## Results for EPI |
21 courses |

Students undertake investigations sponsored by individual faculty members. Prerequisite: consent of instructor.

Terms: Aut, Win, Spr, Sum
| Units: 1-18
| Repeatable
for credit
(up to 99 units total)

Instructors: ; Assimes, T. (PI); Baiocchi, M. (PI); Bondy, M. (PI); Carmichael, S. (PI); Chertow, G. (PI); Cullen, M. (PI); Fisher, P. (PI); Goodman, S. (PI); Halpern-Felsher, B. (PI); Henderson, V. (PI); Hsing, A. (PI); Ioannidis, J. (PI); John, E. (PI); King, A. (PI); Kurian, A. (PI); LaBeaud, D. (PI); Lee, J. (PI); Linos, E. (PI); Lu, Y. (PI); Luby, S. (PI); Maahs, D. (PI); Maldonado, Y. (PI); Nelson, L. (PI); Odden, M. (PI); Palaniappan, L. (PI); Parsonnet, J. (PI); Popat, R. (PI); Rehkopf, D. (PI); Robinson, T. (PI); Rosas, L. (PI); Sainani, K. (PI); Sanders, L. (PI); Shaw, G. (PI); Simard, J. (PI); Stefanick, M. (PI); Whittemore, A. (PI)

Open to graduate, medical, and undergraduate students. Appraisal of the quality and credibility of research findings; evaluation of sources of bias. Meta-analysis as a quantitative (statistical) method for combining results of independent studies. Examples from medicine, epidemiology, genomics, ecology, social/behavioral sciences, education. Collaborative analyses. Project involving generation of a meta-research project or reworking and evaluation of an existing published meta-analysis. Prerequisite: knowledge of basic statistics.

Terms: Win
| Units: 3

Instructors: ; Ioannidis, J. (PI)

(Formerly HRP 214) Step-by-step through the process of writing and publishing a scientific manuscript. How to write effectively, concisely, and clearly in preparation of an actual scientific manuscript. Students are encouraged to bring a manuscript on which they are currently working to develop and polish throughout the course. Please note 3-units students will additionally write and revise a manuscript.

Terms: Win
| Units: 2-3

Instructors: ; Sainani, K. (PI); Nepomuceno, A. (TA)

(Formerly HRP 219) New technologies designed to monitor and improve health outcomes are constantly emerging, but most fail in the clinic and in the marketplace because relatively few are supported by reliable, reproducible evidence that they produce a health benefit. This course covers the designs and methods that should be used to evaluate technologies to diagnose patients, predict prognosis or other health events, or screen for disease. These technologies can include devices, statistical prediction rules, biomarkers, gene panels, algorithms, imaging, or any information used to predict a future or a previously unknown health state. Specific topics to be covered include the phases of test development, how to frame a proper evaluation question, measures of test accuracy, Bayes theorem, internal and external validation, prediction evaluation criteria, decision analysis, net-utility, ROC curves, c-statistics, net reclassification index, decision curves and reporting standards. Examples of technology assessments and original methods papers are used. Knowledge of statistical software is not required, although facility with at least Excel for basic calculations is needed. Open to students with an understanding of introductory biostatistics, epidemiologic and clinical research study design. Undergraduates may enroll with consent of instructor.

Terms: Win
| Units: 3

Instructors: ; Goodman, S. (PI)

This course presents fundamental concepts and methods in genetic epidemiology, with examples on genetic studies of chronic diseases, including cancer, cardiovascular disease, metabolic conditions, and autoimmune diseases. It will provide an overview of various study designs, including family studies, and it covers fundamental analyses, inferences, and their strengths and limitations. It will include topics such as assessing genetic influences on disease; advances in genomics technology; family based study designs for linkage, exome sequencing and case-parent trios; candidate gene and genome-wide association studies of both common and rare genetic variants; gene-environment interactions, epistasis and non-Mendelian genetics; software and web-based data resources; ethical issues in genetic epidemiology; and applications of genetic epidemiology to clinical practice and public health. Guest speakers will discuss these concepts through the lens of various chronic diseases. Prerequisite: introductory biostatistics or epidemiology, biology, and genetics. Biostatistics (intro) or epidemiology (intro), biology, genetics (intro)

Terms: Win
| Units: 3

Instructors: ; Hsing, A. (PI)

(Formerly HRP 226) The principles of study design, measurement, confounding, effect modification, and strategies for minimizing bias in clinical and epidemiologic studies. Prerequisite: 225 or consent of instructor.

