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This document contain class notes on the course epidemiologic methods. It provides an in - depth explanation on bias. It was prepared Dr Ndiritu who is a PhD professor teaching The University of Kabianga. Please visit the document for more information.

This guide covers key epidemiologic concepts, including bias, confounding, validity, regression analysis, T-tests, and ANOVA. It explains types of bias, methods to control confounding, and ways to ensure validity. It also introduces regression models and statistical tests for comparing groups, making it a val

This document provides a concise overview of T-tests and Analysis of Variance (ANOVA), two essential statistical methods used to compare group means in epidemiologic studies. It explains when to use a T-test (for comparing two groups) and ANOVA (for comparing three or more groups), along with their assumptions and interpretations. Readers will learn how these tests help assess differences in health outcomes across populations and how to apply them correctly to avoid common pitfalls. This guide i...

This document provides an introduction to regression analysis, a fundamental statistical method used in epidemiology to examine relationships between variables. It covers key types of regression, including linear regression, logistic regression, and Cox proportional hazards regression, explaining their applications in analyzing health data. Readers will gain insights into interpreting regression coefficients, adjusting for confounders, and assessing model validity. This guide is designed to help...

This document explores the concept of validity in epidemiologic studies, emphasizing its importance in ensuring accurate and reliable research findings. It covers the two main types of validity: internal validity, which refers to the accuracy of study results within a specific population, and external validity, which determines how well findings can be generalized. The document also discusses key threats to validity, including bias, confounding, and measurement errors, along with strategies to m...

This document provides a clear and structured overview of confounding, a crucial concept in epidemiologic research. It explains how confounding can distort associations between exposures and health outcomes, leading to biased conclusions. Readers will learn how to identify potential confounders and apply various methods to control for confounding, including randomization, stratification, matching, and statistical adjustments. Designed for students and researchers in public health, this guide ser...