Randomized Controlled Trials, Development Economics and Policy Making in Developing Countries
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Introduction to Biostatistics
Introduction to Biostatistics Jie Yang, Ph.D. Associate Professor Department of Family, Population and Preventive Medicine Director Biostatistical Consulting Core Director Biostatistics and Bioinformatics Shared Resource, Stony Brook Cancer Center In collaboration with Clinical Translational Science Center (CTSC) and the Biostatistics and Bioinformatics Shared Resource (BB-SR), Stony Brook Cancer Center (SBCC). OUTLINE What is Biostatistics What does a biostatistician do • Experiment design, clinical trial design • Descriptive and Inferential analysis • Result interpretation What you should bring while consulting with a biostatistician WHAT IS BIOSTATISTICS • The science of (bio)statistics encompasses the design of biological/clinical experiments the collection, summarization, and analysis of data from those experiments the interpretation of, and inference from, the results How to Lie with Statistics (1954) by Darrell Huff. http://www.youtube.com/watch?v=PbODigCZqL8 GOAL OF STATISTICS Sampling POPULATION Probability SAMPLE Theory Descriptive Descriptive Statistics Statistics Inference Population Sample Parameters: Inferential Statistics Statistics: 흁, 흈, 흅… 푿ഥ , 풔, 풑ෝ,… PROPERTIES OF A “GOOD” SAMPLE • Adequate sample size (statistical power) • Random selection (representative) Sampling Techniques: 1.Simple random sampling 2.Stratified sampling 3.Systematic sampling 4.Cluster sampling 5.Convenience sampling STUDY DESIGN EXPERIEMENT DESIGN Completely Randomized Design (CRD) - Randomly assign the experiment units to the treatments -
Experimentation Science: a Process Approach for the Complete Design of an Experiment
Kansas State University Libraries New Prairie Press Conference on Applied Statistics in Agriculture 1996 - 8th Annual Conference Proceedings EXPERIMENTATION SCIENCE: A PROCESS APPROACH FOR THE COMPLETE DESIGN OF AN EXPERIMENT D. D. Kratzer K. A. Ash Follow this and additional works at: https://newprairiepress.org/agstatconference Part of the Agriculture Commons, and the Applied Statistics Commons This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License. Recommended Citation Kratzer, D. D. and Ash, K. A. (1996). "EXPERIMENTATION SCIENCE: A PROCESS APPROACH FOR THE COMPLETE DESIGN OF AN EXPERIMENT," Conference on Applied Statistics in Agriculture. https://doi.org/ 10.4148/2475-7772.1322 This is brought to you for free and open access by the Conferences at New Prairie Press. It has been accepted for inclusion in Conference on Applied Statistics in Agriculture by an authorized administrator of New Prairie Press. For more information, please contact [email protected]. Conference on Applied Statistics in Agriculture Kansas State University Applied Statistics in Agriculture 109 EXPERIMENTATION SCIENCE: A PROCESS APPROACH FOR THE COMPLETE DESIGN OF AN EXPERIMENT. D. D. Kratzer Ph.D., Pharmacia and Upjohn Inc., Kalamazoo MI, and K. A. Ash D.V.M., Ph.D., Town and Country Animal Hospital, Charlotte MI ABSTRACT Experimentation Science is introduced as a process through which the necessary steps of experimental design are all sufficiently addressed. Experimentation Science is defined as a nearly linear process of objective formulation, selection of experimentation unit and decision variable(s), deciding treatment, design and error structure, defining the randomization, statistical analyses and decision procedures, outlining quality control procedures for data collection, and finally analysis, presentation and interpretation of results. -
The Influence of Randomized Controlled Trials on Development Economics Research and on Development Policy
The Influence of Randomized Controlled Trials on Development Economics Research and on Development Policy Paper prepared for “The State of Economics, The State of the World” Conference proceedings volume Abhijit Vinayak Banerjee Esther Duflo Michael Kremer12 September 11, 2016 Many (though by no means all) of the questions that development economists and policymakers ask themselves are causal in nature: What would be the impact of adding computers in classrooms? What is the price elasticity of demand for preventive health products? Would increasing interest rates lead to an increase in default rates? Decades ago, the statistician Fisher proposed a method to answer such causal questions: Randomized Controlled Trials (RCT) (Fisher, 1925). In an RCT, the assignment of different units to different treatment groups is chosen randomly. This insures that no unobservable characteristics of the units is reflected in the assignment, and hence that any difference between treatment and control units reflects the impact of the treatment. While the idea is simple, the implementation in the field can be more involved, and it took some time before randomization was considered to be a practical tool for answering questions in social science research in general, and in development economics more specifically. 