Terms: Win
| Units: 3

Instructors: ; Simard, J. (PI); Hinman, J. (TA)

Preference to trainees awarded Stanford internal KL2, TL1 grants. Focus is on students and junior faculty who have received a CTSA KL2 or TL1 Award. Discussions include progress and challenges involved in starting and conducting clinical research, current courses, time management and resources; support from peers; education and professional development. All scholars are required to attend a weekly seminar series meeting throughout the year that will cover an array of cross-cutting methodological topics with published examples of implementation. Prerequisite: Awarded a CTSA KL2, TL1 Grant or Spectrum UL1

Terms: Aut, Win, Spr, Sum
| Units: 1
| Repeatable
8 times
(up to 8 units total)

Instructors: ; Goodman, S. (PI)

(Formerly HRP 231) Principles of the transmission of the infectious agents (viruses, bacteria, rickettsiae, mycoplasma, fungi, and protozoan and helminth parasites). The role of vectors, reservoirs, and environmental factors. Pathogen and host characteristics that determine the spectrum of infection and disease. Endemicity, outbreaks, and epidemics of selected infectious diseases. Principles of control and surveillance.

Terms: Win
| Units: 3

(Formerly HRP 236) Weekly forum for ongoing epidemiologic research by faculty, staff, guests, and students, emphasizing research issues relevant to disease causation, prevention, and treatment. May be repeated for credit.

Terms: Aut, Win, Spr
| Units: 1
| Repeatable
15 times
(up to 15 units total)

(Formerly IPS 290 and HRP 237) How do you come up with an idea for a useful research project in a low resource setting? How do you develop a research question, prepare a concept note, and get your project funded? How do you manage personnel in the field, complex cultural situations, and unexpected problems? How do you create a sampling strategy, select a study design, and ensure ethical conduct with human subjects? This course takes students through the process of health research in under-resourced countries from the development of the initial research question and literature review to securing support and detailed planning for field work. Students progressively develop and receive weekly feedback on a concept note to support a funding proposal addressing a research question of their choosing. Aimed at graduate students interested in global health research, though students of all disciplines interested in practical methods for research are welcome. Undergraduates who have completed 85 units or more may enroll with instructor consent. Sign up for 1 unit credit to participate in class sessions or 3 units to both participate in classes and develop a concept note.

Terms: Win
| Units: 1-3

Instructors: ; Luby, S. (PI)

(Formerly HRP 238) The historical, contemporary, and future research and practice among genetics, epidemiology, clinical medicine, and public health as a source of insight for medicine and public health. Genetic and environmental contributions to multifactorial diseases; multidisciplinary approach to enhancing detection and diagnosis. The impact of the Human Genome Project on analysis of cardiovascular and neurological diseases, and cancer. Ethical and social issues in the use of genetic information. This course must be taken for a minimum of 3 units and a letter grade to be eligible for Ways credit. Prerequisites:Human Biology core or Biology Foundations or consent of instructor.

