In addition to the required courses described below, PhD students are also required to:
Course Director: Xi Kathy Zhou, PhD
This course provides an introduction to biostatistical concepts and reasoning. Specific topics include tools for describing central tendency and variability in data, probability distributions, sampling distributions, estimation, and hypothesis testing. Assignments will involve computation using the R programming language.
Course Director: Samprit Banerjee, PhD
The focus of this course is the theory and application of different types of regression analysis. Topics will include: linear regression, logistic regression, and Cox proportional hazards regression. Additional topics will include coding of explanatory variables, residual diagnostics, model selection techniques, random effects and mixed models, and maximum likelihood estimation. Homework assignments will involve 4 computations using the R statistical package.
This course provides an introduction to data science using both R and Python. In this course students will gain experience working directly with data to pose and answer questions. Topics will include: reproducible research, exploratory data analysis, data manipulation, data visualization techniques, simulation design, and unsupervised learning methods. The first part of the course will be taught with the programming language R and the second with python.
Course Director: Samprit Banerjee, PhD, MStat
The course starts with logistic regression and discriminant analysis with emphasis on classification and prediction. We will then cover more advanced topics such as regularized regression, resampling methods, tree-based methods, and support vector machines.
Course Director: Shoshana Rosenberg, ScD, MPH
The goal of this course is to provide students with a foundation of epidemiologic methods. This course will introduce students to key epidemiologic concepts including measures of disease frequency, study designs, bias, and causal inference. Students will also learn how to critically evaluate epidemiologic research papers.
Course Director: Kevin Kensler, ScD
The goal of this course is to provide students with more advanced epidemiologic methods and statistical analyses appropriate for specific study designs. This course will expand students’ knowledge of epidemiologic concepts related to the design, conduct and interpretation of epidemiologic studies.
Course Director: Jiani Yu, PhD
This course is designed to introduce students to the fundamentals of health services research. Health services research is the discipline that measures the evaluations of interventions designed to improve healthcare. These interventions can include changes to the organization, delivery and financing of health care and various healthcare policies. Common outcome measures in health services research include (but are not limited to) patient safety, healthcare quality, healthcare utilization, and cost. Specific topics to be covered in this course include: refining your research question, identifying common research designs and their strengths and weaknesses, minimizing bias and confounding, selecting data sources, optimizing measurement, and more. There will also be a component of the course that explores how to present your ideas and iteratively refine your work, based on feedback from peers and reviewers. This course includes both lectures and interactive group discussions.
Course Director: Jose Florez-Arango, MD, MS, PhD
In recent years we have witnessed how the collection, storage and usage of digital health data has grown exponentially in both quantity and complexity. The field of health informatics concerns the acquisition, storage, management and use of information in, about, and for human health, and the design and management of related information systems to advance the understanding and practice of healthcare, public health, consumer health and biomedical research. As a discipline, health informatics sits at the intersection of several fields of research, including health and biomedical science, information and computer science, and sociotechnical and cognitive sciences. This course provides students with the basic knowledge and skills to pursue a career in this rapidly growing field and apply informatics methods and tools in their health professional practice.
Course Director: Samprit Banerjee, PhD, Mstat
The goal of this course is to introduce students to innovative research in Population Health Sciences conducted by various faculty at Weill Cornell and MSKCC as well as leaders in the field of PHS beyond our hedges. The seminars will cover a variety of current topics and insightful perspectives in the field that will deepen students’ analytic skills and appreciation for the complexities of PHS. Additional sessions will include a journal club-style series with student-led discussions facilitated by a faculty member to critically examine findings and methods in population health sciences, review literature on specific topics, and propose areas of new research directions.
Students must attend at least 8 seminars in the semester. Assessment is based on satisfactory attendance, participation, discussion, and written assignments.
Course Director: Laura Pinheiro, PhD, MPH
This course will introduce students to the multiple determinants of health including medical care, socioeconomic status, the physical environment and their interactions and the role of multiple determinants in reducing health disparities. In addition, the course will also introduce individual behavior as a determinant of health (behavioral health) and its role in population health disparities. Also covered will be the definition and measurement of population health, conceptualizing and evaluating the multiple causes of health disparities and valuing population health interventions that intend to reduce health disparities.
