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Contributors
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- By Lenard A. Adler, Pinky Agarwal, Rehan Ahmed, Jagga Rao Alluri, Fawaz Al-Mufti, Samuel Alperin, Michael Amoashiy, Michael Andary, David J. Anschel, Padmaja Aradhya, Vandana Aspen, Esther Baldinger, Jee Bang, George D. Baquis, John J. Barry, Jason J. S. Barton, Julius Bazan, Amanda R. Bedford, Marlene Behrmann, Lourdes Bello-Espinosa, Ajay Berdia, Alan R. Berger, Mark Beyer, Don C. Bienfang, Kevin M. Biglan, Thomas M. Boes, Paul W. Brazis, Jonathan L. Brisman, Jeffrey A. Brown, Scott E. Brown, Ryan R. Byrne, Rina Caprarella, Casey A. Chamberlain, Wan-Tsu W. Chang, Grace M. Charles, Jasvinder Chawla, David Clark, Todd J. Cohen, Joe Colombo, Howard Crystal, Vladimir Dadashev, Sarita B. Dave, Jean Robert Desrouleaux, Richard L. Doty, Robert Duarte, Jeffrey S. Durmer, Christyn M. Edmundson, Eric R. Eggenberger, Steven Ender, Noam Epstein, Alberto J. Espay, Alan B. Ettinger, Niloofar (Nelly) Faghani, Amtul Farheen, Edward Firouztale, Rod Foroozan, Anne L. Foundas, David Elliot Friedman, Deborah I. Friedman, Steven J. Frucht, Oded Gerber, Tal Gilboa, Martin Gizzi, Teneille G. Gofton, Louis J. Goodrich, Malcolm H. Gottesman, Varda Gross-Tsur, Deepak Grover, David A. Gudis, John J. Halperin, Maxim D. Hammer, Andrew R. Harrison, L. Anne Hayman, Galen V. Henderson, Steven Herskovitz, Caitlin Hoffman, Laryssa A. Huryn, Andres M. Kanner, Gary P. Kaplan, Bashar Katirji, Kenneth R. Kaufman, Annie Killoran, Nina Kirz, Gad E. Klein, Danielle G. Koby, Christopher P. Kogut, W. Curt LaFrance, Patrick J.M. Lavin, Susan W. Law, James L. Levenson, Richard B. Lipton, Glenn Lopate, Daniel J. Luciano, Reema Maindiratta, Robert M. Mallery, Georgios Manousakis, Alan Mazurek, Luis J. Mejico, Dragana Micic, Ali Mokhtarzadeh, Walter J. Molofsky, Heather E. Moss, Mark L. Moster, Manpreet Multani, Siddhartha Nadkarni, George C. Newman, Rolla Nuoman, Paul A. Nyquist, Gaia Donata Oggioni, Odi Oguh, Denis Ostrovskiy, Kristina Y. Pao, Juwen Park, Anastas F. Pass, Victoria S. Pelak, Jeffrey Peterson, John Pile-Spellman, Misha L. Pless, Gregory M. Pontone, Aparna M. Prabhu, Michael T. Pulley, Philip Ragone, Prajwal Rajappa, Venkat Ramani, Sindhu Ramchandren, Ritesh A. Ramdhani, Ramses Ribot, Heidi D. Riney, Diana Rojas-Soto, Michael Ronthal, Daniel M. Rosenbaum, David B. Rosenfield, Durga Roy, Michael J. Ruckenstein, Max C. Rudansky, Eva Sahay, Friedhelm Sandbrink, Jade S. Schiffman, Angela Scicutella, Maroun T. Semaan, Robert C. Sergott, Aashit K. Shah, David M. Shaw, Amit M. Shelat, Claire A. Sheldon, Anant M. Shenoy, Yelizaveta Sher, Jessica A. Shields, Tanya Simuni, Rajpaul Singh, Eric E. Smouha, David Solomon, Mehri Songhorian, Steven A. Sparr, Egilius L. H. Spierings, Eve G. Spratt, Beth Stein, S.H. Subramony, Rosa Ana Tang, Cara Tannenbaum, Hakan Tekeli, Amanda J. Thompson, Michael J. Thorpy, Matthew J. Thurtell, Pedro J. Torrico, Ira M. Turner, Scott Uretsky, Ruth H. Walker, Deborah M. Weisbrot, Michael A. Williams, Jacques Winter, Randall J. Wright, Jay Elliot Yasen, Shicong Ye, G. Bryan Young, Huiying Yu, Ryan J. Zehnder
- Edited by Alan B. Ettinger, Albert Einstein College of Medicine, New York, Deborah M. Weisbrot, State University of New York, Stony Brook
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- Book:
- Neurologic Differential Diagnosis
- Published online:
- 05 June 2014
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- 17 April 2014, pp xi-xx
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Contributors
