HepTronik’s unique expertise provides customers with the most robust solutions in the industry.
SAS Introduction
In Information and Technology area, managing huge databases and analyzing the data is a challenging task. For managing data, warehouses are created as per the client’s specifications and requirements and variety of software tools are used to process the data.
Statistical Analysis System, known as SAS System, is one of the most widely used, flexible data processing tools. It is used to perform:
- Data entry, retrieval and management
- Report writing and graphics
- Statistical and mathematical analysis
- Business forecasting and decision support
- Operations research and project management
- Application development
The core of the SAS System is base SAS software. It consists of
- SAS language: It is a programming language that you use to manage data
- Procedure that are software tools for data analysis and reporting
- A macro facility
- A windowing environment called the SAS Display Manager System.
Before analyze the data and produce the final report we have to arrange the data in the order (format) that the software will recognize the data for further processing. SAS will recognize the data in the form of data set. SAS data set consists of two parts i.e.
- Descriptor information: This describes the contents of the SAS data set to the SAS System.
- Data values: Data that have been collected or calculated which is organized into a rectangular structure containing rows called observations and columns called variables
SAS language consists of statements. Each SAS statement is terminated by semi-colon (;) When SAS program is executed, log and lst (list or output) files are created by the system.
Log file contains the Error messages, Warnings and Notes. Whenever we run the SAS program, the first step is to open the log file and check for the errors, warnings and notes. This will help us the make the SAS program error free. When the log file is no errors, no warnings, no notes (it displays at the bottom of the log file specifying Warnings 0 Errors 0 Notes 0) then we can confirm that the processing is accurate.
Output file It contains the results of the processing. Base SAS mainly completes with two steps called Data Step and Procedure Step.
Data Step It is used to create data sets. Proc Step is used to execute the pre-defined procedures that are used for processing.
We can start SAS session with the SAS command. SAS system can be used in different environments like DOS, Windows, NT, Unix, MVS, VMS etc.
SAS programs can be run in the following methods:
- Display Manager Mode: This method is used in windowing environment. We can edit and execute programming statement, display the SAS log and output windows.
- Interactive Line Mode: In this mode, program statements are entered in sequence in response to prompts from the SAS system.
- Non-interactive mode: SAS program statements are stored in an external file and executes immediately.
- Batch mode: we can run SAS jobs in batch mode under host systems batch or background executive.
This is the main part to create a data or to describe the data that SAS system recognizes for processing. A Data step is a group of SAS language statements that begins with DATA statement and followed by programming statements that perform the manipulations necessary to build the data sets. Report writing, file management and information retrieval can all handled in the Data Step.
We can submit the Data step to the SAS system for execution. SAS System first compiles and then execute.
- SAS system checks the syntax of the SAS statements and compiles them, while compile, it translates the statements into machine code.
- DATA statement dataset name (begins step)
- Input or Set, Merge or Update (reads a record from input data)
- Optional SAS programming statements (further process the data)
- Run (end of the data step)
- Sample DATA Step: Data weight; Input rollno sub1 sub2 sub3 ; Total=sum(sub1,sub2,sub3); Datalines; 1001 90 80 75 1003 80 85 87 1004 90 95 90 ;
- Automotive
- Banking
- Financial Services
- Government & Education
- Healthcare
- LifeSciences
- Manufacturing
- Media & Entertainment
- Pharmaceutical***
- Retail
- Telecommunication
- FDA’s most preferred tool for Clinical trials for Phase I, II, III, IV etc. Only validated tool, which create regular opportunities for SAS, trained consultants.
- All major pharmaceutical companies USE SAS as the analysis tool for clinical research.
- On an average, a drug takes 12 –13 years to reach the market, therefore utilizations of SAS in all phase creates great opportunities for SAS trained consultants.
- On an average 6000-8000 clinical trials are conducted every year. Great Demand for SAS trained Consultants.
- Always there is a demand for the SAS consultants all through the year and years to come.
