Module 1: R Programming Fundamentals for Clinical Data

• Setting up the R and RStudio environment and essential packages
• Core R data structures: vectors, factors, data frames, and lists
• Writing and executing basic R scripts for data manipulation
• Principles of clean, reproducible R code for statistical analysis
• Practical session: Performing initial setup and writing an R script to calculate patient demographics and baseline characteristics

Module 2: Data Acquisition and Wrangling in R (Tidyverse)

• Reading and writing various clinical data formats (CSV, SAS/SPSS files, RData)
• Data transformation and cleaning using the dplyr package (filter, mutate, select)
• Data reshaping and pivoting using the tidyr package (wide-to-long formats)
• Handling missing values and conducting outlier detection in trial data
• Practical session: Cleaning and standardizing a raw clinical trial dataset into a tidy format using readr and dplyr

Module 3: Introduction to Clinical Trial Design and Data Management

• Understanding common clinical trial designs (e.g., randomized controlled trials, crossover)
• Key concepts: endpoints, intent-to-treat (ITT), per-protocol (PP) analysis
• Introduction to CDISC standards (SDTM and ADaM) for structured data analysis
• Handling longitudinal data and repeated measures in R
• Practical session: Restructuring a simulated Subject Data Tabulation Model (SDTM) dataset into an Analysis Data Model (ADaM) equivalent

Module 4: Descriptive Biostatistics and Data Summarization in R

• Review of descriptive statistics: measures of central tendency and dispersion
• Summarizing categorical data using frequency tables and proportions
• Presenting continuous data by group (e.g., mean $\pm$ SD, median and IQR)
• Using the arsenal or tableone packages for creating Table 1 (baseline characteristics)
• Practical session: Generating a complete Table 1 of baseline demographics for a clinical trial cohort in R

Module 5: Inferential Statistics and Hypothesis Testing in Clinical Research

• Setting up the Null Hypothesis ($H_0$) and the Alternative Hypothesis ($H_a$) for clinical endpoints
• Detailed use of the One-Sample and Two-Sample t-tests for continuous data
• Applying Chi-Square ($\chi^2$) and Fisher's exact tests for categorical outcome comparison
• Interpreting p-values, confidence intervals, and effect size in clinical reports
• Practical session: Conducting a Two-Sample t-test in R to assess a significant difference in the primary efficacy endpoint between treatment groups

Module 6: Analysis of Variance (ANOVA) and Non-Parametric Tests

• Understanding when to use One-Way and Two-Way Analysis of Variance (ANOVA)
• Post-Hoc tests (e.g., Tukey's HSD) for identifying specific pair differences after ANOVA
• The need for non-parametric tests when assumptions are violated (e.g., Mann-Whitney U, Wilcoxon Signed-Rank)
• Selecting the appropriate statistical test based on data distribution and research question
• Practical session: Performing a One-Way ANOVA and post-hoc analysis on drug concentration levels across three different patient subgroups

Module 7: Linear and Logistic Regression for Clinical Outcomes

• Building and interpreting Simple and Multiple Linear Regression models for continuous outcomes
• Applying Logistic Regression to predict binary outcomes (e.g., response to treatment, adverse events)
• Interpretation of coefficients, Odds Ratios (ORs), and confidence intervals in the clinical context
• Model diagnostics: checking assumptions and assessing goodness-of-fit for regression
• Practical session: Developing and evaluating a Logistic Regression model to predict the probability of a positive therapeutic response

Module 8: Survival Analysis and Time-to-Event Data in R

• Core concepts: event, censoring (right, left, interval), and the survival function
• Calculating and visualizing the Kaplan-Meier Survival Curve using the survival package
• Performing the Log-Rank Test to compare survival curves across different treatment arms
• Introduction to the Cox Proportional Hazards Model for multivariate analysis
• Practical session: Running a Kaplan-Meier analysis and Log-Rank test to compare Time-to-Progression (TTP) between two therapies

Module 9: Advanced Clinical Modeling and Sample Size Calculation

• Introduction to Mixed-Effects Models for longitudinal data and repeated measurements
• Methods for modeling competing risks in clinical events
• Fundamental principles of calculating appropriate sample size and statistical power
• Using simulation in R to validate complex model structures
• Practical session: Calculating the necessary sample size for a new clinical trial design based on expected effect size and power

