Module 1: Introduction to Medical Data Analysis & R Setup

• The role of the Data Analyst in clinical and public health research
• Overview of the R ecosystem, RStudio IDE, and essential packages
• Installing and configuring R, RStudio, and necessary packages (e.g., tidyverse)
• Understanding common medical data formats (e.g., CDISC, FHIR, delimited files)
• Principles of tidy data and data governance in health contexts
• Practical session: Setting up the RStudio environment and successfully installing all core packages

Module 2: R Fundamentals and Data Import for Healthcare Data

• Basic R data structures: vectors, matrices, data frames, and lists
• Importing data from various sources: CSV, Excel, and statistical files (.sas7bdat, .dta)
• Handling missing values and special codes common in medical research
• Accessing, summarizing, and manipulating data frame columns and rows
• Writing basic R scripts for reproducible data management
• Practical session: Importing a synthetic clinical trial dataset and inspecting its structure and variable types

Module 3: Data Wrangling in R using the tidyverse

• Introduction to the dplyr package for data manipulation
• Using key functions: select(), filter(), mutate(), and summarise()
• Pivoting data from long to wide format and vice-versa using tidyr
• Joining multiple datasets (e.g., patient demographics and lab results) using join functions
• Creating derived clinical variables and recoding categorical data
• Practical session: Performing a complete data cleaning pipeline: filtering out incomplete records, calculating BMI, and merging two related clinical tables

Module 4: Descriptive Statistics and Data Summarization in Medical Context

• Calculating measures of central tendency (mean, median) and dispersion (SD, IQR)
• Summarizing categorical variables using frequency tables and proportions
• Creating stratified summaries by clinical group or demographic factor
• Automating report generation of descriptive statistics using packages like gtsummary
• Data visualization for descriptive statistics: histograms, box plots, and bar charts
• Practical session: Generating a full Table 1 (Patient Demographics and Baseline Characteristics) for a research paper

Module 5: Basic Biostatistics in R (Hypothesis Testing)

• Review of core statistical concepts: p-values, confidence intervals, and null hypotheses
• Selecting the correct statistical test based on data type and distribution
• Implementing $t$-tests and Analysis of Variance (ANOVA) for continuous outcomes
• Performing Chi-squared and Fisher's Exact tests for categorical outcomes
• Interpreting and reporting test results in a clinical research context
• Practical session: Conducting a group comparison analysis (e.g., comparing treatment vs. control group on a primary outcome)

Module 6: Introduction to Regression Models in Medical Research

• Understanding the principles of Linear Regression and its assumptions
• Implementing Logistic Regression for binary medical outcomes (e.g., disease status)
• Model building techniques: variable selection and confounding adjustment
• Evaluating model fit and interpreting coefficients (e.g., odds ratios)
• Visualizing regression results and effect sizes
• Practical session: Building a multivariable logistic regression model to predict a health outcome based on risk factors

Module 7: Survival Analysis Fundamentals and Implementation in R

• Introduction to time-to-event data and censoring in medical studies
• Calculating and visualizing Kaplan-Meier survival curves
• Performing the Log-rank test to compare survival across groups
• Implementing the Cox Proportional Hazards Model
• Interpreting Hazard Ratios (HR) and checking model assumptions
• Practical session: Analyzing a dataset for time-to-relapse, plotting the survival curve, and fitting a Cox model

Module 8: Data Visualization for Medical Research using ggplot2

• Mastering the grammar of graphics: layers, aesthetics, and geometric objects
• Creating complex plots specific to medical data: forest plots, receiver operating characteristic (ROC) curves
• Customizing plot themes, colors, and scales for professional publication quality
• Exporting visualizations in high-resolution formats for reports and presentations
• Creating dynamic and interactive plots using plotly
• Practical session: Replicating a key figure from a medical journal article using ggplot2 and creating a multi-panel visualization

Module 9: Introduction to Power BI for Data Integration

• Overview of the Power BI ecosystem: Desktop, Service, and Report Server
• Understanding the difference between Power BI Desktop and Power BI Service
• The four stages of Power BI development: Get Data, Transform, Model, Visualize
• Best practices for organizing and structuring a Power BI project
• Introduction to the user interface and key functionalities
• Practical session: Installing Power BI Desktop and connecting to a sample medical data file