Course Director: Debra D’Angelo
This course covers tools that students will need to create, manage and maximize value from big databases. The emphasis is on design and implementation of relational databases and the use of Structured Query Language (SQL). At the end of this course, students will be able to explain the requirements for handling large and complex datasets; be able to design, build, and query a relational database; and understand how relational databases and big-data targeted tools complement one another.
Course Director: Samprit Banerjee, PhD, MStat
There has been an explosion of big data in medicine and healthcare. There are four main sources of such big data – 1) administrative databases in healthcare such as electronic health records and health insurance claims, 2) biomedical imaging (e.g. MRI, CT-Scan, X-ray etc.) 3) sensors in smartphones, wearable and implantable devices and 4) genetics and genomics. It is difficult to navigate and critically assess the statistical methods and analytic tools that are needed to conduct analytics and research with such big biomedical data. This course will introduce the four above-mentioned important sources of big data in medical studies, discuss the nuances and intricacies of how such data are generated and introduce tools to navigate such databases visualize and describe them.
Course Director: Lisa Kern MD, MPH
The goal of this course is to educate students about the complexity and nuances of healthcare delivery. The course will be especially useful for non-clinicians who intend to go into fields that will require a detailed understanding of healthcare. Class sessions will not summarize healthcare; rather, they will analyze healthcare – through themes such as people, time, money, communication, uncertainty, and others. Students will come away from the course with a deeper appreciation of why it is difficult to change healthcare. They will then be able to anticipate the intended and unintended consequences of interventions and policies that they and others might implement.
Course Director: Fei Wang, PhD
Introduces students to a variety of analytic methods for health data using computational tools. The course covers topics in data mining, machine learning, classification, clustering and prediction. Students engage in hands-on exercises using a popular collection of data mining algorithms.
Course Director: Yifan Peng, PhD
This course introduces students to the field of natural language processing (NLP), applied to the health domain. NLP focuses on text data, which lacks the structure of conventional tabular data. In the health domain text is abundant in electronic health records, the medical literature and on the Web. Important applications of NLP include information extraction (pulling facts out of text) and information retrieval (searching through a collection of texts). The course presents methods for working with text: identifying the elements (words and symbols), recognizing sentence boundaries, parsing syntactic structures, assigning meaning, and establishing the structure of the discourse as a whole. The students build skills with these methods through laboratory work.
Course Director: Samuel Solomon
The US healthcare system is in the midst of transformational changes that have been catalyzed in part by the continued effects of the Affordable Care Act and the 2008 recession. This course will look at the major trends occurring in healthcare from a provider viewpoint, how leaders are both responding to and anticipating these changes, and how these changes will shape the healthcare system of the future. The goal of this course is to provide students with an understanding of the nature and context of the changes happening in healthcare, while also offering real-world perspectives from industry leaders who will speak to how they are adapting to and even shaping these changes in their roles. Upon completing this course, both clinical and non-clinical students will have gained greater insight into the healthcare
Prerequisites: Biostatistics I or Introduction to Biostatistics
The cost effectiveness analysis course is a 2 part course. The first part provides an overview of techniques used to understand medical decision making under uncertainty. Participants will learn how to structure decision analysis questions, construct decision trees, and analyze outcomes using probability. The second part provides an in-depth exposure to techniques used to conduct economic evaluations of health care technologies and programs. Participants learn how to critique economic evaluations using cost effectiveness approaches and are introduced to tools they can use to apply these techniques in their own research projects.
Course Director: Yuhua Bao, PhD
Economic incentives embedded in the health care system shape the behaviors of key stakeholders. This course provides an overview and analysis of incentives in the current US health care system for consumers/patients, health care providers, payers and insurers, and other stakeholders such as pharmaceutical and medical device companies. Discussion centers around how the medical care market differs from markets for other goods and services and how incentives interact to affect health care delivery and outcomes. We then use the lens of incentives to examine the rationale and consequences – both intended and unintended – of major reform models designed to align incentives with improving the quality and experience of care while containing the growth of health care costs.