1 Abhijit Banerjee and Esther Duflo are in the department of economics at MIT and co-director of J-PAL Michael Kremer is in the department of economics at Harvard and serves as part-time Scientific Director of Development Innovation Ventures at USAID, which has also funded research by both Banerjee and Duflo. The views expressed in this document reflect the personal opinions of the author and are entirely the author’s own. -
Understanding Development and Poverty Alleviation
14 OCTOBER 2019 Scientific Background on the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2019 UNDERSTANDING DEVELOPMENT AND POVERTY ALLEVIATION The Committee for the Prize in Economic Sciences in Memory of Alfred Nobel THE ROYAL SWEDISH ACADEMY OF SCIENCES, founded in 1739, is an independent organisation whose overall objective is to promote the sciences and strengthen their influence in society. The Academy takes special responsibility for the natural sciences and mathematics, but endeavours to promote the exchange of ideas between various disciplines. BOX 50005 (LILLA FRESCATIVÄGEN 4 A), SE-104 05 STOCKHOLM, SWEDEN TEL +46 8 673 95 00, [email protected] WWW.KVA.SE Scientific Background on the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2019 Understanding Development and Poverty Alleviation The Committee for the Prize in Economic Sciences in Memory of Alfred Nobel October 14, 2019 Despite massive progress in the past few decades, global poverty — in all its different dimensions — remains a broad and entrenched problem. For example, today, more than 700 million people subsist on extremely low incomes. Every year, five million children under five die of diseases that often could have been prevented or treated by a handful of proven interventions. Today, a large majority of children in low- and middle-income countries attend primary school, but many of them leave school lacking proficiency in reading, writing and mathematics. How to effectively reduce global poverty remains one of humankind’s most pressing questions. It is also one of the biggest questions facing the discipline of economics since its very inception. -
Ec 1530 Reading List Becker Chapters 1 and 2 J. Angrist and A
Ec 1530 Reading List Becker Chapters 1 and 2 J. Angrist and A. Krueger "Instrumental variables and the search for identification: From supply and demand to natural experiments" J of Economic Perspectives 15(4):69‐85 2001 Banerjee and Duflo, 2011, Chapters 1 and 2 Pitt, Mark, "Food Preferences and Nutrition in Rural Bangladesh," Review of Economics and Statistics, February 1983, 105‐114. [JSTOR] Jensen, Robert and Nolan Miller (2008). “Giffen Behavior and Subsistence Consumption,” American Economic Review, 98(4), p. 1553 − 1577. [jstor] M. Ravallion, "The performance of rice markets in Bangladesh during the 1974 famine", Oxford Economic Journal 95 (377): 15‐29 A.D. Foster, "Prices, Credit Constraints, and Child Growth in Rural Bangladesh", Economic. Journal, 105(430): 551‐570, May 1995 JM Cunha, G DeGiorgi, S Jayachandran, NBER 17456The Price Effects of Cash Versus In‐Kind Transfers Banerjee and Duflo, Chapter 3 'Bliss, Christopher and N.H. Stern, "Productivity, Wages and Nutrition, Part I Journal of Development Economics, 1978, 331‐398. [E‐journal] J. Strauss, "Does better nutrion raise farm productivity", JPE, 94(2) 297‐320. Foster, Andrew D. and Mark R. Rosenzweig, "A Test for Moral Hazard in the Labor Market: Contractual Arrangements, Effort and Health," Review of Economic and Statistics, May 1994, 213‐227. [JSTOR] Banerjee and Duflo Chapter 4 Andrew Foster and Mark Rosenzweig, "Technical change and human capital returns and investments: Evidence from the Green Revoloution", American Economic Review 86(4): 931‐53 [jstor] Esther Duflo "Schooling and labor market consequences of school construction in Indonesia: Evidence from an unusual policy experement", American Economic Review 91(4):795‐813 [jstor] Jensen, Robert (2010). -
Rohini Pande
