1. No category

‫קורסים ארציים בסטטיסטיקה לתלמידי תואר שני ושלישי‪ ,‬תשע"ז‬
‫אנו שמחים לבשר כי גם בשנת הלימודים הקרובה‪ ,‬בסמסטר א'‪ ,‬יינתנו שלושה קורסים מתקדמים בסטטיסטיקה‬
‫הפתוחים לתלמידי תואר שני ושלישי‪ .‬הקורסים יינתנו בימי חמישי‪ ,‬בזה אחר זה‪ ,‬בקמפוס של אוניברסיטת תל‬
‫אביב‪ .‬כל תלמידי המוסדות האקדמיים השונים מוזמנים להרשם ולהשתתף ללא תשלום נוסף‪ ,‬וציונם בקורסים‬
‫יועבר למוסד האם‪.‬‬
‫התוכנית‬
‫‪ ,statistical evolutionary genomics 12:00 10:00‬ד"ר אור צוק מהאוניברסיטה העברית‬
‫‪ ,selective inference 15:00 13:00‬פרופ' יואב בנימיני מאוניברסיטת תל אביב‬
‫‪ ,advanced topics in clinical trials 17:00 15:00‬פרופ' רבקה בטנסקי מאוניברסיטת הרווארד‬
‫‪ 18:00 17:00‬מפגש חברתי‪ ,‬תלמידים ומרצים‪ ,‬באסה )בירה‪ ,‬אוכל‪ ,‬סטטיסטיקה והביתה( בסבבה‪.‬‬
‫הקורס של פרופ' בטנסקי ינתן באנגלית‪ .‬יתכן שגם שני הקורסים האחרים ינתנו באנגלית‪ ,‬לפי הביקוש‪.‬‬
‫הרשמה‬
‫תלמיד‪/‬ה שאינו‪/‬ה מאוניברסיטת תל אביב מתבקש‪/‬ת למלא טופס הנקרא "טופס רישום לאוניברסיטה אחרת"‪,‬‬
‫אותו ניתן לקבל במזכירות התלמידים במוסד בו לומד‪/‬ת הסטודנט‪/‬ית‪ .‬את הטופס המלא יש למסור )באימייל או‬
‫בייד( למזכירות באוניברסיטת תל אביב‪ ,‬לידי גב' שחר ארז‪ ,‬בניין קפלון‪ ,‬קומה ‪ ,3‬חדר ‪ ,303‬אימייל‪:‬‬
‫‪shacharerez@tauex.tau.ac.il‬‬
‫מפגשים‬
‫תאריכי המפגשים יתפרסמו בהמשך‪ .‬בקורס של פרופ' בטנסקי יתקיימו רק כעשרה מפגשים )או פחות( ולכן חלקם‬
‫יהיו ארוכים יותר מהמצוין לעיל‪ .‬פרטים מדוייקים יתפרסמו בקרוב‪.‬‬
‫אוניברסיטת תל אביב תממן את הוצאות הנסיעה של המרצים והתלמידים שאינם מאוניברסיטת תל אביב‪ .‬יש‬
‫למלא את הטופס שיופץ בהמשך‪ ,‬ולהגיש את הקבלות )מקור ולא צילום(‪ .‬ללא קבלות לא ניתן יהיה לקבל‬
‫החזר הוצאות‪ .‬את הטופס עם הקבלות יש למסור לגב' נורית ליברמן‪ ,‬במזכירות הפקולטה‪.‬‬
‫לפרטים נוספים ניתן לפנות למיכה מנדל ‪ ,micha.mandel@mail.huji.ac.il‬יאיר גולדברג ‪yair.goldy@gmail.com‬‬
‫או מלכה גורפיין ‪malkago12@gmail.com‬‬
Statistical evolutionary genomics, Dr. Or Zuk, The Hewbrew University of Jerusalem
Summary: The course will introduce the students to major mathematical and statistical models
used in evolutionary genomics and population genetics, and their impact on analysis of genomic
data from an evolutionary perspective.
The first part of the course (1-3) will introduce classic evolutionary theory, and will focus on the
mathematical details and properties of the models introduced.
The second part (4-5) will focus on inference of evolutionary parameters and properties from
genomic data, with examples from the recent literature showing analysis of modern genomic
datasets.
Topics will include:
1. Basic introduction to genetics and mutational models.
2. The Fisher-Wright model and its extensions, the continuous diffusion approximation, fixation
and extinction probabilities, the allele frequency spectrum.