- Once trained, very easy to be adopted for different industries needs
Each training session will be from 6 to 8 hours based on the topic and discussion.
- Introduction to Healthcare Industry
- Introduction to Pharmaceutical Industry
- Concepts of Pharmacokinetic / Pharmacodynamic
- Design and development of CRF-Case Report Forms
- Fundamentals of Statistics related to SAS Tools and Technology
- Introduction to SAS Analysis tool and its applications
- Organizational Structure of Clinical Research Development
- Creating Analysis Datasets and Workshop
- SAS Functions and Workshop
- SAS Statements and Workshop
- SAS Procedure and Workshop
- SAS SQL and Workshop
- SAS Macros and Workshop
- SAS Import, Export and Workshop
- Analytical technique of SAS Graphs and Workshop
- Mock Interview Practice – optional
Clinical Data Management(CDM) and Safety Risk Assessment(SRA)
Heptronik is providing training on Clinical Data Management(CDM) with hands-on experience with State-of-art EDC/RDC application, Safety Risk Assessment/Management.
- Pharmaceutical Industry Overview
- Clinical Data Management process
- CRF design and Common Data sets
- Managing Lab Data/PK data
- Validation/data Import/Export/Reporting
- Database Lock/Freeze
- EDC an RDC experience
- SAS programming basics to perform edit check/validation
- Pharmaceutical Industry Overview
- Post-marketing safety analysis
- Clinical Trial Safety
- 360 degree analysis of AE/SAE/Lab/ECG/Dose/Conmeds/Medical/Surgical dataset
- Developing analysis reports
JMP Training
Heptronik is providing training on JMP with hands-on experience. This course introduces JMP, its philosophy, and its extensive graphical data exploration capabilities. Consultants learn to perform elementary exploratory data analysis (EDA) and discover natural patterns in data. Consultants use JMP to learn to navigate through menus, dialogs, and results; manage data; save and present results in various formats; use decision trees in the EDA context; and obtain and interpret descriptive statistics.
Overview of the Course Contents:
- Exploring user-assistance options
- Opening different types of files in JMP
- Understanding modeling type
- Introducing the Table panel, Columns panel, and Rows panel
- Using the Columns and Rows menus
- Creating new columns/tables
- Using the Tabulate option to create summary tables
- Exploring relationships between continuous columns
- Exploring relationships between attribute data columns
- Using recursive partitioning for exploratory analysis
- Saving results
- Exploring relationships between attribute data columns
Instructor Profile
- 12+ years of experience in healthcare with 6+ years in pharmaceuticals industry.
- Experience in statistical reporting for new drug application (NDA) submission support in Neuroscience, Oncology and Infectious Diseases, Cardiovascular, GI.
- Experience with clinical protocols development (CPD), PK/PD analysis, Designing (CRF) case report forms, SAP (Statistical Analysis Plan) and clinical trial data management (CDM) databases.
- Experience, in developing secondary databases for Ad-hoc report creation, interim analysis, responsible for creating mockup table and listing, generated tables and listings, cleaned the database and developed specification & performed edit/quality checks.
- Participate in all stages of clinical trail study team review meetings including statistical review, presentation, publication and SOP trainings.
- 9+ years of experience in Information Technology and healthcare domain(Insurance and Health Economics).
- Over 6+ years of experience as SAS® Programmer, Data manipulation, Statistical Analysis and Data Warehousing.
- Profound Knowledge and Strong experience in Base SAS, SAS/MACRO, SAS/ODS, SAS/GRAPH, SAS/SQL.
- Strong experience in SAS SHARE, SAS/IntrNet, SAS/STAT, SAS/Connect.
- Thorough knowledge of ORACLE 8i, MS-Access, SQL Server 2000, DB2 and SQL.
- Good experience in IBM Mainframes: MVS/JCL, TSO/ISPF.
- Proficient in writing Perl, Perl/CGI and UNIX Shell Scripts (KORN, BOURNE).