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- By Shamsuddin Akhtar, Greg Albert, Sidney Allison, Muhammad Anwar, Haruo Arita, Amanda Barker, Mary Hanna Bekhit, Jeanna Blitz, Tyson Bolinske, David Burbulys, Asokumar Buvanendran, Gregory Cain, Keith A. Candiotti, Daniel B. Carr, Derek Chalmers, John Charney, Rex Cheng, Roger Chou, Keun Sam Chung, Anna Clebone, Frederick Conlin, Susan Dabu-Bondoc, Tiffany Denepitiya-Balicki, Jeanette Derdemezi, Anahat Kaur Dhillon, Ho Dzung, Juan Jose Egas, Stephen M. Eskaros, Zhuang T. Fang, Claudia R. Fernandez Robles, Victor A. Filadora, Ellen Flanagan, Dan Froicu, Allison Gandey, Nehal Gatha, Boris Gelman, Christopher Gharibo, Muhammad K. Ghori, Brian Ginsberg, Michael E. Goldberg, Jeff Gudin, Thomas Halaszynski, Martin Hale, Dorothea Hall, Craig T. Hartrick, Justin Hata, Lars E. Helgeson, Joe C. Hong, Richard W. Hong, Balazs Horvath, Eric S. Hsu, Gabriel Jacobs, Jonathan S. Jahr, Rongjie Jaing, Inderjeet Singh Julka, Zeev N. Kain, Clinton Kakazu, Kianusch Kiai, Mary Keyes, Michael M. Kim, Peter G. Lacouture, Ryan Lanier, Vivian K. Lee, Mark J. Lema, Oscar A. de Leon-Casasola, Imanuel Lerman, Philip Levin, Steven Levin, JinLei Li, Eric C. Lin, Sharon Lin, David A. Lindley, Ana M. Lobo, Marisa Lomanto, Mirjana Lovrincevic, Brenda C. McClain, Tariq Malik, Jure Marijic, Joseph Marino, Laura Mechtler, Alan Miller, Carly Miller, Amit Mirchandani, Sukanya Mitra, Fleurise Montecillo, James M. Moore, Debra E. Morrison, Philip F. Morway, Carsten Nadjat-Haiem, Hamid Nourmand, Dana Oprea, Sunil J. Panchal, Edward J. Park, Kathleen Ji Park, Kellie Park, Parisa Partownavid, Akta Patel, Bijal Patel, Komal D. Patel, Neesa Patel, Swati Patel, Paul M. Peloso, Danielle Perret, Anthony DePlato, Marjorie Podraza Stiegler, Despina Psillides, Mamatha Punjala, Johan Raeder, Siamak Rahman, Aziz M. Razzuk, Maggy G. Riad, Kristin L. Richards, R. Todd Rinnier, Ian W. Rodger, Joseph Rosa, Abraham Rosenbaum, Alireza Sadoughi, Veena Salgar, Leslie Schechter, Michael Seneca, Yasser F. Shaheen, James H. Shull, Elizabeth Sinatra, Raymond S. Sinatra, Neil Singla, Neil Sinha, Denis V. Snegovskikh, Dmitri Souzdalnitski, Julie Sramcik, Zoreh Steffens, Alexander Timchenko, Vadim Tokhner, Marc C. Torjman, Co T. Truong, Nalini Vadivelu, Ashley Vaughn, Anjali Vira, Eugene R. Viscusi, Dajie Wang, Shu-ming Wang, J. Michael Watkins-Pitchford, Steven J. Weisman, Ira Whitten, Bryan S. Williams, Jeremy M. Wong, Thomas Wong, Christopher Wray, Yaw Wu, Anthony T. Yarussi, Laurie Yonemoto, Bita H. Zadeh, Jill Zafar, Martha Zegarra, Keren Ziv
- Edited by Raymond S. Sinatra, Jonathan S. Jahr, University of California, Los Angeles, School of Medicine, J. Michael Watkins-Pitchford
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- Book:
- The Essence of Analgesia and Analgesics
- Published online:
- 06 December 2010
- Print publication:
- 14 October 2010, pp xi-xviii
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Contributors
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- By Nicholas B. Allen, Joan Rosenbaum Asarnow, Jeanne Brooks-Gunn, Ronald E. Dahl, Joanne Davila, Laura M. DeRose, Lea R. Dougherty, Nancy Eisenberg, Erika E. Forbes, Wyndol Furman, Paul Gilbert, Julia A. Graber, Danielle M. Hessler, Erin C. Hunter, Chris Irons, Lynn Fainsilber Katz, Amanda Kesek, Daniel N. Klein, Annette M. La Greca, Rebecca S. Laptook, Reed W. Larson, Primrose Letcher, Peter M. Lewinsohn, Marc D. Lewis, Christine McDunn, James W. McKowen, Christopher S. Monk, Amanda Sheffield Morris, Thomas M. Olino, Tomáš Paus, Daniel S. Pine, Ann V. Sanson, John R. Seeley, Lisa B. Sheeber, Rebecca Siegel, Jennifer S. Silk, Diana Smart, Martha C. Tompson, Julie Vaughan, Brennan J. Young, Philip David Zelazo
