What This Document Is
This document outlines a series of data analysis projects designed for students in an advanced epidemiological methods course. It details several independent projects focusing on practical application of statistical techniques to real-world health data – specifically, data from the CPUM study (Centralia Pneumoconiosis Study). The projects require students to utilize statistical software (specifically SAS) and apply proportional hazards modeling, risk set analysis, and related methods. It’s structured as a set of assignments with varying levels of complexity and assigned participation.
Why This Document Matters
This resource is crucial for students enrolled in Statistical Methods for Epidemiological Studies (PM 518b) at the University of Southern California. It serves as the primary guide for completing hands-on data analysis exercises. Students will benefit from working through these projects to solidify their understanding of epidemiological modeling, data manipulation, and interpretation of results. It’s particularly valuable when you need to translate theoretical knowledge into practical skills, preparing you for independent research or professional roles involving epidemiological data analysis. This is best used *during* the course, as the projects build upon concepts taught in lectures.
Common Limitations or Challenges
This document does *not* provide step-by-step instructions or fully worked-out solutions. It assumes a foundational understanding of statistical concepts and SAS programming. While it specifies the datasets to be used and the general analytical goals, it’s up to the student to independently implement the methods and interpret the outputs. It also doesn’t offer extensive background information on the CPUM study itself – familiarity with the study context is expected. Access to the necessary SAS software and data files (available separately) is also required.
What This Document Provides
* A list of distinct data analysis projects (A, B, C, and D) with assigned student groups.
* Descriptions of the analytical tasks for each project, including the statistical models to be employed.
* Specific data files required for each project, including details on their contents.
* References to relevant SAS code files provided separately, intended to assist with implementation.
* Clear deadlines for project submission.
* Opportunities for extra credit by completing unassigned projects.