- Excellent skills in Web Technologies like ASP, Java Script, XML, XSLT, CSS and HTML.
- Possess experience in SAS® on Mainframes, UNIX and Microsoft Windows environments.
- Profound concepts and extensive programming experience in Java, C, C++ and Perl.
Resources
- FDA Homepage: Links or all FDA offices. (FDA.org)
- FDA CBER:Describes recent biological approvals
- FDA CADER:Describes new drug approvals, NDA review process, links to CDER’s gopher, which provides access to a variety of directories, including policies and procedures, DMFs, Human Drug CGMP notes, approved drug products, and FDA phone/fax numbers
- GPO Access: The Government printing offices home page
- NIH Small Business Funding Opportunities
- National Center for Biotechnology Information (NCBI)
- DIA: Drug Information Association
- Regulatory Affairs Professional Society (RAPS) Home page:
- BIO online:The Biotechnology Industry Organization
- GPO Access: The Government printing offices home page
- AAPS The American Association of Pharmaceutical Scientists
- DIA: Drug Information Association
- IDRAC: The world’s most comprehensive pharmaceutical regulatory affairs database
- HIS Health information: HIS Health Information offers the most comprehensive set of pharmaceutical and medical device manufacturing regulation and standards available from one source
- BioSpace.com: The Global Hubsite for Life Sciences
- BioSpaceIndustry Links:
- BioWorld.Online:The worldwide Biotechnology News and Information Source
- Medscape Molecular Medicine:
- Pharmatech’s Home page
- Pharmaceuticals Education & Research Institute
- Promega’s Home Page
- CLIA:Clinical Laboratory Improvement Amendments
- MMA:Medical Marketing Association (Devices)
- AAD:American Academy of Dermatology
- AHA:American Heart Association
- ACS:American Cancer Society
- ASGT:American Society for Gene Therapy
- ASCO:American Society of Clinical Oncology
- CFF:Cystic Fibrosis Foundation
Comprehensive R Programming
This detailed course is designed to introduce participants to the R programming language and its application in data analysis and statistical computing. Through a series of lectures, hands-on coding exercises, and real-world projects, attendees will gain a robust understanding of R’s capabilities and practical uses.
By the end of this course, participants will be able to:
- Understand the fundamentals of R programming.
- Perform data manipulation and transformation using R.
- Create and interpret data visualizations.
- Conduct statistical analyses and apply various models.
- Develop R scripts and functions for reproducible analysis.
- Utilize R for real-world data analysis projects.
- Aspiring Data Scientists
- Statisticians
- Data Analysts
- Researchers
- Students and professionals interested in data science and analytics.
Prerequisites:
- Basic understanding of statistics.
- Familiarity with programming concepts (though no prior programming experience in R is required).