Module 10: Connecting, Importing, and Transforming Medical Data in Power BI

• Connecting to various data sources relevant to healthcare (SQL databases, APIs, files)
• Utilizing the Power Query Editor (M language basics) for data transformation
• Cleaning and preparing data: handling missing values, changing data types, and splitting columns
• Merging and appending queries to consolidate disparate health datasets
• Creating custom columns and conditional columns for analytical flags
• Practical session: Importing data from two different sources (e.g., Excel and Web) and transforming/cleaning them in Power Query

Module 11: Data Modeling and Relationships in Power BI

• The importance of data modeling for accurate and scalable reports
• Understanding Star Schema design: fact tables and dimension tables
• Creating and managing relationships between tables in the Model View
• Setting relationship cardinality (one-to-many, many-to-many) and cross-filter direction
• Creating calculated tables to simplify complex data structures
• Practical session: Building a Star Schema model using demographic, clinical event, and diagnosis tables

Module 12: Introduction to DAX for Advanced Calculations

• Understanding Calculated Columns vs. Measures and when to use each
• Fundamentals of DAX syntax, context, and iterative functions
• Creating core measures: counts, sums, averages, and time intelligence functions
• Utilizing the CALCULATE function for powerful context modification
• Implementing complex medical metrics (e.g., re-admission rates, average length of stay)
• Practical session: Writing DAX measures to calculate rolling averages and Year-over-Year change for clinical KPIs

Module 13: Creating Interactive Dashboards in Power BI

• Principles of effective dashboard design and user experience (UX)
• Utilizing various visual types: bar charts, line charts, scatter plots, and cards
• Implementing filtering, slicing, and drill-through actions for data exploration
• Designing reports for different audiences (e.g., clinicians vs. administrators)
• Advanced interactions: bookmarks, buttons, and custom navigation
• Practical session: Building the first interactive report page, including slicers and cross-filtering between visuals

Module 14: Visualization Best Practices for Medical Reporting

• Choosing the right chart type for different types of health data (e.g., funnel charts for patient flow)
• Using color effectively to highlight key clinical findings and minimize clutter
• Implementing custom visual templates and themes for organizational branding
• Creating Key Performance Indicator (KPI) visuals with conditional formatting and targets
• Accessibility considerations in dashboard design for all users
• Practical session: Creating a comprehensive hospital performance dashboard with clear KPIs and action-oriented visuals

Module 15: Incorporating R Visuals and Scripts into Power BI

• Setting up the R environment integration within Power BI Desktop
• Writing R scripts to perform custom statistical analysis (e.g., clustering)
• Creating advanced R visuals (e.g., network graphs, specialized plots) directly in Power BI
• Understanding data exchange limitations and performance considerations
• Troubleshooting common R script errors within the Power BI environment
• Practical session: Running an R script in Power BI to generate a dynamic, custom biostatistical visualization based on the dashboard's filters

Module 16: Ethics, Data Governance, and Reproducible Research

• Understanding regulatory requirements for health data (e.g., HIPAA, GDPR)
• Techniques for data anonymization and de-identification in analysis
• Documenting R code and Power BI transformations for reproducibility
• Strategies for version control and collaborative data analysis
• Ethical considerations for communicating results that impact patient care
• Practical session: Developing a standard operating procedure (SOP) template for documenting a complete R and Power BI data pipeline

Module 17: Advanced Power BI: Security and Distribution

• Publishing reports to the Power BI Service and setting up refresh schedules
• Implementing Row-Level Security (RLS) using DAX functions to restrict data access
• Managing workspaces, permissions, and sharing reports with specific users
• Creating Power BI Apps for simplified content delivery to end-users
• Monitoring usage, performance, and data security alerts in the Power BI Service
• Practical session: Publishing the final report to the Power BI Service and configuring a simple RLS model based on user roles

Module 18: Capstone Project: End-to-End Medical Analytics Workflow

• Defining a clear medical research question and necessary data requirements
• Independently executing the entire workflow: R data wrangling, statistical modeling
• Integrating the final, cleaned data into Power BI for data modeling
• Designing and presenting a comprehensive, interactive final dashboard
• Peer review and expert feedback on both the statistical robustness (R) and the visualization quality (Power BI)
• Practical session: Participants finalize their independent capstone project and present their analytical process and results

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