Module 10: Principles of Data Visualization for Publication (ggplot2)

• Utilizing the Grammar of Graphics framework for building clinical plots in R
• Best practices for designing clear, unbiased visualizations (Tufte's principles)
• Customizing plot themes, fonts, and colors for journal publication standards
• Effective use of facets and small multiples for comparing trial subgroups
• Practical session: Creating a multi-panel, publication-ready scatter plot using ggplot2 to show drug efficacy over time

Module 11: Communicating Uncertainty and Error in Clinical Graphics

• Correctly displaying measures of variability: standard deviation vs. standard error
• Visualizing confidence intervals (CIs) on bar charts and line plots
• Techniques for visualizing statistical significance directly on plots (e.g., annotation)
• Avoiding deceptive visualization practices (e.g., truncated axes, inappropriate aggregation)
• Practical session: Creating a plot that correctly displays both the mean effect and the 95% confidence interval for a primary outcome

Module 12: Specialty Clinical Visualizations (Forest, Funnel Plots)

• Creating Forest Plots to display the results of subgroup analyses or meta-analyses
• Generating Funnel Plots to assess publication bias or heterogeneity in results
• Using high-density plots (e.g., violin, joy plots) to visualize distributions of clinical variables
• Techniques for mapping clinical data (if geographically relevant) using R
• Practical session: Constructing a complete Forest Plot summarizing the treatment effect across several pre-specified clinical subgroups

Module 13: Power BI Fundamentals and Data Ingestion

• Understanding the Power BI ecosystem (Desktop, Service, Gateway)
• Connecting Power BI to diverse data sources, including R script outputs and Excel sheets
• Importing and linking multiple data tables relevant to the clinical study
• Overview of data inspection and profiling capabilities in Power BI
• Practical session: Connecting Power BI Desktop to a simulated database containing patient metadata and lab results

Module 14: Data Transformation with Power Query (M Language)

• Using the Power Query Editor for advanced data cleaning, reshaping, and merging
• Writing custom columns and logic using the M language for clinical feature engineering
• Handling complex date/time data and calculating time intervals relevant to treatment
• Optimizing query performance for large datasets and refreshing data feeds
• Practical session: Performing advanced data cleaning and creating new derived clinical variables (e.g., age bands) using Power Query

Module 15: Designing Robust Data Models and Relationships (Star Schema)

• Principles of dimensional modeling: Fact Tables (e.g., visits, events) and Dimension Tables (e.g., patients, sites)
• Creating and managing table relationships for accurate filtering and analysis
• Optimizing the data model structure for speed and scalability (Star Schema approach)
• Structuring fields and hierarchies for intuitive report consumption by clinical staff
• Practical session: Building a robust Star Schema data model based on patient demographics, site information, and trial event records

Module 16: DAX Measures and Key Performance Indicators (KPIs) for Trials

• Understanding the difference between calculated columns and DAX measures
• Fundamental DAX functions (SUM, CALCULATE, FILTER) and context transitions
• Time Intelligence functions for tracking trends (e.g., enrollment rates over time)
• Creating critical clinical KPIs (e.g., Adverse Event Rate, Screen Failure Rate, Compliance)
• Practical session: Writing core DAX measures to calculate trial enrollment velocity and patient retention percentage

Module 17: Interactive Clinical Dashboard Creation in Power BI

• Principles of effective dashboard design for clinical users (e.g., clarity, minimal clutter)
• Utilizing standard and custom visuals for clinical reporting (e.g., Slicers, Cards, Gauges)
• Implementing advanced filtering, drill-through, and report page tooltips
• Best practices for securing and publishing reports to the Power BI Service
• Practical session: Designing and publishing a comprehensive, interactive Clinical Trial Monitoring Dashboard (CTMD)

Module 18: Ethical Reporting, Data Governance, and R Integration

• Review of data governance policies and ethical reporting standards (e.g., regulatory guidelines)
• Strategies for addressing privacy and security concerns (e.g., Row-Level Security conceptual)
• Integrating R visuals and model outputs into Power BI reports via R Visuals
• Using Power BI to track and visualize R model performance metrics
• Practical session: Integrating an R-generated survival plot (Kaplan-Meier) into a Power BI report for dynamic visual presentation

Requirements:

Participants should be reasonably proficient in English.

Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588