ROHINI PANDE 27 Hillhouse Avenue 203.432.3637(w) PO Box 208269 [email protected] New Haven, CT 06520-8269 https://campuspress.yale.edu/rpande EDUCATION 1999 Ph.D., Economics, London School of Economics 1995 M.Sc. in Economics, London School of Economics (Distinction) 1994 MA in Philosophy, Politics and Economics, Oxford University 1992 BA (Hons.) in Economics, St. Stephens College, Delhi University PROFESSIONAL EXPERIENCE ACADEMIC POSITIONS 2019 – Henry J. Heinz II Professor of Economics, Yale University 2018 – 2019 Rafik Hariri Professor of International Political Economy, Harvard Kennedy School, Harvard University 2006 – 2017 Mohammed Kamal Professor of Public Policy, Harvard Kennedy School, Harvard University 2005 – 2006 Associate Professor of Economics, Yale University 2003 – 2005 Assistant Professor of Economics, Yale University 1999 – 2003 Assistant Professor of Economics, Columbia University VISITING POSITIONS April 2018 Ta-Chung Liu Distinguished Visitor at Becker Friedman Institute, UChicago Spring 2017 Visiting Professor of Economics, University of Pompeu Fabra and Stanford Fall 2010 Visiting Professor of Economics, London School of Economics Spring 2006 Visiting Associate Professor of Economics, University of California, Berkeley Fall 2005 Visiting Associate Professor of Economics, Columbia University 2002 – 2003 Visiting Assistant Professor of Economics, MIT CURRENT PROFESSIONAL ACTIVITIES AND SERVICES 2019 – Director, Economic Growth Center Yale University 2019 – Co-editor, American Economic Review: Insights 2014 – IZA -
Natural Experiments
Natural Experiments Jason Seawright [email protected] August 11, 2010 J. Seawright (PolSci) Essex 2010 August 11, 2010 1 / 31 Typology of Natural Experiments J. Seawright (PolSci) Essex 2010 August 11, 2010 2 / 31 Typology of Natural Experiments Classic Natural Experiment J. Seawright (PolSci) Essex 2010 August 11, 2010 2 / 31 Typology of Natural Experiments Classic Natural Experiment Instrumental Variables-Type Natural Experiment J. Seawright (PolSci) Essex 2010 August 11, 2010 2 / 31 Typology of Natural Experiments Classic Natural Experiment Instrumental Variables-Type Natural Experiment Regression-Discontinuity Design J. Seawright (PolSci) Essex 2010 August 11, 2010 2 / 31 Bolstering Understand assumptions. J. Seawright (PolSci) Essex 2010 August 11, 2010 3 / 31 Bolstering Understand assumptions. Explore role of qualitative evidence. J. Seawright (PolSci) Essex 2010 August 11, 2010 3 / 31 Classic Natural Experiment J. Seawright (PolSci) Essex 2010 August 11, 2010 4 / 31 Classic Natural Experiment 1 “Nature” randomizes the treatment. J. Seawright (PolSci) Essex 2010 August 11, 2010 4 / 31 Classic Natural Experiment 1 “Nature” randomizes the treatment. 2 All (observable and unobservable) confounding variables are balanced between treatment and control groups. J. Seawright (PolSci) Essex 2010 August 11, 2010 4 / 31 Classic Natural Experiment 1 “Nature” randomizes the treatment. 2 All (observable and unobservable) confounding variables are balanced between treatment and control groups. 3 No discretion is involved in assigning treatments, or the relevant information is unavailable or unused. J. Seawright (PolSci) Essex 2010 August 11, 2010 4 / 31 Classic Natural Experiment 1 “Nature” randomizes the treatment. 2 All (observable and unobservable) confounding variables are balanced between treatment and control groups. -
Esther Duflo
Policies, Politics: Can Evidence Play a Role in the Fight against Poverty? Esther Duflo The Sixth Annual Richard H. Sabot Lecture A p r i l 2 0 1 1 The Center for Global Development The Richard H. Sabot Lecture Series The Richard H. Sabot Lecture is held annually to honor the life and work of Richard “Dick” Sabot, a respected professor, celebrated development economist, successful internet entrepreneur, and close friend of the Center for Global Development who died suddenly in July 2005. As a founding member of CGD’s board of directors, Dick’s enthusiasm and intellect encouraged our beginnings. His work as a scholar and as a development practitioner helped to shape the Center’s vision of independent research and new ideas in the service of better development policies and practices. Dick held a PhD in economics from Oxford University; he was Professor of Economics at Williams College and taught previously at Yale University, Oxford University, and Columbia University. His contributions to the fields of economics and international development were numerous, both in academia and during ten years at the World Bank. The Sabot Lecture Series hosts each year a scholar-practitioner who has made significant contributions to international development, combining, as did Dick, academic work with leadership in the policy community. We are grateful to the Sabot family and to CGD board member Bruns Grayson for the support to launch the Richard H. Sabot Lecture Series. Previous Lectures 2010 Kenneth Rogoff, “Austerity and the IMF.” 2009 Kemal Derviş, “Precautionary Resources and Long-Term Development Finance.” 