Day 1: Introduction to R and RStudio
Introduction to R:
Basic R Syntax:
Introduction to R:
- Overview of R and its applications
- Installing R and RStudio
- Navigating the RStudio interface
Basic R Syntax:
- R as a calculator
- Variables and assignment
- Data types and structures (vectors, matrices, lists, and data frames)
Working with Vectors:
Data Frames and Lists:
Matrices and Arrays:
- Creating and manipulating vectors
- Vectorized operations
Data Frames and Lists:
- Creating and accessing data frames
- Basic data frame operations
- Working with lists and list operations
Matrices and Arrays:
- Creating matrices and arrays
- Basic operations with matrices and arrays
Introduction to dplyr:
Data Manipulation Verbs:
Pipes in R:
- Installing and loading dplyr
- Understanding the grammar of data manipulation
Data Manipulation Verbs:
- Select, filter, arrange, mutate, and summarize
- Grouping data with group_by
Pipes in R:
- Introduction to the pipe operator (%>%)
- Using pipes for cleaner and more readable code
Reading and Writing Data:
Web Data and APIs:
- Importing data from CSV, Excel, and other formats
- Writing data to files
- Using readr and readxl packages
Web Data and APIs:
- Scraping data from the web
- Accessing APIs and JSON data
Introduction to ggplot2:
Creating Visualizations:
Advanced Visualization Techniques:
- Understanding the grammar of graphics
- Basic plotting functions
Creating Visualizations:
- Bar charts, histograms, box plots, and scatter plots
- Customizing plots (themes, labels, and colors)
Advanced Visualization Techniques:
- Faceting, annotations, and plot overlays
- Saving and exporting plots
Descriptive Statistics:
Inferential Statistics:
- Summary statistics and data exploration
- Frequency tables and cross-tabulations
Inferential Statistics:
- Hypothesis testing (t-tests, chi-squared tests) o Correlation and regression analysis
Time Series Analysis:
Cluster Analysis and PCA:
Advanced Modeling:
- Working with time series data
- Basic time series modeling and forecasting
Cluster Analysis and PCA:
- Introduction to clustering algorithms
- Principal Component Analysis (PCA)
Advanced Modeling:
- Linear and logistic regression
- Introduction to machine learning concepts
Creating Functions:
Scripting in R:
- Writing custom functions in R
- Function arguments and return values
Scripting in R:
- Writing and running R scripts
- Best practices for script organization and documentation
Introduction to RMarkdown:
Report Generation:
- Creating dynamic documents
- Embedding R code in RMarkdown
Report Generation:
- Generating HTML, PDF, and Word reports
- Customizing RMarkdown output
Real-world Case Studies:
Capstone Project:
Course Review and Q&A:
- Analyzing datasets from various domains (e.g., finance, healthcare, marketing)
Capstone Project:
- Guided project to apply learned skills
- Creating a comprehensive data analysis report
Course Review and Q&A:
- Recap of key concepts
- Addressing participant questions
- Course feedback and next steps
- Comprehensive course manual
- Access to R and RStudio (installation guidelines provided)
- Sample datasets for practice
- Additional resources (articles, videos, and reference guides)
Certification: Participants will receive a Certificate of Completion upon successfully completing the course and the final project.
Enroll Now: Start your journey into the world of data analysis with R programming. Enroll now to develop essential skills and advance your career in data science!
Mastering TIBCO Spotfire
This comprehensive course is designed to equip participants with the knowledge and skills necessary to harness the full potential of TIBCO Spotfire, a powerful data visualization and analytics tool. Through a blend of theoretical instruction and hands-on practice, attendees will learn to create insightful dashboards, perform advanced data analysis, and effectively communicate data-driven insights.
By the end of this course, participants will be able to:
- Navigate the TIBCO Spotfire interface and understand its core functionalities.
- Connect to various data sources and perform data wrangling.
- Create interactive and visually appealing dashboards and reports.
- Utilize advanced data visualization techniques.
- Implement data analytics and statistical methods within Spotfire.
- Customize and extend Spotfire functionalities through scripting and automation.
- Share insights and collaborate effectively using Spotfire’s sharing capabilities.
- Data Analysts
- Business Intelligence Professionals
- Data Scientists
- IT Professionals
- Anyone looking to enhance their data visualization and analysis skills using TIBCO Spotfire.
- Basic understanding of data analysis and visualization concepts.
- Familiarity with data manipulation and querying.
- Prior experience with other data visualization tools is beneficial but not required.
Day 1: Introduction to Spotfire and Data Preparation
Introduction to TIBCO Spotfire:
Data Connections and Importing:
Data Wrangling:
Introduction to TIBCO Spotfire:
- Overview of Spotfire capabilities and features
- Understanding the Spotfire environment and user interface
Data Connections and Importing:
- Connecting to various data sources (databases, Excel, CSV, etc.)
