CLINICAL TRAIL DATA ANALYSIS (CTDA) WITH R PROGRAMMING
- Clinical Trail Data Analysis with R Programming full course.
- Duration: 3 Month (Weekends) /65 Hours.
- Training Days: SAT / SUN (2 to 3 Hrs per Session)
- Training Mode: Offline /Online
- Course Materials: Real-Time Project, Videos, Books
- Training Access: Life-time access for Both offline & Online Mode.
- Offline training Location: Mumbai & Pune
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Comprehensive Course Curriculum
- History and Overview of R
- Getting Started with R
- R Nuts and Bolts
- Mixing Objects
- Getting Data In and Out of R
- Using Textual and Binary Formats for Storing Data
- Interfaces to the Outside World
- Sub setting R Objects
- Vectorized Operations
- Dates and Times
- Managing Data Frames with the dplyr package
- Control Structures
- Functions
- Scoping Rules of R
- Coding Standards for R
- Loop Functions Regular Expressions
- Debugging
- Profiling R Code
Data Visualization in R :
Topics :
- Creating Bar Plot
- Creating Histogram
- Creating Scatter plot
- Creating Boxplot
- Creating Pie plot
- Adding Legends and labels to plots
- Formatting the plot using par parameters
- Create all the above plot for more effective Visualization in R using ggplot2
Descriptive statistics with R
- Understanding Types of Data
- Types of Descriptive Statistics?
- Measure of Central Tendency – Mean, Median, Mode
- Measure of Spread / Dispersion – Range, Variance, standard deviation
- Skewness
- Kurtosis
- Correlation
Probability and statistics with R
Topics
- Probability and Central Limit Theorem
- Exploratory Data Analysis
- Normal Distribution
- Understanding the sampling and it importance
- Different type of Sampling Method
- Importance of Hypothesis Testing in Business
- Basics of Hypothesis Testing
- Null and Alternate Hypothesis
- Understanding Types of Errors – Type I and Type II erroes
- Confidence Interval and Confidence level
- Z-Test
- P Value
- Degree of Freedom
statistical modelling using R:
Topic: Linear and multiple regression
- Introduction to Linear Regression & multiple regression
- Linear Regression with Multiple Variables
- Assumptions of Linear Regression
- Disadvantage of Linear Models
- Case Study on Linear regression Model using R
- Interpretation of Model Outputs
- Evaluation Metric for Linear regression – Mean absolute error, MAPE etc
- B) Topic: Logistic Regression
- Introduction to Logistic Regression
- Why Logistic Regression
- Odds Ratio
- Advantages and Disadvantages of Logistic Regression
- Case Study on Logistic regression Model using R
- Evaluation Metric for Logistic regression – Confusion Matrix, ROC and AUC
CDICS: R Programming For STDM, ADAM & TLF
&
Real- Time Project: (Dermatology & Oncology) Therapeutic Study
- Parallel Computation
- Safety-Listings
- Safety-Tables
- Efficacy-Tables
- Safety & Efficacy Figures/Graphs
- Safety-ADAM Data Sets
- Efficacy-ADAM Data Sets
- Safety-SDTM Data Sets
- Efficacy-SDTM Data Sets
- How to read CRF raw data (SAS7bdat, xlsx, SQL table) using R studio packages such as
Sqldf.
- How to create STDM & ADAM data output in xpt files by using R studio packages such as dplyr, filter, mutate.
- Ind-depth practical understating of most widely used R packages for STDM & ADAM
Such as dplyr, plyr, tidyr.
- How to create table, listing, figure and write output in different format such as rtf, pdf, html & word using R studio most advanced packages such as flextable, officer magrittr knitr & ggplot2.
PART B
INTRODUCTION TO CDISC (Clinical Data Interchange Standards Consortium)
CDICS – It’s a non-profit organization. CDICS standards are used for organizing and standardizing clinical trial data to ensure data quality, consistency, and regulatory compliance.
Module 1: CDISC (STDM, ADAM & TLF)
Fundamentals of Clinical Trial:
- Drug development
- Trial design Important documents
- Data management
- Statistical programming Statistical analysis Guidelines
CDISC: SDTM
- Assignment to Analysis Populations
- Informed Consent
- Subject Demographics
- Withdrawals from the Study
- Abnormal Biochemistry Values
- Serious Adverse Events Leading to Death
CDISC: ADaM
- Introduction to ADaM
- Why ADaM Key Concepts
- ADaM naming convention
- ADaM Implementation
- ADaM basics and general conventions Fundamentals and purpose of the main ADaM data structures
- Real time project on ADaM ADSL ADMH ADAE ADCM ADVS ADLB ADTTE
TLF – Listing
- CDISC overview including CDASH
- CRF annotation
- SDTM fundamentals and domains
- From source to SDTM: step-by step
- Models for special purpose domains – Demographics
- Domain model based on the general observation classes
- Events – Adverse Events and Medical History
- Interventions – Concomitant and Prior Medication
- Findings – Vital Signs
TLF – Tables:
- Subject Assignment to Analysis Populations
- Subject Disposition by Treatment (Safety Population)
- Subject Demographics – Age (Safety Population)
- Subject Demographics – Age (ITT Population)
- Subject Demographics (Safety Population)
- Subject Demographics -Sex and Race (Safety Population)
- Subject Demographics -Sex and Race (ITT Population)
- Treatment Emergent Adverse Events by Treatment, System Organ Class and Preferred Term (Safety Population)
- Summary of Changes in Vital Signs from Baseline to Final Visit (Safety Population)
- Shift Table from Baseline to End of period (Safety Population)
Frequently Asked Questions
A Clinical trial data analyst with SAS & R at ICRB is a specialized training program focused on providing comprehensive knowledge and Techno functional skills for creating reports on clinical Trail Data As per Regulatory guidelines.
Tool such AS SAS, SQL, Macros & complete R programming.
Platform Such as SAS, R Programming for creating table listing figures (TLF) as per RA guidelines
Cdics guidelines
Clinical trial domain Such as STDM ADAM
You will never miss a lecture at Techdata Solution You can choose either of the two options:
View the recorded session of the class available in your LMS.
You can attend the missed session, in any other live batch.
Yes! All learners get lifetime access to all course material once you have enrolled.
Yes. Certificate is Rewarded to Those candidates who completes the Real-time project.
Yes, we have flexible timing for our course.