Terms: Win
| Units: 2-3

Instructors: ; Popat, R. (PI); Cooper, D. (TA)

See http://rogosateaching.com/stat209/. Application of potential outcomes formulation for causal inference to research settings including: mediation, compliance adjustments, time-1 time-2 designs, encouragement designs, heterogeneous treatment effects, aggregated data, instrumental variables, analysis of covariance regression adjustments, and implementations of matching methods. Prerequisite: STATS 209A/MSE 327 or other introduction to causal inference methods. (Formerly HRP 239)

Terms: Win
| Units: 2

Instructors: ; Rogosa, D. (PI); Lemhadri, I. (TA)

(Formerly HRP 244) The focus of this course is on providing the skills necessary to develop, validate and administer both qualitative and quantitative measures and instruments. Topics will include creating valid measures, ensuring the measures used address and apply to the research questions, design and samples; determining when to use standardized measures or develop new ones; instrument validation techniques; factor analysis; and survey administration, including determining the most effective way of administering measures (e.g., online, paper-and-pencil, ACASI) and the best way to design the survey.

Terms: Win
| Units: 2

Instructors: ; Halpern-Felsher, B. (PI)

How can one practice evidence-based medicine and make evidence-based decisions for clinical practice and policy making? Using pivotal papers published in the recent scientific literature addressing important clinical questions on diverse medical topics, we will probe a wide range of types of studies, types of targeted therapeutic or preventive interventions, and types of studied outcomes (effectiveness and/or safety), including RCTs, observational studies, epidemiologic surveillance studies, systematic reviews-umbrella reviews-meta-analyses-meta-analyses of individual patient data, studies on the evaluation of diagnostic tests and prognostic models, economic analyses studies, and guidelines. Students enrolled for 4 units will complete an additional project or other engagement approved by the instructor. MD studies enroll for +/-. GR students enroll for Letter grade.

Terms: Win
| Units: 3-4

Instructors: ; Contopoulos-Ioannidis, D. (PI)

(Formerly HRP 261) Methods for analyzing data from case-control and cross-sectional studies: the 2x2 table, chi-square test, Fisher's exact test, odds ratios, Mantel-Haenzel methods, stratification, tests for matched data, logistic regression, conditional logistic regression. Emphasis is on data analysis in SAS or R. Special topics: cross-fold validation and bootstrap inference.

Terms: Win
| Units: 3

Instructors: ; Sainani, K. (PI); Sigurdson, M. (TA)

Preference to graduate students with prior coursework in Epidemiology. Focuses on understanding the theory and empirical evidence that shows support for the relationships between social environments and health. Covers four main topics: the historical development of social epidemiology, and a survey of the major theories in social epidemiology; the three main empirical approaches used to generate new knowledge in social epidemiology: traditional observational studies, quasi-experimental studies and experimental approaches; how the constructs of social class, race/ethnicity and gender are used in social epidemiology; new emerging empirical approaches within the field including the application of causal, machine learning and complex systems methods.

Terms: Win
| Units: 2

Instructors: ; Rehkopf, D. (PI)

The course will consist of an introduction to the fundamentals of clinical research at Stanford, including the science of clinical research (design and analysis) and logistics (GCP, data management, regulatory). Material will be covered in approximately 4-6 3 hour sessions per quarter.

Terms: Win
| Units: 1

Instructors: ; Goodman, S. (PI)

Epidemiology, preventive medicine, medical genetics, public health, occupational or environmental medicine, international health, or related fields. May be repeated for credit. Prerequisite: consent of instructor.

Terms: Aut, Win, Spr, Sum
| Units: 1-18
| Repeatable
for credit

Instructors: ; Assimes, T. (PI); Baiocchi, M. (PI); Bondy, M. (PI); Carmichael, S. (PI); Chertow, G. (PI); Cullen, M. (PI); Fisher, P. (PI); Goodman, S. (PI); Halpern-Felsher, B. (PI); Henderson, V. (PI); Hsing, A. (PI); Ioannidis, J. (PI); John, E. (PI); King, A. (PI); Kurian, A. (PI); LaBeaud, D. (PI); Lee, J. (PI); Linos, E. (PI); Lu, Y. (PI); Luby, S. (PI); Maahs, D. (PI); Maldonado, Y. (PI); Nelson, L. (PI); Odden, M. (PI); Palaniappan, L. (PI); Parsonnet, J. (PI); Popat, R. (PI); Rehkopf, D. (PI); Robinson, T. (PI); Rosas, L. (PI); Sainani, K. (PI); Sanders, L. (PI); Shaw, G. (PI); Simard, J. (PI); Stefanick, M. (PI); Whittemore, A. (PI)

Investigations sponsored by individual faculty members. Prerequisite: consent of instructor.

