Training On Epidemiological Data Analysis Using STATA
INTRODUCTION
The "Epidemiological Data Analysis Using STATA" training course is designed to equip participants with the skills necessary to analyze epidemiological data using STATA, a powerful statistical software widely used in public health, clinical research, and epidemiology. This course focuses on the application of statistical techniques to understand health-related phenomena and to draw meaningful conclusions from data.
Participants will learn how to manage and manipulate datasets, perform descriptive and inferential statistical analyses, and interpret the results in the context of public health research. The course will cover various epidemiological concepts, such as risk assessment, correlation, regression analysis, and survival analysis, providing participants with practical tools to conduct their own analyses.
Through hands-on exercises and real-world examples, participants will gain the confidence to use STATA effectively for epidemiological research. By the end of the course, attendees will be able to design studies, analyze data, and communicate findings, contributing to evidence-based decision-making in health-related fields.
DURATION
5 Days
TARGET AUDIENCE
The course is suitable for potential epidemiologists and biostatisticians and current researchers including clinicians, laboratory and social scientists. Participants should have knowledge of Basic Statistics and be familiar with the Statistical package Stata.
OBJECTIVES
By the end of this course, participants will be able to:
- Understand the principles of epidemiology and how they relate to data analysis.
- Navigate the STATA software interface and utilize its features for data management and analysis.
- Import, clean, and prepare epidemiological datasets for analysis.
- Perform descriptive statistical analyses to summarize epidemiological data.
- Conduct inferential statistical tests relevant to epidemiological research (e.g., t-tests, chi-square tests).
- Utilize regression analysis techniques to assess relationships between variables, including linear and logistic regression.
- Implement survival analysis methods to evaluate time-to-event data.
- Interpret and communicate statistical results clearly and effectively in the context of public health.
- Apply best practices in data management and ensure data quality throughout the analysis process.
- Address ethical considerations in epidemiological research and data analysis
COURSE OUTLINE
Module 1: Introduction to Epidemiology and STATA
- Overview of epidemiology: key concepts and terminology
- The role of data analysis in epidemiological research
- Introduction to STATA: installation, interface, and basic commands
Module 2: Data Management in STATA
- Importing data from various formats (Excel, CSV, etc.)
- Data cleaning techniques: handling missing values and outliers
- Creating and modifying variables: recoding, generating, and labeling
- Structuring datasets for analysis: wide vs. long formats
Module 3: Descriptive Data Analysis
- Calculating measures of central tendency and dispersion
- Generating frequency distributions and cross-tabulations
- Visualizing data using graphs: histograms, bar charts, and boxplots
- Summarizing and interpreting descriptive statistics
Module 4: Inferential Statistical Analysis
- Overview of hypothesis testing and confidence intervals
- Performing t-tests for comparing means between groups
- Conducting chi-square tests for categorical data analysis
- Interpreting results and understanding p-values and effect sizes
Module 5: Regression Analysis in Epidemiology
- Introduction to regression analysis: purpose and applications
- Conducting linear regression: assumptions, interpretation, and diagnostics
- Exploring logistic regression for binary outcomes: modeling and interpretation
- Assessing model fit and identifying confounding variables
Module 6: Survival Analysis Techniques
- Overview of survival analysis concepts: censored data and survival curves
- Using Kaplan-Meier estimator to estimate survival probabilities
- Conducting log-rank tests for comparing survival distributions
- Implementing Cox proportional hazards model for time-to-event analysis
Module 7: Advanced Statistical Techniques
- Introduction to multivariable regression analysis: controlling for confounders
- Using generalized estimating equations (GEEs) for correlated data
- Handling complex survey data with appropriate statistical techniques
- Exploring machine learning techniques for epidemiological data (optional)
Module 8: Interpreting Results and Reporting Findings
- Communicating statistical findings to diverse audiences
- Best practices for presenting data visualizations and reports
- Writing research reports: structure and key components
- Addressing limitations and implications for public health practice
Module 9: Ethical Considerations in Epidemiological Research
- Understanding ethical issues in data collection and analysis
- Informed consent, confidentiality, and data protection
- Institutional Review Boards (IRBs) and their role in research
- Navigating ethical dilemmas in epidemiological studies
Module 10: Practical Applications and Case Studies
- Hands-on exercises with real-world epidemiological datasets
CERTIFICATION
- Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate
TRAINING VENUE
- Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.
AIRPORT PICK UP AND ACCOMMODATION
- Airport pick up and accommodation is arranged upon request
TERMS OF PAYMENT
- Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com