2008 Lord Nicholas Stern, “Towards a Global Deal on Climate Change.” 2007 Ngozi Okonjo-Iweala, “Corruption: Myths and Reality in a Developing Country Context.” 2006 Lawrence H. -
Internal and External Validity: Can You Apply Research Study Results to Your Patients? Cecilia Maria Patino1,2,A, Juliana Carvalho Ferreira1,3,B
J Bras Pneumol. 2018;44(3):183-183 CONTINUING EDUCATION: http://dx.doi.org/10.1590/S1806-37562018000000164 SCIENTIFIC METHODOLOGY Internal and external validity: can you apply research study results to your patients? Cecilia Maria Patino1,2,a, Juliana Carvalho Ferreira1,3,b CLINICAL SCENARIO extent to which the results of a study are generalizable to patients in our daily practice, especially for the population In a multicenter study in France, investigators conducted that the sample is thought to represent. a randomized controlled trial to test the effect of prone vs. supine positioning ventilation on mortality among patients Lack of internal validity implies that the results of the with early, severe ARDS. They showed that prolonged study deviate from the truth, and, therefore, we cannot prone-positioning ventilation decreased 28-day mortality draw any conclusions; hence, if the results of a trial are [hazard ratio (HR) = 0.39; 95% CI: 0.25-0.63].(1) not internally valid, external validity is irrelevant.(2) Lack of external validity implies that the results of the trial may not apply to patients who differ from the study STUDY VALIDITY population and, consequently, could lead to low adoption The validity of a research study refers to how well the of the treatment tested in the trial by other clinicians. results among the study participants represent true fi ndings among similar individuals outside the study. INCREASING VALIDITY OF RESEARCH This concept of validity applies to all types of clinical STUDIES studies, including those about prevalence, associations, interventions, and diagnosis. The validity of a research To increase internal validity, investigators should ensure study includes two domains: internal and external validity. -
The Distribution of Information in Speculative Markets: a Natural Experiment∗
The Distribution of Information in Speculative Markets: A Natural Experiment∗ Alasdair Brown† University of East Anglia June 9, 2014 ∗I would like to thank an associate editor, two anonymous referees, Fuyu Yang, and seminar participants at UEA for comments on this work. All remaining errors are my own. †School of Economics, University of East Anglia, Norwich, NR4 7TJ. Email: [email protected] 1 1 Introduction Abstract We use a unique natural experiment to shed light on the distribution of information in speculative markets. In June 2011, Betfair - a U.K. betting exchange - levied a tax of up to 60% on all future profits accrued by the top 0.1% of profitable traders. Such a move appears to have driven at least some of these traders off the exchange, taking their information with them. We investigate the effect of the new tax on the forecasting capacity of the exchange (our measure of the market’s incorporation of information into the price). We find that there was scant decline in the forecasting capacity of the exchange - relative to a control market - suggesting that the bulk of information had hitherto been held by the majority of traders, rather than the select few affected by the rule change. This result is robust to the choice of forecasting measure, the choice of forecasting interval, and the choice of race type. This provides evidence that more than a few traders are typically involved in the price discovery process in speculative markets. JEL Classification: G14, G19 Keywords: informed trading, natural experiment, betting markets, horse racing 1 Introduction There is a common perception that the benefits of trading in speculative assets are skewed towards those on the inside of corporations, or those that move in the same circles as the corporate elite. -
Should the Randomistas (Continue To) Rule?
