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Advanced Biostatistics Syllabus • Principles of regression • Methods of regression • Linear regression • Logistic regression • Poisson regression • Cox proportional hazards regression • Regression diagnostics • Introduction to multilevel modelling • Introduction to data imputation • Choosing the best models Course Structure : 1. Introduction Importance of Advanced Biostatistics in Medical Research […]

Definition and Purpose of Regression Analysis: Regression analysis is a statistical technique used to examine the relationship between one or more independent variables and a dependent variable. It is primarily employed to understand how changes in one or more independent variables are associated with changes in the dependent variable. The primary purposes of regression analysis

Medical research is a dynamic and critical field dedicated to advancing our understanding of diseases, treatments, and healthcare practices. At the heart of this scientific pursuit lies a fundamental need for sound and rigorous data analysis. Advanced biostatistics, a specialized branch of statistics tailored for biomedical and clinical investigations, plays a pivotal role in this

1. Handling Count Data: Definition: Poisson regression is a statistical technique used to model the relationship between a count-dependent variable and one or more independent variables. It is particularly suited for situations where the dependent variable represents the number of events or occurrences within a fixed period or area. Handling Count Data: Count data are

1. Modeling Binary Outcomes: Definition: Logistic regression is a statistical method used to model the relationship between a binary (dichotomous) dependent variable (usually coded as 0 or 1) and one or more independent variables. It is employed when the outcome of interest is categorical and binary, such as yes/no, presence/absence, or success/failure. Modelling Binary Outcomes:

1. Understanding Linear Relationships: Definition: Linear regression is a statistical method used to model the relationship between a dependent variable (Y) and one or more independent variables (X) by fitting a linear equation (a straight line) to the data. Understanding Linear Relationships: In a linear regression model, the relationship between the dependent variable and the

Methods of Regression: Overview of Different Regression Methods, Use Cases, and Applicability Regression analysis encompasses a variety of methods that can be applied depending on the characteristics of the data and the research objectives. Below is an overview of different regression methods, along with their common use cases and applicability: 1. Linear Regression: Overview: Linear