The z-difference can be used to measure covariate balance in matched propensity score analyses

Journal of Clinical Epidemiology

Volumn 66, Issue 11, 2013, Pages 1302-1307

  Kuss, Oliver ^a  

^a MARTIN LUTHER UNIVERSITY HALLE WITTENBERG   (Germany)

Author keywords

Covariate balance; Propensity score; Propensity score matching; Standardized difference; z Difference Q Q plot

Indexed keywords

AGED; ARTICLE; COMPARATIVE STUDY; CONTROLLED STUDY; COVARIANCE; FEMALE; HEART SURGERY; HUMAN; MAJOR CLINICAL STUDY; MALE; MEASUREMENT; PRIORITY JOURNAL; PROPENSITY SCORE; RANDOMIZED CONTROLLED TRIAL (TOPIC); SAMPLE SIZE; STANDARDIZATION;

COVARIATE BALANCE; PROPENSITY SCORE; PROPENSITY SCORE MATCHING; Q-Q PLOT; STANDARDIZED DIFFERENCE; Z-DIFFERENCE;

AGED; ANALYSIS OF VARIANCE; CARDIAC SURGICAL PROCEDURES; DATA INTERPRETATION, STATISTICAL; FEMALE; HUMANS; MALE; MIDDLE AGED; PROPENSITY SCORE; RANDOMIZED CONTROLLED TRIALS AS TOPIC; TREATMENT OUTCOME;

EID: 84885022704     PISSN: 08954356     EISSN: 18785921     Source Type: Journal    
DOI: 10.1016/j.jclinepi.2013.06.001     Document Type: Article

References (22)

1. E.F. Cook, and L. Goldman Performance of tests of significance based on stratification by a multivariate confounder score or by a propensity score J Clin Epidemiol 42 1989 317 324
2. M.S. Cepeda, R. Boston, J.T. Farrar, and B.L. Strom Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders Am J Epidemiol 158 2003 280 287
3. J.M. Oakes, and P.J. Johnson Propensity score matching for social epidemiology J.M. Oakes, J.S. Kaufman, Methods in social epidemiology 2006 Jossey-Bass San Francisco 364 386
4. E. Williamson, R. Morley, A. Lucas, and J. Carpenter Propensity scores: from naive enthusiasm to intuitive understanding Stat Methods Med Res 21 2012 273 293
5. P.C. Austin The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies Med Decis Making 29 2009 661 677
6. P.C. Austin Propensity-score matching in the cardiovascular surgery literature from 2004 to 2006: a systematic review and suggestions for improvement J Thorac Cardiovasc Surg 134 2007 1128 1137
7. S.L. Morgan, and D.J. Harding Matching estimators of causal effects - prospects and pitfalls in theory and practice Sociological Methods Res 35 1 2006 3 60
8. K. Imai, G. King, and E.A. Stuart Misunderstandings between experimentalists and observationalists about causal inference J R Stat Soc Ser A-Statistics Soc 171 2008 481 502
9. D.B. Rubin Using propensity scores to help design observational studies: application to tobacco litigation Health Serv Outcomes Res Methodol 2 3-4 2001 169 188
10. P.C. Austin Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples Stat Med 28 2009 3083 3107
11. P.C. Austin Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research Commun Statistics-Simulation Comput 38 6 2009 1228 1234
12. J. Borgermann, K. Hakim, A. Renner, A. Parsa, A. Aboud, and T. Becker Clampless off-pump versus conventional coronary artery revascularization: a propensity score analysis of 788 patients Circulation 126 11 Suppl 1 2012 S176 S182
13. J. Hill, D.B. Rubin, and N. Thomas The design of the New York School Choice Scholarship Program evaluation L. Bickman, Research design: Donald Campbell's legacy 2000 Sage Publications Thousand Oaks, CA, USA 155 180
14. S. Senn Testing for baseline balance in clinical trials Stat Med 13 1994 1715 1726
15. P.C. Austin An introduction to propensity score methods for reducing the effects of confounding in observational studies Multivariate Behav Res 46 3 2011 399 424
16. E.L. Lehmann, and G. Casella Theory of point estimation 1998 Springer-Verlag New York
17. G.E. Miller, and C.J. Feltz Asymptotic inference for coefficients of variation Commun Statistics-Theory Methods 26 3 1997 715 726
18. D.B. Rubin, and N. Thomas Characterizing the effect of matching using linear propensity score methods with normal distributions Biometrika 79 4 1992 797 809
19. D.B. Rubin, and N. Thomas Matching using estimated propensity scores: relating theory to practice Biometrics 52 1996 249 264
20. S.V. Belitser, E.P. Martens, W.R. Pestman, R.H. Groenwold, A. de Boer, and O.H. Klungel Measuring balance and model selection in propensity score methods Pharmacoepidemiol Drug Saf 20 11 2011 1115 1129
21. J. Hartung, G. Knapp, and B.K. Sinha Statistical meta-analysis with applications 2008 Hoboken New Jersey
22. S.N. Goodman Toward evidence-based medical statistics. 1: the P value fallacy Ann Intern Med 130 1999 995 1004