- Edited by Nicholas B. Allen, University of Melbourne, Lisa B. Sheeber
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- Book:
- Adolescent Emotional Development and the Emergence of Depressive Disorders
- Published online:
- 14 September 2009
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- 20 November 2008, pp ix-xiv
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List of Contributors
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- By Harold P. Adams, Colum F. Amory, Anne Angelillo-Scherrer, Irena Anselm, Marcel Arnold, Robert W. Baloh, Ralf W. Baumgartner, José Biller, Valérie Biousse, Matthias Bischof, Julien Bogousslavsky, Natan M. Bornstein, Marie Germaine Bousser, Robin L. Brey, John C. M. Brust, Alan Bryer, Olivier Calvetti, Louis R. Caplan, José Castillo, Hugues Chabriat, Chin-Sang Chung, Charlotte Cordonnier, Steven C. Cramer, Luís Cunha, Rima M. Dafer, John F. Dashe, Cyrus K. Dastur, Antonio Dávalos, Larry E. Davis, Patricia Davis, Stephen M. Davis, Jan L. De Bleecker, Michael A. De Georgia, Amir R. Dehdashti, Oscar H. Del Brutto, Jacques L. De Reuck, Hans-Christoph Diener, Kathleen B. Digre, Vivian U. Fritz, Nancy Futrell, Bhuwan P. Garg, Philip B. Gorelick, Glenn D. Graham, Alexander Y. Gur, John J. Halperin, Michael Hennerici, Isabel Lestro Henriques, Roberto C. Heros, Daniel B. Hier, Lorenz Hirt, Joanna C. Jen, Taro Kaibara, Sumit Kapoor, Sarosh M. Katrak, Siddharth Kharkar, Walter J. Koroshetz, Monisha Kumar, Sandeep Kumar, Emre Kumral, Tobias Kurth, Rogelio Leira, Steven R. Levine, Didier Leys, Doris Lin, Jonathan Lipton, Alfredo M. Lopez-Yunez, Betsy B. Love, Ayrton Roberto Massaro, Heinrich P. Mattle, Manu Mehdiratta, John H. Menkes, Philippe Metellus, Reto Meuli, Patrik Michel, Panayiotis Mitsias, Jorge Moncayo-Gaete, Julien Morier, Krassen Nedeltchev, Bernhard Neundörfer, Olukemi A. Olugemo, Nikolaos I. H. Papamitsakis, Stephen D. Reck, Luca Regli, Marc D. Reichhart, Daniele Rigamonti, Michael J. Rivkin, E. Steve Roach, Jose F. Roldan, David Z. Rose, Daniel M. Rosenbaum, N. Paul Rosman, Elayna O. Rubens, Sean I. Savitz, Marc Schapira, Robert J. Schwartzman, Magdy Selim, Yukito Shinohara, Aneesh B. Singhal, Michael A. Sloan, Barney J. Stern, Mathias Sturzenegger, Oriana Thompson, A. Wesley Thevathasan, Jonathan D. Trobe, Michael Varner, Dana Védy, Jorge Vidaurre, Engin Y. Yilmaz, Khaled Zamel, Mathieu Zuber
- Edited by Louis R. Caplan, Julien Bogousslavsky
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- Book:
- Uncommon Causes of Stroke
- Published online:
- 06 January 2010
- Print publication:
- 09 October 2008, pp ix-xiv
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A Stochastic Comparison for Arrangement Increasing Functions
- Abba M. Krieger, Paul R. Rosenbaum
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- Journal:
- Combinatorics, Probability and Computing / Volume 3 / Issue 3 / September 1994
- Published online by Cambridge University Press:
- 12 September 2008, pp. 345-348
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Let h(·) be an arrangement increasing function, let X have an arrangement increasing density, and let XE be a random permutation of the coordinates of X. We prove E{h(XE)} ≤ E{h(X)}. This comparison is delicate in that similar results are sometimes true and sometimes false. In a finite distributive lattice, a similar comparison follows from Holley's inequality, but the set of permutations with the arrangement order is not a lattice. On the other hand, the set of permutations is a lattice, though not a distributive lattice, if it is endowed with a different partial order, but in this case the comparison does not hold.
