biostatistics in public health

1. Choosing the Best Models: Definition: In statistical modeling and machine learning, choosing the best model refers to the process of selecting the most appropriate model from a set of candidate models. The goal is to find a model that accurately captures the underlying patterns in the data while avoiding overfitting (excessive complexity). 2. Model […]

1. Dealing with Missing Data in Biostatistical Analysis: Definition: Missing data refers to the absence of values or observations for some variables or cases in a dataset. Handling missing data is a critical step in biostatistical analysis because ignoring it can lead to biased results and reduced statistical power. In biostatistical research, missing data can

1. Hierarchical Data Structures in Biostatistics: Definition: In biostatistics, hierarchical data structures refer to data collected from nested or hierarchical levels of observation. This means that data points are organized into groups or clusters, and observations within the same group may be more similar to each other than to observations in other groups. Such structures

1. Assessing Model Assumptions: Definition: Assessing model assumptions is a crucial step in regression analysis to ensure that the statistical model used is appropriate for the data. Regression models, including linear regression, logistic regression, and Poisson regression, rely on several key assumptions: Linearity: The relationship between the dependent and independent variables is linear. This assumption

1. Survival Analysis and Hazard Functions: Definition: Cox proportional hazards regression, often referred to as just Cox regression, is a statistical method used for survival analysis. Survival analysis deals with time-to-event data, where the “event” can be anything of interest, such as death, disease recurrence, or equipment failure. Survival Analysis: Survival analysis focuses on the

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: