SAS JMP Statistical Discovery Clinical 18.0 Win x64

New in JMP 18

New in JMP
Configurable Data Connectors
JMP gives you easy access to your data from a variety of sources. With the additional data connectors now available with JMP 18, even more data is available to enrich your analyses. These data connectors are configurable, for more options to customize the details of your connections.
- Use customizable built-in configurations for various database systems with examples to reference in the scripting index.
- Import data using the streamlined PI Server Import. Multiple attributes can be easily imported into a single data table, multiple rows of data within a given time interval can be collapsed into a single row, and data can be sampled at a specified interval.
- Customize the details of how JMP connects with a data source using configurable data connectors that support ODBC connections.
- Easily share connectors will colleagues to facilitate their work.
- Publish and refresh data to JMP Live using configurations that are compatible with both Mac and Windows
Platform Presets
When you have customized a report to your liking (including colors, axis settings, red triangle options), you can capture those customizations for use with future launches of the same platform. JMP includes a set of popular platform presets, or you can save your own preferred configuration as a preset for any data table or set of columns.
- Save often-used report output customizations for efficient analysis and sharing.
- Create presentation- or publication-ready outputs to share with colleagues.
- Provide guidance on how to perform common analyses in reports using presets that have the correct red triangle options pre-selected.
- Assist new users in setting up platforms used frequently in your organization.
Model Profiler Enhancements
The Prediction Profiler is a staple in the analytic workflow for many data analysts, allowing practical, interactive model exploration. Now you have more options for using profilers to understand your predictive models.
- Show prediction intervals for predicted (individual) values, as well as confidence intervals for predicted means.
- Show data points and overlaid interaction traces on profilers.
- Save prediction and interval formulas in one step in model fitting platforms. This feature makes it easier to Description confidence and prediction intervals using Graph >Profiler.
- Use the simpler, easier launch dialog in all profiler platforms under the Graph menu.
- View and update constraints in the profiler more easily.
- Use K Nearest Neighbors-based extrapolation control.
- Publish profilers with prediction intervals and interactive profilers for more model types in JMP Live
New in JMP Pro
Tackle even more complex and challenging analytic problems using the latest data science techniques in JMP Pro, including advanced predictive modeling and machine learning with extended analytic capabilities.
- New methods for Direct FPCA in FDE, including Multivariate Curve Resolution and Penalized SVD. Peak detection is also available for chromatography and spectrometry profiles.
- Enhanced GLMM is now more full-featured, supporting additional distributions and covariance structures.
- More Genomics capabilities, a new marker relatedness platform, and improvements to marker statistics, marker simulation, and multivariate embedding (UMAP/t-SNE) platforms.
- Models that include random effects can now be specified in Fit Response Screening.
- Enhancements to SEM including new model shortcuts, platform-wide bootstrapping, and robust inference.
Improved Python Integration
For data analysts, scientists, and engineers using Python, the new JMP integration simplifies the programming to keep the focus on the analysis. JMP 18 includes a single, supported version of Python, an extensive set of standard Python libraries, a more robust connection between JMP and Python data structures, and an improved Python coding experience within JMP.
- Run the integrated Python version in JMP using the new Python editor for a simple but familiar Python programming experience.
- Access to JMP data tables in memory from Python directly; easy access to Python data from JMP.
- Run JSL from Python.
- Continue using JMP with other software, such as SAS, R, and MATLAB, using openly available Python packages.
New in JMP Clinical
An enhanced suite of tools in JMP Clinical 18 gives you expanded options for interactively exploring your clinical trial data, more easily filtering, and quickly screening for safety issues.
- New comprehensive Drug-Induced Liver Injury (DILI) report based on FDA guidelines for identifying subjects in a trial that have possible liver injury.
- Clinical Data Mapping Tool for converting raw data to STDM data standards.
- Redeveloped Risk-Based Monitoring report with updated visualization of the risk metric scores
- A GUI-driven Subpopulation Builder to specify search and filter criteria to identify subpopulations of trial subjects that meet ad hoc or predefined criteria.
- Clinical Recoder to recode study variables and to allow combining or splitting levels for a variable.
Additional Items
- Loading JMP add-ins in JMP sessions.
- Support for multiple local data filters.
- Options for making report changes with red triangle menus in distributions.
- New confidence intervals.
- Enhanced pattern filling and data/time patterns.
- Improved authentication for data access.
- History for reports or all content in a space.
- Directory access for refresh scripts.
- Support for a private key in Snowflake authentication.

JMP Clinicaloffers tools to make it easy for key participants in the trial review process to explore trends and outliers, detect hidden data, identify safety and efficacy issues, and perform medical monitoring, data integrity validation, and statistical analyses. Summary dashboards in JMP Clinical enable medical reviewers to evaluate safety and efficacy issues with the click of a button. With JMP Clinical, medical writers can automate patient profiles and patient narratives to reduce the time and complexity of creating output for review and submission to regulatory agencies. The goal of clinical operations is to mitigate data quality risks that could hinder a regulatory submission or drug approval. Risk-based monitoring tools in JMP Clinical help you identify data anomalies at the vendor, monitor, site, and country level to determine the factors responsible for lapses in safety or data quality.
JMP Clinical
JMP is a business unit of SASthat produces interactive software for desktop statistical discovery. John Sall, SAS co-founder and Executive Vice President, created this dynamic software and remains its chief architect and leader of the JMP division. The SAS commitment to accuracy, value and quality is evident in every JMP software release. Training, technical support, licensing and distribution are provided to JMP users the world over through SAS. Teams specializing in JMP products are located in the United States, Europe, China and Japan.