14 - The Bias Due to Incomplete Matching
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- By Paul R. Rosenbaum, University of Pennsylvania
- Donald B. Rubin, Harvard University, Massachusetts
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- Book:
- Matched Sampling for Causal Effects
- Published online:
- 05 June 2012
- Print publication:
- 04 September 2006, pp 217-232
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Summary
Abstract: Observational studies comparing groups of treated and control units are often used to estimate the effects caused by treatments. Matching is a method for sampling a large reservoir of potential controls to produce a control group of modest size that is ostensibly similar to the treated group. In practice, there is a trade-off between the desires to find matches for all treated units and to obtain matched treated–control pairs that are extremely similar to each other. We derive expressions for the bias in the average matched pair difference due to (i) the failure to match all treated units – incomplete matching, and (ii) the failure to obtain exact matches – inexact matching. A practical example shows that the bias due to incomplete matching can be severe, and moreover, can be avoided entirely by using an appropriate multivariate nearest available matching algorithm, which, in the example, leaves only a small residual bias due to inexact matching.
INTRODUCTION
The Effects Caused by Treatments
A treatment is an intervention that can, in principle, be given to or withheld from any experimental unit under study. With an experimental treatment and a control treatment, each unit has two potential responses: a response r1 that would be observed if the unit received the experimental treatment, and a response r0 that would be observed if the unit received the control treatment.
11 - Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome
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- By Paul R. Rosenbaum, University of Pennsylvania
- Donald B. Rubin, Harvard University, Massachusetts
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- Book:
- Matched Sampling for Causal Effects
- Published online:
- 05 June 2012
- Print publication:
- 04 September 2006, pp 185-192
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Summary
Abstract: This paper proposes a simple technique for assessing the range of plausible causal conclusions from observational studies with a binary outcome and an observed categorical covariate. The technique assesses the sensitivity of conclusions to assumptions about an unobserved binary covariate relevant to both treatment assignment and response. A medical study of coronary artery disease is used to illustrate the technique.
INTRODUCTION AND NOTATION
Inevitably, the results of clinical studies are subject to dispute. In observational studies, one basis for dispute is obvious: since patients were not assigned to treatments at random, patients at greater risk may be over-represented in some treatment groups. This paper proposes a method for assessing the sensitivity of causal conclusions to an unmeasured patient characteristic relevant to both treatment assignment and response. Despite their limitations, observational studies will continue to be a valuable source of information, and therefore it is prudent to develop appropriate methods of analysis for them.
