools (LANDFIRE) data (http://www.landfire.gov/). LANDFIRE is an interagency vegetation, fire, and fuel characteristics mapping program, sponsored by the U.S. Department of the Interior and the U.S. Department of Agriculture Forest Service.

Correlations (above diagonal), standard deviations (diagonal) and covariances (below diagonal) of grip strength across waves for males.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

This asset includes environmental justice-related analyses of population located within a mile of Superfund and RCRA Corrective Action sites. It characterizes demographics and socio-economic characteristics of near-site communities as compared to the average U.S. population. It also examined children of up to 17 years of age living within 1 mile of SF and RCRA CA sites where human health protective measures may not have been in place. It compared data on the health status of these children to the status of all children in the U.S. Information from this study contributed to the America's Children and the Environment (ACE) report for 2013.

The KADS Trial is a randomised controlled trial which aims to determine whether a 4-week course of subcutaneous ketamine is an efficacious therapy for treatment-resistant depression.

This registration contains the study's clinical trial protocol, as well as the statistical analysis plan for the primary publication relating to the study's randomised controlled trial phase.

The main registration for this trial is available on the Australian New Zealand Clinical Trials Registry (www.anzctr.org.au; registration number ACTRN12616001096448).

According to a majority of the respondents, the global telecom and banking sectors are expected to launch new data ecosystem initiatives in the upcoming future, while the government and public sector are planning to strengthen existing initiatives during 2021. On an average global scale, 48 percent of respondents throughout all the various sectors indicated that their companies plan to launch new data ecosystem initiatives in the future.