- Data import techniques and best practices
Data Wrangling:
- Data transformation and cleaning
- Handling missing data and data types
- Introduction to calculated columns and data expressions
Creating Basic Visualizations:
Interactive Dashboards:
Advanced Visualization Techniques:
- Bar charts, line charts, scatter plots, and pie charts
- Customizing visualizations and formatting
Interactive Dashboards:
- Creating and organizing multiple visualizations
- Using filters, markings, and detailed visualizations
Advanced Visualization Techniques:
- Heatmaps, tree maps, and waterfall charts
- Geospatial visualizations and map layers
Data Analytics and Statistical Tools:
Custom Expressions and Calculations:
Scripting and Automation:
- Using Spotfire’s built-in statistical tools
- Time series analysis and forecasting
- Regression and correlation analysis
Custom Expressions and Calculations:
- Writing custom expressions for complex calculations
- Utilizing Spotfire’s expression language for advanced data manipulation
Scripting and Automation:
- Introduction to IronPython scripting in Spotfire
- Automating tasks and workflows
Collaboration Features:
Web Player and Mobile Access:
Security and Permissions:
- Sharing Spotfire analyses and dashboards
- Exporting visualizations and reports
Web Player and Mobile Access:
- Deploying dashboards for web and mobile users
- Ensuring optimal performance and user experience
Security and Permissions:
- Managing user roles and access permissions
- Ensuring data security and compliance
Real-world Case Studies:
Hands-on Projects:
Q&A and Wrap-up:
- Analyzing and visualizing business scenarios using Spotfire
- Best practices and tips from industry examples
Hands-on Projects:
- Guided projects to reinforce learning
- Creating comprehensive dashboards from scratch
Q&A and Wrap-up:
- Addressing participant queries
- Recap of key concepts
- Course feedback and next steps
- Comprehensive course manual
- Access to Spotfire software (trial or licensed version)
- Sample datasets for practice
- Additional resources (articles, videos, and reference guides)
Certification: Participants will receive a Certificate of Completion upon successfully completing the course and the final project.
Enroll Now: Take the first step towards mastering TIBCO Spotfire and unlocking the power of your data. Enroll now and transform your data analysis skills!
SAS Introduction
In Information and Technology area, managing huge databases and analyzing the data is a challenging task. For managing data, warehouses are created as per the client’s specifications and requirements and variety of software tools are used to process the data.
Statistical Analysis System, known as SAS System, is one of the most widely used, flexible data processing tools. It is used to perform:
- Data entry, retrieval and management
- Report writing and graphics
- Statistical and mathematical analysis
- Business forecasting and decision support
- Operations research and project management
- Application development
The core of the SAS System is base SAS software. It consists of
- SAS language: It is a programming language that you use to manage data
- Procedure that are software tools for data analysis and reporting
- A macro facility
- A windowing environment called the SAS Display Manager System.
Before analyze the data and produce the final report we have to arrange the data in the order (format) that the software will recognize the data for further processing. SAS will recognize the data in the form of data set. SAS data set consists of two parts i.e.
- Descriptor information: This describes the contents of the SAS data set to the SAS System.
- Data values: Data that have been collected or calculated which is organized into a rectangular structure containing rows called observations and columns called variables
SAS language consists of statements. Each SAS statement is terminated by semi-colon (;) When SAS program is executed, log and lst (list or output) files are created by the system.
Log file contains the Error messages, Warnings and Notes. Whenever we run the SAS program, the first step is to open the log file and check for the errors, warnings and notes. This will help us the make the SAS program error free. When the log file is no errors, no warnings, no notes (it displays at the bottom of the log file specifying Warnings 0 Errors 0 Notes 0) then we can confirm that the processing is accurate.
Output file It contains the results of the processing. Base SAS mainly completes with two steps called Data Step and Procedure Step.
Data Step It is used to create data sets. Proc Step is used to execute the pre-defined procedures that are used for processing.
We can start SAS session with the SAS command. SAS system can be used in different environments like DOS, Windows, NT, Unix, MVS, VMS etc.
