Course Overview
Advanced Statistical Models for Bio-statisticians Using R Course is meticulously designed to provide Bio-Statisticians with a comprehensive understanding of advanced statistical modeling techniques essential for analyzing complex biological data. In this course, participants will deep into sophisticated statistical methods and their practical applications using the R programming language.By the end of this course, participants will be equipped with the skills to implement and interpret advanced statistical models, making you proficient in tackling intricate bio-statistical challenges.
Duration
5 days
Target Audience
- Biostatisticians
- Data analysts in health sciences
- Biostatistics researchers
- Advanced statistics students
- Healthcare data professionals
Organizational Impact
- Enhanced ability to handle complex statistical analyses in research.
- Improved accuracy in interpreting biostatistical data.
- Greater capability to make data-driven decisions in health and life sciences.
- Strengthened research quality and rigor in biostatistical studies.
- Increased efficiency in managing and analyzing large datasets.
Personal Impact
- Advanced understanding of complex statistical models and techniques.
- Improved proficiency in using R for biostatistical analysis.
- Enhanced problem-solving skills in handling real-world biostatistical challenges.
- Greater career opportunities in biostatistics and data analysis.
- Boosted confidence in conducting and interpreting sophisticated analyses.
Course Level:
Course Objectives
- Master advanced statistical models and methods relevant to biostatistics.
- Develop proficiency in using R for complex data analysis and visualization.
- Apply statistical techniques to real-world biostatistical problems and datasets.
- Understand and implement model validation and diagnostic techniques.
- Interpret and communicate results from advanced statistical analyses effectively.
Course Outline
Module 1: Introduction to R Programming
- Understand how to work with variables, vectors, matrices, factors, data frames, lists, and arrays
- Learn the various data types
- Learning the different functions for reading and writing data
- Learn various loop functions and structures
- Understand simulation and profiling
- Case Study: Building a Data Analysis Pipeline for Clinical Trial Data Using R
Module 2: Statistical Methods in R
- Learn errors in statistical analysis
- Understanding the logic in and the choice of significance tests
- Comparison of two independent and paired groups of data
- Multiplicity and comparison of more than two groups of data
- Calculation of variable correlation
- Equivalence and non-inferiority tests
- Confidence intervals versus p-values and trends toward significance
- Using power analysis
- Case Study: Analyzing the Effectiveness of a New Drug by Comparing Multiple Treatment Groups
Module 3: The Weibull Model
- Interpret coefficients and compute the Weibull model using the
ggsurvplot()andggsurvplot_df() - Computing the survival curves
- Visualizing a Weibull model
- Working with the
survreg()arguments - Computing the Weibull Model and the Log-Normal Model
- Case Study: Assessing the Reliability of Medical Devices Using Weibull Survival Analysis
Module 4: Survival Analysis Using Kaplan-Meier Graphs and the Log-Rank Test
- Why use Kaplan-Meier estimate
- Kaplan-Meier estimate and various functions to compute it
- Estimating and visualizing a survival curve using the Kaplan-Meier curve
- Exercising with ignoring censoring
- Estimating and visualizing the survival curve
- Comparison of the Weibull and Kaplan-Meier curves
- The Log-Rank Test
- Case Study: Comparing Survival Rates of Different Cancer Treatments Using Kaplan-Meier Analysis
Module 5: The Cox Model for Survival Analysis
- Introduction to the Cox model
- Computation and visualization of the Cox model
- Understanding the proportional hazard assumption
- Computation of the survival curve from the Cox model
- Using
surv_summary()for analysis - Comparison of the survival curves
- Case Study: Investigating the Impact of Various Risk Factors on Patient Survival Using the Cox Model
Related Courses
Course Administration Details:
Methodology
These instructor-led training sessions are delivered using a blended learning approach and include presentations, guided practical exercises, web-based tutorials, and group work. Our facilitators are seasoned industry experts with years of experience as professionals and trainers in these fields. All facilitation and course materials are offered in English. Participants should be reasonably proficient in the language.
Accreditation
Upon successful completion of this training, participants will be issued an Indepth Research Institute (IRES) certificate certified by the National Industrial Training Authority (NITA).
Training Venue
The training will be held at IRES Training Centre. The course fee covers the course tuition, training materials, two break refreshments, and lunch. All participants will additionally cater to their travel expenses, visa application, insurance, and other personal expenses.
Accommodation and Airport Transfer
Accommodation and Airport Transfer are arranged upon request. For reservations contact the Training Officer.
- Email: [email protected]
- Phone: +254715 077 817
Tailor-Made
This training can also be customized to suit the needs of your institution upon request. You can have it delivered in our IRES Training Centre or at a convenient location. For further inquiries, please contact us on:
- Email: [email protected]
- Phone: +254715 077 817
Payment
Payment should be transferred to the IRES account through a bank on or before the start of the course. Send proof of payment to [email protected]
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