Date: 21-30 June 2024

Time: 14:00-16:00 IST

Registration Fee

For General Participants: 1000 INR

For MTTF Members: 600 INR

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Objective

The objective of the Biostatistics Bootcamp is to provide participants with a comprehensive understanding of statistical tools and techniques relevant to biostatistics, ranging from foundational concepts to advanced methods. Through practical sessions and hands-on exercises using R programming language, participants will learn how to apply statistical analysis to biomedical data effectively. Topics covered include probability theory, hypothesis testing, regression analysis, survival analysis, and longitudinal data analysis. By the end of the boot camp, participants will have gained practical skills in R programming and a deeper understanding of how to analyze and interpret data in the context of biostatistics. Successful completion of the boot camp will also result in a certificate of participation.

Biostatistics

Biostatistics is a field of statistics that focuses on the design, analysis, and interpretation of data in biology, medicine, public health, and related sciences. It plays a crucial role in biomedical research by providing the tools and methods necessary to make sense of complex data generated from experiments, clinical trials, epidemiological studies, and observational research.

Why R programming?

R programming is widely regarded as a powerful and versatile tool for statistical analysis, particularly in the field of biostatistics. Its open-source nature and extensive library of packages make it ideal for handling complex datasets commonly found in biomedical research. R offers a wide range of statistical functions and visualization capabilities, allowing researchers to conduct sophisticated analyses and present their findings effectively. Moreover, its popularity in the academic and research communities means that users have access to a wealth of resources and support. By mastering R programming, professionals in biostatistics can enhance their analytical skills and contribute meaningfully to advancements in healthcare and medical research.

Key Features:

1. Foundational Concepts: Participants will start with essential statistical concepts such as descriptive statistics, probability distributions, and principles of statistical inference. This foundational knowledge sets the stage for more advanced topics.
2. Advanced Techniques: As the bootcamp progresses, participants will delve into advanced biostatistical techniques including regression analysis, survival analysis, meta-analysis, and machine learning methods relevant to biostatistics in the context of healthcare and life sciences.
3. Hands-On Learning: The bootcamp emphasizes practical application through hands-on exercises, case studies, and real-world examples. Participants will work with datasets commonly encountered in biomedical research, enabling them to apply statistical techniques in meaningful contexts.
4. Expert Guidance: Experienced instructors lead the sessions, providing clear explanations, answering questions, and offering guidance throughout the workshop. Participants can engage in discussions, seek clarification, and collaborate with peers in group activities.
5. Progressive Learning: The content is structured in a logical sequence, building upon foundational concepts and progressively introducing more complex topics. This approach ensures that participants grasp fundamental principles before advancing to advanced techniques.
6. Networking Opportunities: Participants have opportunities to network, share experiences, and connect with other professionals in the field of biostatistics and healthcare research. This fosters collaboration and the exchange of ideas beyond the workshop.
7. Comprehensive Resources: Participants receive comprehensive course materials, including lecture slides, handouts, datasets, recording of each session and supplementary resources. Post-workshop support may also be provided to reinforce learning and address additional questions or challenges.

Who can participate?

The Biostatistics Bootcamp is open to healthcare professionals, researchers, and students interested in mastering statistical tools for biomedical data analysis. Whether you’re a beginner looking to build a solid foundation in biostatistics or an experienced researcher aiming to enhance your analytical skills, this intensive ten-day workshop covers topics from basic concepts to advanced techniques. Join us to unlock the power of statistical analysis in healthcare and research!

Content

Day 1: Introduction to Biostatistics

• Overview of biostatistics and its importance in healthcare and research
• Basic statistical concepts: populations, samples, variables, data types
• Descriptive statistics: measures of central tendency and variability

Day 2: Probability Fundamentals

• Understanding probability distributions: normal, binomial, and Poisson distributions
• Probability rules and calculations
• Introduction to hypothesis testing: null and alternative hypotheses, significance levels, p-values

Day 3: Statistical Inference Part 1

• Parametric vs. non-parametric tests
• One-sample and two-sample t-tests
• Confidence intervals and their interpretation

Day 4: Statistical Inference Part 2

• Analysis of variance (ANOVA) basics
• Post-hoc tests and multiple comparisons
• Chi-square tests for categorical data

Day 5: Regression Analysis Basics

• Introduction to linear regression
• Assumptions of linear regression
• Model fitting and interpretation

Day 6: Advanced Regression Techniques

• Multiple regression analysis
• Logistic regression for binary outcomes
• Model evaluation and validation techniques

Day 7: Survival Analysis

• Introduction to survival analysis
• Kaplan-Meier survival curves
• Cox proportional hazards model

Day 8: Longitudinal Data Analysis

• Understanding longitudinal data
• Repeated measures ANOVA
• Mixed-effects models for longitudinal data

Day 9: Data Visualization and Interpretation

• Principles of effective data visualization
• Graph types for different data types and research questions
• Interpretation of graphical representations in biostatistics

Day 10: Practical Applications and Project Day

• Case studies and real-world examples in biostatistics
• Group projects or individual assignments applying learned techniques
• Presentation and discussion of project outcomes

Organized by

MathTech Thinking Foundation, Fazilka, India