Terms: Aut, Win, Spr, Sum
| Units: 1-18
| Repeatable
for credit
(up to 99 units total)

Instructors: ; Assimes, T. (PI); Baiocchi, M. (PI); Bondy, M. (PI); Carmichael, S. (PI); Chertow, G. (PI); Cullen, M. (PI); Fisher, P. (PI); Goodman, S. (PI); Halpern-Felsher, B. (PI); Han, S. (PI); Henderson, V. (PI); Hsing, A. (PI); Ioannidis, J. (PI); John, E. (PI); King, A. (PI); Kurian, A. (PI); LaBeaud, D. (PI); Lee, J. (PI); Linos, E. (PI); Lu, Y. (PI); Luby, S. (PI); Maahs, D. (PI); Maldonado, Y. (PI); Nelson, L. (PI); Odden, M. (PI); Palaniappan, L. (PI); Parsonnet, J. (PI); Popat, R. (PI); Rehkopf, D. (PI); Robinson, T. (PI); Rosas, L. (PI); Sainani, K. (PI); Sanders, L. (PI); Shaw, G. (PI); Simard, J. (PI); Stefanick, M. (PI); Whittemore, A. (PI)

Terms: Aut, Win, Spr, Sum
| Units: 0
| Repeatable
for credit
(up to 99 units total)

Instructors: ; Assimes, T. (PI); Baiocchi, M. (PI); Bondy, M. (PI); Carmichael, S. (PI); Chertow, G. (PI); Cullen, M. (PI); Fisher, P. (PI); Goodman, S. (PI); Halpern-Felsher, B. (PI); Han, S. (PI); Henderson, V. (PI); Hsing, A. (PI); Ioannidis, J. (PI); John, E. (PI); King, A. (PI); Kurian, A. (PI); LaBeaud, D. (PI); Lee, J. (PI); Linos, E. (PI); Lu, Y. (PI); Luby, S. (PI); Maahs, D. (PI); Maldonado, Y. (PI); Nelson, L. (PI); Odden, M. (PI); Palaniappan, L. (PI); Parsonnet, J. (PI); Popat, R. (PI); Rehkopf, D. (PI); Robinson, T. (PI); Rosas, L. (PI); Sainani, K. (PI); Sanders, L. (PI); Shaw, G. (PI); Simard, J. (PI); Stefanick, M. (PI); Whittemore, A. (PI)

Terms: Aut, Win, Spr, Sum
| Units: 0
| Repeatable
for credit
(up to 99 units total)

Instructors: ; Assimes, T. (PI); Baiocchi, M. (PI); Bondy, M. (PI); Carmichael, S. (PI); Chertow, G. (PI); Cullen, M. (PI); Fisher, P. (PI); Goodman, S. (PI); Halpern-Felsher, B. (PI); Han, S. (PI); Henderson, V. (PI); Hsing, A. (PI); Ioannidis, J. (PI); John, E. (PI); King, A. (PI); Kurian, A. (PI); LaBeaud, D. (PI); Lee, J. (PI); Linos, E. (PI); Lu, Y. (PI); Luby, S. (PI); Maahs, D. (PI); Maldonado, Y. (PI); Nelson, L. (PI); Odden, M. (PI); Palaniappan, L. (PI); Parsonnet, J. (PI); Popat, R. (PI); Rehkopf, D. (PI); Robinson, T. (PI); Rosas, L. (PI); Sainani, K. (PI); Sanders, L. (PI); Shaw, G. (PI); Simard, J. (PI); Stefanick, M. (PI); Whittemore, A. (PI)