Should the Randomistas (Continue to) Rule? Martin Ravallion Abstract The rising popularity of randomized controlled trials (RCTs) in development applications has come with continuing debates on the pros and cons of this approach. The paper revisits the issues. While RCTs have a place in the toolkit for impact evaluation, an unconditional preference for RCTs as the “gold standard” is questionable. The statistical case is unclear on a priori grounds; a stronger ethical defense is often called for; and there is a risk of distorting the evidence-base for informing policymaking. Going forward, pressing knowledge gaps should drive the questions asked and how they are answered, not the methodological preferences of some researchers. The gold standard is the best method for the question at hand. Keywords: Randomized controlled trials, bias, ethics, external validity, ethics, development policy JEL: B23, H43, O22 Working Paper 492 August 2018 www.cgdev.org Should the Randomistas (Continue to) Rule? Martin Ravallion Department of Economics, Georgetown University François Roubaud encouraged the author to write this paper. For comments the author is grateful to Sarah Baird, Mary Ann Bronson, Caitlin Brown, Kevin Donovan, Markus Goldstein, Miguel Hernan, Emmanuel Jimenez, Madhulika Khanna, Nishtha Kochhar, Andrew Leigh, David McKenzie, Berk Özler, Dina Pomeranz, Lant Pritchett, Milan Thomas, Vinod Thomas, Eva Vivalt, Dominique van de Walle and Andrew Zeitlin. Staff of the International Initiative for Impact Evaluation kindly provided an update to their database on published impact evaluations and helped with the author’s questions. Martin Ravallion, 2018. “Should the Randomistas (Continue to) Rule?.” CGD Working Paper 492. Washington, DC: Center for Global Development. -
As an "Odds Ratio", the Ratio of the Odds of Being Exposed by a Case Divided by the Odds of Being Exposed by a Control
STUDY DESIGNS IN BIOMEDICAL RESEARCH VALIDITY & SAMPLE SIZE Validity is an important concept; it involves the assessment against accepted absolute standards, or in a milder form, to see if the evaluation appears to cover its intended target or targets. INFERENCES & VALIDITIES Two major levels of inferences are involved in interpreting a study, a clinical trial The first level concerns Internal validity; the degree to which the investigator draws the correct conclusions about what actually happened in the study. The second level concerns External Validity (also referred to as generalizability or inference); the degree to which these conclusions could be appropriately applied to people and events outside the study. External Validity Internal Validity Truth in Truth in Findings in The Universe The Study The Study Research Question Study Plan Study Data A Simple Example: An experiment on the effect of Vitamin C on the prevention of colds could be simply conducted as follows. A number of n children (the sample size) are randomized; half were each give a 1,000-mg tablet of Vitamin C daily during the test period and form the “experimental group”. The remaining half , who made up the “control group” received “placebo” – an identical tablet containing no Vitamin C – also on a daily basis. At the end, the “Number of colds per child” could be chosen as the outcome/response variable, and the means of the two groups are compared. Assignment of the treatments (factor levels: Vitamin C or Placebo) to the experimental units (children) was performed using a process called “randomization”. The purpose of randomization was to “balance” the characteristics of the children in each of the treatment groups, so that the difference in the response variable, the number of cold episodes per child, can be rightly attributed to the effect of the predictor – the difference between Vitamin C and Placebo.