Our sensitivity analysis consists of the estimation of the average effect of a treatment on a binary outcome variable after adjustment for observed categorical covariates and an unobserved binary covariate u, under several sets of assumptions about u. Both Cornfield et al. (1959) and Bross (1966) have proposed guidelines for determining whether an unmeasured binary covariate having specified properties could explain all of the apparent effect of a treatment, that is, whether the treatment effect, after adjustment for u could be zero.
13 - Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score
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- By Paul R. Rosenbaum, University of Pennsylvania
- Donald B. Rubin, Harvard University, Massachusetts
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- Book:
- Matched Sampling for Causal Effects
- Published online:
- 05 June 2012
- Print publication:
- 04 September 2006, pp 207-216
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Summary
Abstract: Matched sampling is a method for selecting units from a large reservoir of potential controls to produce a control group of modest size that is similar to a treated group with respect to the distribution of observed covariates. We illustrate the use of multivariate matching methods in an observational study of the effects of prenatal exposure to barbiturates on subsequent psychological development. A key idea is the use of the propensity score as a distinct matching variable.
INTRODUCTION: BACKGROUND; WHY MATCH?
Matched Sampling in Observational Studies. In many observational studies, there is a relatively small group of subjects exposed to a treatment and a much larger group of control subjects not exposed. When the costs associated with obtaining outcome or response data from subjects are high, some sampling of the control reservoir is often necessary. Matched sampling attempts to choose the controls for further study so that they are similar to the treated subjects with respect to background variables measured on all subjects.
The Danish Cohort. We examine multivariate matched sampling using initial data from a proposed study of the effects on psychological development of prenatal exposure to barbiturates. The analyses presented are preliminary and intended only to explore methodological options; none of the matched samples are the actual ones to be used for study of in utero exposure to barbiturates. The children under study were born between 1959 and 1961 and have been the object of other studies (e.g., Mednick et al. 1971; Zachau-Christiansen and Ross 1975).
15 - Affinely Invariant Matching Methods with Ellipsoidal Distributions
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- By Paul R. Rosenbaum, University of Pennsylvania
- Donald B. Rubin, Harvard University, Massachusetts
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- Book:
- Matched Sampling for Causal Effects
- Published online:
- 05 June 2012
- Print publication:
- 04 September 2006, pp 235-248
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Summary
Abstract: Matched sampling is a common technique used for controlling bias in observational studies. We present a general theoretical framework for studying the performance of such matching methods. Specifically, results are obtained concerning the performance of affinely invariant matching methods with ellipsoidal distributions, which extend previous results on equal percent bias reducing methods. Additional extensions cover conditionally affinely invariant matching methods for covariates with conditionally ellipsoidal distributions. These results decompose the effects of matching into one subspace containing the best linear discriminant, and the subspace of variables uncorrelated with the discriminant. This characterization of the effects of matching provides a theoretical foundation for understanding the performance of specific methods such as matched sampling using estimated propensity scores. Calculations for such methods are given in subsequent articles.
BACKGROUND
Matched sampling is a popular and important technique for controlling bias in observational studies. It has received increasing attention in the statistical literature in recent years [Cochran (1968a); Cochran and Rubin (1973); Rubin (1973a, b), (1976b, c), (1979b); Carpenter (1977); and Rosenbaum and Rubin (1983a, 1985a)]. The basic situation has two populations of units, treated (e.g., smokers) and control (e.g., nonsmokers), and a set of observed matching variables X = (X1, …, Xp) (e.g., age, gender, weight). The objective is to compare the distributions of the outcome variables having adjusted for differences in the distributions of X in the two populations. Matched sampling is a way of adjusting for X through data collection.