SAS programs can be run in the following methods:
- Display Manager Mode: This method is used in windowing environment. We can edit and execute programming statement, display the SAS log and output windows.
- Interactive Line Mode: In this mode, program statements are entered in sequence in response to prompts from the SAS system.
- Non-interactive mode: SAS program statements are stored in an external file and executes immediately.
- Batch mode: we can run SAS jobs in batch mode under host systems batch or background executive.
This is the main part to create a data or to describe the data that SAS system recognizes for processing. A Data step is a group of SAS language statements that begins with DATA statement and followed by programming statements that perform the manipulations necessary to build the data sets. Report writing, file management and information retrieval can all handled in the Data Step.
We can submit the Data step to the SAS system for execution. SAS System first compiles and then execute.
- SAS system checks the syntax of the SAS statements and compiles them, while compile, it translates the statements into machine code.
- DATA statement dataset name (begins step)
- Input or Set, Merge or Update (reads a record from input data)
- Optional SAS programming statements (further process the data)
- Run (end of the data step)
- Sample DATA Step: Data weight; Input rollno sub1 sub2 sub3 ; Total=sum(sub1,sub2,sub3); Datalines; 1001 90 80 75 1003 80 85 87 1004 90 95 90 ;
- Automotive
- Banking
- Financial Services
- Government & Education
- Healthcare
- LifeSciences
- Manufacturing
- Media & Entertainment
- Pharmaceutical***
- Retail
- Telecommunication
- FDA’s most preferred tool for Clinical trials for Phase I, II, III, IV etc. Only validated tool, which create regular opportunities for SAS, trained consultants.
- All major pharmaceutical companies USE SAS as the analysis tool for clinical research.
- On an average, a drug takes 12 –13 years to reach the market, therefore utilizations of SAS in all phase creates great opportunities for SAS trained consultants.
- On an average 6000-8000 clinical trials are conducted every year. Great Demand for SAS trained Consultants.
- Always there is a demand for the SAS consultants all through the year and years to come.
- Once trained, very easy to be adopted for different industries needs
Each training session will be from 6 to 8 hours based on the topic and discussion.
- Introduction to Healthcare Industry
- Introduction to Pharmaceutical Industry
- Concepts of Pharmacokinetic / Pharmacodynamic
- Design and development of CRF-Case Report Forms
- Fundamentals of Statistics related to SAS Tools and Technology
- Introduction to SAS Analysis tool and its applications
- Organizational Structure of Clinical Research Development
- Creating Analysis Datasets and Workshop
- SAS Functions and Workshop
- SAS Statements and Workshop
- SAS Procedure and Workshop
- SAS SQL and Workshop
- SAS Macros and Workshop
- SAS Import, Export and Workshop
- Analytical technique of SAS Graphs and Workshop
- Mock Interview Practice – optional
Clinical Data Management(CDM) and Safety Risk Assessment(SRA)
Heptronik is providing training on Clinical Data Management(CDM) with hands-on experience with State-of-art EDC/RDC application, Safety Risk Assessment/Management.
- Pharmaceutical Industry Overview
- Clinical Data Management process
- CRF design and Common Data sets
- Managing Lab Data/PK data
- Validation/data Import/Export/Reporting
- Database Lock/Freeze
- EDC an RDC experience
- SAS programming basics to perform edit check/validation
- Pharmaceutical Industry Overview
- Post-marketing safety analysis
- Clinical Trial Safety
- 360 degree analysis of AE/SAE/Lab/ECG/Dose/Conmeds/Medical/Surgical dataset
- Developing analysis reports
JMP Training
Heptronik is providing training on JMP with hands-on experience. This course introduces JMP, its philosophy, and its extensive graphical data exploration capabilities. Consultants learn to perform elementary exploratory data analysis (EDA) and discover natural patterns in data. Consultants use JMP to learn to navigate through menus, dialogs, and results; manage data; save and present results in various formats; use decision trees in the EDA context; and obtain and interpret descriptive statistics.