3. Coalescent theory: gene genealogies, recombination, selection (purifying, positive, balancing).
4. Measures and tests for natural selection:
(i) Intra-species data: F_st, Tajima's D, Ka/Ks, codon volatility, tests for recent positive
selection.
(ii) Inter-species data: Ka/Ks, constraint in multiple sequence alignment (phastcons,
phylop/siphy), lineage-specific selection.
(iii) Inter-species and intra-species data: McDonald-Kreitman test, the HKA test.
5. Modeling, detecting and exploiting correlated-evolution among adjacent genomic sites.
Selective Inference, Prof. Yoav Benjamini
Summary: With the dimension and complexity of problems being addressed in applied statistics
rapidly increasing, inference about a few parameters selected after viewing the data is
unavoidable. This raises a major challenge, as the usual properties of tests, estimators, and
confidence intervals are seriously affected. The course covers both older but mostly newer
approaches to address these problems (i) simultaneous inference methods that protect for any
possible selection, (ii) the introduction of the theory and methodology of the false discovery rate,
(iii) the many variations developed on the false discovery rate, and (iv) conditional inference. We
will discuss many application areas: Genomics, Brain Imaging, Medical Statistics, Behavioral
Genetics, and more. We shall make the connection between selective inference and the recently
surfacing replicability problem in Science.
Course Goals:
• Learn to recognize the challenges posed by multiple inferences problems, simultaneous
and selective inference, and develop critical thinking about them.
• Understand the theory and some of the newer methodology for simultaneous inference, as
well as their limitations.
• Learn to address selective inference via the False Discovery Rate (FDR) and the False
Coverage-statement Rate (FCR).
• Be able to implement FDR controlling procedures in code.
• Understand the underlying theory to the point of being able to make contributions.
• Appreciate the scope of applications where selective inference is being used and the
further challenges these applications raise to methodology and theory.
• Realize the importance of addressing selective inference in order to enhance replicability
in science.
There will be 6 assignments throughout the course, and a final project, to be submitted via email,
as a PDF or a Word file.
Clinical Trials, Prof. Rebecca Betensky, Harvard University, USA
Goals: This course will introduce students to the many facets of clinical trials, including their
justification and structure, along with many real examples that have been reported in top clinical
journals. It will delve deeply into the statistical aspects of the designs and analyses of several
types of trials, including those that involve interim monitoring.
Prerequisites:
1. Courses in Probability and Statistical Inference.
2. Proficiency in a programming language (e.g., R, Python, Matlab) will be useful for standard
calculations and for simulations.
Topics:
1. Introduction
a. History and background
b. Ethics
c. Types of trials
2. Phases of clinical trials
a. Phase I trials
b. Phase II trials
i. Statistical issues and methods
ii. Two-stage designs
c. Phase III trials
i. Ethical issues
ii. Blinding and placebos
iii. Protocol, safety and efficacy monitoring
3. Randomization
a. Design based inference
b. Fixed allocation randomization (simple, permuted block, stratified)
c. Adaptive randomization (Efron biased coin, Urn model, minimization)
d. Response adaptive randomization
4. Sample size and power: calculations and simulations
5. Multiple endpoints
6. Early stopping for efficacy or futility
a. Information based design and monitoring
b. Boundaries (Pocock, O’Brien-Fleming, spending function)
c. Analysis following a sequential test
d. Repeated confidence intervals
e. Futility boundaries
f. Multi-armed studies
7. Adaptive designs
a. Internal pilot studies
b. Response adaptive designs (e.g., SPCD)
8. Analytical issues
a. Subgroup analysis
b. Missing data
Homework: Regular homework assignments will be given and will include critical reading of
reports of clinical trials in the medical literature alongside statistical problems, calculations and
simulations.
Grades: The final grade will be based on homework and a final assignment.
References: (These are just a select few and are all optional).
1. Clinical Trials: A Methodologic Perspective. Steven Piantadosi. Wiley. 1997.
2. Fundamentals of Clinical Trials, 3 rd Edition. Lawrence M. Freidman, Curt D. Furberg, and
David L. DeMets. Springer. 1998.
3. Introduction to Statistical Methods for Clinical Trials. Thomas D. Cook and David L. DeMets.
Chapman & Hall. 2008.
4. Clinical Trials in Oncology. Stephanie Green, Jacqueline Benedetti, Angela Smith and John
Crowley. Chapman & Hall/CRC. 2012.
5. Group Sequential Methods with Applications to Clinical Trials. Christopher Jennison and
Bruce W. Turnbull. Chapman & Hall/CRC. 2000.
6. Sequential Analysis. David O. Siegmund. Springer. 1985.