10 - The Central Role of the Propensity Score in Observational Studies for Causal Effects
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- By Paul R. Rosenbaum, University of Pennsylvania
- Donald B. Rubin, Harvard University, Massachusetts
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- Book:
- Matched Sampling for Causal Effects
- Published online:
- 05 June 2012
- Print publication:
- 04 September 2006, pp 170-184
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Summary
Abstract: The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Both large and small sample theory show that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates. Applications include: (i) matched sampling on the univariate propensity score, which is a generalization of discriminant matching, (ii) multivariate adjustment by subclassification on the propensity score where the same subclasses are used to estimate treatment effects for all outcome variables and in all subpopulations, and (iii) visual representation of multivariate covariance adjustment by a two-dimensional plot.
DEFINITIONS
The Structure of Studies for Causal Effects
Inferences about the effects of treatments involve speculations about the effect one treatment would have had on a unit which, in fact, received some other treatment. We consider the case of two treatments, numbered 1 and 0. In principle, the ith of the N units under study has both a response r1i that would have resulted if it had received treatment 1, and a response r0i that would have resulted if it had received treatment 0. In this formulation, causal effects are comparisons of r1i and r0i, for example r1i – r0i or r1i/r0i. Since each unit receives only one treatment, either r1i or r0i is observed, but not both, so comparisons of r1i and r0i imply some degree of speculation.
12 - Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
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- By Paul R. Rosenbaum, University of Pennsylvania
- Donald B. Rubin, Harvard University, Massachusetts
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- Book:
- Matched Sampling for Causal Effects
- Published online:
- 05 June 2012
- Print publication:
- 04 September 2006, pp 193-206
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Summary
Abstract: The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Previous theoretical arguments have shown that subclassification on the propensity score will balance all observed covariates. Subclassification on an estimated propensity score is illustrated, using observational data on treatments for coronary artery disease. Five subclasses defined by the estimated propensity score are constructed that balance 74 covariates, and thereby provide estimates of treatment effects using direct adjustment. These subclasses are applied within subpopulations, and model-based adjustments are then used to provide estimates of treatment effects within these subpopulations. Two appendixes address theoretical issues related to the application: the effectiveness of subclassification on the propensity score in removing bias, and balancing properties of propensity scores with incomplete data.
INTRODUCTION: SUBCLASSIFICATION AND THE PROPENSITY SCORE
Adjustment by Subclassification in Observational Studies
In observational studies for causal effects, treatments are assigned to experimental units without the benefits of randomization. As a result, treatment groups may differ systematically with respect to relevant characteristics and, therefore, may not be directly comparable. One commonly used method of controlling for systematic differences involves grouping units into subclasses based on observed characteristics, and then directly comparing only treated and control units who fall in the same subclass. Obviously such a procedure can only control the bias due to imbalances in observed covariates.
Cochran (1968a) presents an example in which the mortality rates of cigarette smokers, cigar/pipe smokers, and nonsmokers are compared after subclassification on the covariate age.
