PPT-Rerandomization in Randomized Experiments

Kari Lock and Don Rubin Harvard University JSM 2010 The Gold Standard Why are randomized experiments so good They yield unbiased estimates of the treatment effect They eliminate confounding factors. CS648. . Lecture 3. Two fundamental problems. Balls into bins. Randomized Quick Sort. Random Variable and Expected . value. 1. Balls into BINS. Calculating probability of some interesting events. 2. Dr. Kari Lock Morgan. Collecting Data: Randomized Experiments. SECTION 1.3. . Randomized Experiments. Exercise and the Brain. A study found that elderly people who walked at least a mile a day had significantly higher brain volume (gray matter related to reasoning) and significantly lower rates of Alzheimer’s and dementia compared to those who walked less. CS648. . Lecture 6. Reviewing the last 3 lectures. Application of Fingerprinting Techniques. 1-dimensional Pattern matching. . Preparation for the next lecture.. . 1. Randomized Algorithms . discussed till now. Finding . True Program Impacts Through. Randomization. Session overview. Background. What is a randomized experiment?. Why randomize?. Key Takeaways. Materials used from MIT Open Courseware . http://ocw.mit.edu. . True Experiments. Characteristics. Threats to validity controlled by experiments. Threats not controlled by experiments. Obstacles to true experiments in the field. . Quasi-experiments. The logic of quasi-experiments . “Randomized Evaluations” . Jim Berry. Asst. Professor of Economics. Cornell University. Types of impact evaluation methods. Randomized Evaluations . Also known as:. Random Assignment Studies . Randomized Field Trials . Data Collection: . Experiments and Observational Studies. 1/23/12. Association. versus Causation. Confounding Variables. Observational Studies . vs. Experiments. Randomized Experiments. Section 1.3. Honors advanced algebra. Presentation 1-4. vocabulary. Individuals. – . People, animals, or objects that are described by data.. Variables. – . Characteristics used to describe individuals.. Treatment Group. RELATED TO NUCLEAR DATA. FOR FAST REACTOR APLICATIONS. DEN/CAD/DER/SPRC/. LEPh. . |. . Pierre Leconte. , David Bernard. , Cyrille de Saint Jean. DEN/. CAD/. DER/. SPEx. /LPE . |. Patrick Blaise, Benoit . Design and Analysis. STA 320. Design and Analysis of Causal Studies. Dr. Kari Lock Morgan and Dr. Fan Li. Department of Statistical Science. Duke University. Covariates. Pre-treatment variables. X: n x k covariate matrix. 9/4/12. Collecting Data: Experiments. SECTION 1.3. . Association versus Causation. Confounding Variables. Observational Studies . vs. Experiments. Randomized Experiments. http://www.dukechronicle.com/article/teenage-marijuana-use-causes-mental-deficit. Experiments and Observational Studies. 1/23/12. Association. versus Causation. Confounding Variables. Observational Studies . vs. Experiments. Randomized Experiments. Section 1.3. Professor Kari Lock Morgan. Toseland. Using Big Data To Solve . Economic . and Social Problems. Photo Credit: Florida Atlantic University. Note: Lighter Color = More Upward Mobility. Download Statistics for Your Area at www.equality-of-opportunity.org. Wendy . Parulekar. MD, FRCP(C). Wei Tu PhD. Objectives. To review the classification of randomized phase II trial designs. To propose and critique potential  randomized phase II trials designs for a concept in head and neck cancer (case scenario to be presented).