Introduction to Biostatistics

INTRODUCTION TO BIOSTATIS

Biostatistics is essential to ensuring that findings and practices in public health and biomedicine are supported by reliable evidence. This course covers the basic tools for the collection, analysis, and presentation of data in all areas of public health. Central to these skills is assessing the impact of chance and variability on the interpretation of research findings and subsequent recommendations for public health practice and policy. Topics covered include: general principles of study design; probability, hypothesis testing; review of methods for comparison of discrete and continuous data including ANOVA, t-test, correlation, and regression. This course is part of the core course requirement for the MPH and is a prerequisite for other courses in the Department of Biostatistics and throughout the Mailman School of Public Health.

• Instructor: Christine M Mauro
  Info
• 3 points

Introduction to Biostatistical Methods

INTRO-BIOSTATISTICAL METH

Like many fields of learning, biostatistics has its own vocabulary often seen in medical and public health literature. Phrases like statistical significance", "p-value less than 0.05", "95% confident", and "margin of error" can have enormous impact in a world that relies on statistics to make decisions: Should Drug A be recommended over Drug B? Should a national policy on X be implemented? Does Vitamin C truly prevent colds? However, do we really know what these terms and phrases mean? Understanding the theory and methodology behind study design, estimation and hypothesis testing is crucial to ensuring that findings and practices in public health and biomedicine are supported by reliable evidence.

• 3 points

Principles of Epidemiology I

PRINCIPLES OF EPIDEMIOLOG

Epidemiology is one of the pillars of public health. Epidemiologists study the distribution and determinants of disease in human populations; they also develop and test ways to prevent and control disease. The discipline covers the full range of disease occurrence, including genetic and environmental causes for both infectious and noninfectious diseases. Increasingly, epidemiologists view causation in the broadest sense, as extending from molecular factors at the one extreme, to social and cultural determinants at the other. This course introduces students to the theory, methods, and body of knowledge of epidemiology. Principles of Epidemiology is designed for students in all fields of public health. The primary objective of the course is to teach the basic principles and applications of epidemiology.

• 3 points

WRITE-SUCCESSFL NIH GRANT APPLC

• 1 points

Decision Analysis and Cost Effectiveness

DEC ANAL & COST EFFECT HL

The course is an introduction to the concepts, methods, and application of decision analysis and economic evaluation of healthcare programs. In particular, the course focuses on the foundations and construction of cost-effectiveness analyses as well as the interpretation and critique of cost-effectiveness literature. Students will gain hands on experience using both Excel and TreeAge Pro to conduct decision analytic models.

• 2 points

Introduction to Advanced Epidemiologic M

INTRO TO ADV EPI METHODS

This course is geared towards physician researchers to provide hands-on experience with statistical methods that go beyond the basics to enhance grant writing skills. Students who are actively developing grants are encouraged to use the course to develop their methods section and/or analyze preliminary data. Topics include sample size calculations, clustered data, interrupted time series analysis, multilevel modeling, and longitudinal data analysis techniques. The course will take place over six 3.5-hour sessions conducted on Zoom. Each session will begin with a lecture introducing the analysis methods covered which will be followed by a lab session in which students will use SAS to conduct exercises implementing the analytic techniques (datasets and example code will be provided). Lab sessions will include individual and breakout group work. Course assignments will include readings, labs (which will not be graded), short quizzes on the analysis methods, assignments and a final project in which students will develop an analytic plan, conduct a data analysis and present findings (students can use their own or a course-provided dataset). Each student must have a full-version of SAS installed (SAS University and SAS OnDemand will not be sufficient for this course) and SAS experience is strongly preferred.

• F 9 a.m.-12:30 p.m.
• 1.5 points

Introduction to Race and Health

INTRODUCTION TO RACE & HE

It is widely acknowledged that as a variable, 'race' often explains a significant portion of the variation we observe in patterns of morbidity and mortality. But it isalso understood that race is a socially determined construct that functions as a proxy for a host of other variables associated with, among others, socioeconomic status, culture, place of residence, and position within social networks. The question that we will explore together is how to deconstruct ‘race’ to understand what factor or group of factors create the patterns of health disparities that are so dramatically present among populations of color here in the US. COVID-19 has exploited these factors tocreate a burden of disease in many communities of color that will substantially impact medicine and public health for much of the foreseeable future.One of the issues of particular salience for medical and public health research is how to go beyond describing the correlation between race and health to create effective interventions for eliminating such disparities. How can our exploration of health disparities generate the levers that we can use to promote health and prevent disease? How well do our explanatory models of race and health provide us with the tools to eliminate disparities and create a system dedicated to creating and preserving health equity?

• 1 points