Overview of the Course Contents:
- Exploring user-assistance options
- Opening different types of files in JMP
- Understanding modeling type
- Introducing the Table panel, Columns panel, and Rows panel
- Using the Columns and Rows menus
- Creating new columns/tables
- Using the Tabulate option to create summary tables
- Exploring relationships between continuous columns
- Exploring relationships between attribute data columns
- Using recursive partitioning for exploratory analysis
- Saving results
- Exploring relationships between attribute data columns
Instructor Profile
- 12+ years of experience in healthcare with 6+ years in pharmaceuticals industry.
- Experience in statistical reporting for new drug application (NDA) submission support in Neuroscience, Oncology and Infectious Diseases, Cardiovascular, GI.
- Experience with clinical protocols development (CPD), PK/PD analysis, Designing (CRF) case report forms, SAP (Statistical Analysis Plan) and clinical trial data management (CDM) databases.
- Experience, in developing secondary databases for Ad-hoc report creation, interim analysis, responsible for creating mockup table and listing, generated tables and listings, cleaned the database and developed specification & performed edit/quality checks.
- Participate in all stages of clinical trail study team review meetings including statistical review, presentation, publication and SOP trainings.
- 9+ years of experience in Information Technology and healthcare domain(Insurance and Health Economics).
- Over 6+ years of experience as SAS® Programmer, Data manipulation, Statistical Analysis and Data Warehousing.
- Profound Knowledge and Strong experience in Base SAS, SAS/MACRO, SAS/ODS, SAS/GRAPH, SAS/SQL.
- Strong experience in SAS SHARE, SAS/IntrNet, SAS/STAT, SAS/Connect.
- Thorough knowledge of ORACLE 8i, MS-Access, SQL Server 2000, DB2 and SQL.
- Good experience in IBM Mainframes: MVS/JCL, TSO/ISPF.
- Proficient in writing Perl, Perl/CGI and UNIX Shell Scripts (KORN, BOURNE).
- Excellent skills in Web Technologies like ASP, Java Script, XML, XSLT, CSS and HTML.
- Possess experience in SAS® on Mainframes, UNIX and Microsoft Windows environments.
- Profound concepts and extensive programming experience in Java, C, C++ and Perl.
Resources
- FDA Homepage: Links or all FDA offices. (FDA.org)
- FDA CBER:Describes recent biological approvals
- FDA CADER:Describes new drug approvals, NDA review process, links to CDER’s gopher, which provides access to a variety of directories, including policies and procedures, DMFs, Human Drug CGMP notes, approved drug products, and FDA phone/fax numbers
- GPO Access: The Government printing offices home page
- NIH Small Business Funding Opportunities
- National Center for Biotechnology Information (NCBI)
- DIA: Drug Information Association
- Regulatory Affairs Professional Society (RAPS) Home page:
- BIO online:The Biotechnology Industry Organization
- GPO Access: The Government printing offices home page
- AAPS The American Association of Pharmaceutical Scientists
- DIA: Drug Information Association
- IDRAC: The world’s most comprehensive pharmaceutical regulatory affairs database
- HIS Health information: HIS Health Information offers the most comprehensive set of pharmaceutical and medical device manufacturing regulation and standards available from one source
- BioSpace.com: The Global Hubsite for Life Sciences
- BioSpaceIndustry Links:
- BioWorld.Online:The worldwide Biotechnology News and Information Source
- Medscape Molecular Medicine:
- Pharmatech’s Home page
- Pharmaceuticals Education & Research Institute
- Promega’s Home Page
- CLIA:Clinical Laboratory Improvement Amendments
- MMA:Medical Marketing Association (Devices)
- AAD:American Academy of Dermatology
- AHA:American Heart Association
- ACS:American Cancer Society
- ASGT:American Society for Gene Therapy
- ASCO:American Society of Clinical Oncology
- CFF:Cystic Fibrosis Foundation