Looking Backward, Looking Forward: MLA Members Speak
- April Alliston, Elizabeth Ammons, Jean Arnold, Nina Baym, Sandra L. Beckett, Peter G. Beidler, Roger A. Berger, Sandra Bermann, J.J. Wilson, Troy Boone, Alison Booth, Wayne C. Booth, James Phelan, Marie Borroff, Ihab Hassan, Ulrich Weisstein, Zack Bowen, Jill Campbell, Dan Campion, Jay Caplan, Maurice Charney, Beverly Lyon Clark, Robert A. Colby, Thomas C. Coleman III, Nicole Cooley, Richard Dellamora, Morris Dickstein, Terrell Dixon, Emory Elliott, Caryl Emerson, Ann W. Engar, Lars Engle, Kai Hammermeister, N. N. Feltes, Mary Anne Ferguson, Annie Finch, Shelley Fisher Fishkin, Jerry Aline Flieger, Norman Friedman, Rosemarie Garland-Thomson, Sandra M. Gilbert, Laurie Grobman, George Guida, Liselotte Gumpel, R. K. Gupta, Florence Howe, Cathy L. Jrade, Richard A. Kaye, Calhoun Winton, Murray Krieger, Robert Langbaum, Richard A. Lanham, Marilee Lindemann, Paul Michael Lützeler, Thomas J. Lynn, Juliet Flower MacCannell, Michelle A. Massé, Irving Massey, Georges May, Christian W. Hallstein, Gita May, Lucy McDiarmid, Ellen Messer-Davidow, Koritha Mitchell, Robin Smiles, Kenyatta Albeny, George Monteiro, Joel Myerson, Alan Nadel, Ashton Nichols, Jeffrey Nishimura, Neal Oxenhandler, David Palumbo-Liu, Vincent P. Pecora, David Porter, Nancy Potter, Ronald C. Rosbottom, Elias L. Rivers, Gerhard F. Strasser, J. L. Styan, Marianna De Marco Torgovnick, Gary Totten, David van Leer, Asha Varadharajan, Orrin N. C. Wang, Sharon Willis, Louise E. Wright, Donald A. Yates, Takayuki Yokota-Murakami, Richard E. Zeikowitz, Angelika Bammer, Dale Bauer, Karl Beckson, Betsy A. Bowen, Stacey Donohue, Sheila Emerson, Gwendolyn Audrey Foster, Jay L. Halio, Karl Kroeber, Terence Hawkes, William B. Hunter, Mary Jambus, Willard F. King, Nancy K. Miller, Jody Norton, Ann Pellegrini, S. P. Rosenbaum, Lorie Roth, Robert Scholes, Joanne Shattock, Rosemary T. VanArsdel, Alfred Bendixen, Alarma Kathleen Brown, Michael J. Kiskis, Debra A. Castillo, Rey Chow, John F. Crossen, Robert F. Fleissner, Regenia Gagnier, Nicholas Howe, M. Thomas Inge, Frank Mehring, Hyungji Park, Jahan Ramazani, Kenneth M. Roemer, Deborah D. Rogers, A. LaVonne Brown Ruoff, Regina M. Schwartz, John T. Shawcross, Brenda R. Silver, Andrew von Hendy, Virginia Wright Wexman, Britta Zangen, A. Owen Aldridge, Paula R. Backscheider, Roland Bartel, E. M. Forster, Milton Birnbaum, Jonathan Bishop, Crystal Downing, Frank H. Ellis, Roberto Forns-Broggi, James R. Giles, Mary E. Giles, Susan Blair Green, Madelyn Gutwirth, Constance B. Hieatt, Titi Adepitan, Edgar C. Knowlton, Jr., Emanuel Mussman, Sally Todd Nelson, Robert O. Preyer, David Diego Rodriguez, Guy Stern, James Thorpe, Robert J. Wilson, Rebecca S. Beal, Joyce Simutis, Betsy Bowden, Sara Cooper, Wheeler Winston Dixon, Tarek el Ariss, Richard Jewell, John W. Kronik, Wendy Martin, Stuart Y. McDougal, Hugo Méndez-Ramírez, Ivy Schweitzer, Armand E. Singer, G. Thomas Tanselle, Tom Bishop, Mary Ann Caws, Marcel Gutwirth, Christophe Ippolito, Lawrence D. Kritzman, James Longenbach, Tim McCracken, Wolfe S. Molitor, Diane Quantic, Gregory Rabassa, Ellen M. Tsagaris, Anthony C. Yu, Betty Jean Craige, Wendell V. Harris, J. Hillis Miller, Jesse G. Swan, Helene Zimmer-Loew, Peter Berek, James Chandler, Hanna K. Charney, Philip Cohen, Judith Fetterley, Herbert Lindenberger, Julia Reinhard Lupton, Maximillian E. Novak, Richard Ohmann, Marjorie Perloff, Mark Reynolds, James Sledd, Harriet Turner, Marie Umeh, Flavia Aloya, Regina Barreca, Konrad Bieber, Ellis Hanson, William J. Hyde, Holly A. Laird, David Leverenz, Allen Michie, J. Wesley Miller, Marvin Rosenberg, Daniel R. Schwarz, Elizabeth Welt Trahan, Jean Fagan Yellin
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- Journal:
- PMLA / Publications of the Modern Language Association of America / Volume 115 / Issue 7 / December 2000
- Published online by Cambridge University Press:
- 23 October 2020, pp. 1986-2078
- Print publication:
- December 2000
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