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Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation
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“Nearly all analyses show Rubin's B<25%, Rubin's R of 0.5–2, and a percentage bias of <10% for each covariate (Fig. 1) (Morgan, 2018; Rubin, 2001).”
From Longitudinal associations between reading for pleasure and child maladjustment: Results from a propensity score matching analysis (2020) · cites this paper as Rubin, 2001
In the citing paper
“We used directed acyclic graphs to identify factors that could be associated with both reading for pleasure and children's strengths and difficulties, or with children's strengths and difficulties only based on previous empirical research (Brookhart et al., 2006; Caliendo and Kopeinig, 2008; Rubin, 2001; Sauer and VanderWeele, 2013).”
From Longitudinal associations between reading for pleasure and child maladjustment: Results from a propensity score matching analysis (2020) · cites this paper as Rubin, 2001
In the citing paper
“Secondly, this process of variable selection can be based on previous research [59].”
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The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
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Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
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Self-citationA Generalization of Sampling Without Replacement From a Finite Universe
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Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs
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Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania
Card, D., & Krueger, A. B. (1993) · National Bureau of Economic Research · doi:10.3386/w4509 · Cited by 2,072
The role of the propensity score in estimating dose-response functions
Imbens, G. W. (2000) · Biometrika · doi:10.1093/biomet/87.3.706 · Cited by 1,779
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Cited by
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Items 201–300 of 1,001
Mishra, G. S., Clewlow, R., Mokhtarian, P. L., & Widaman, K. F. (2015) · Research in Transportation Economics · doi:10.1016/j.retrec.2015.10.010 · Cited by 94
Individual treatment selection for patients with posttraumatic stress disorder
Deisenhofer, A., Delgadillo, J., Rubel, J. A., Boehnke, J. R., Zimmermann, D., Schwartz, B., & Lutz, W. (2018) · Depression and Anxiety · doi:10.1002/da.22755 · Cited by 93
“A SMD .25 indicates acceptable match between samples on the respective covariates (Rubin, 2001).”
BackgroundCorporate Twitter use and cost of equity capital
Guindy, M. A. (2021) · Journal of Corporate Finance · doi:10.1016/j.jcorpfin.2021.101926 · Cited by 102
Sasidharan, L., & Donnell, E. T. (2012) · Accident Analysis & Prevention · doi:10.1016/j.aap.2012.05.036 · Cited by 84
Race, Neighbourhood Context and Perceptions of Injustice by the Police in Cincinnati
MacDonald, J. M., Stokes, R. J., Ridgeway, G. K., & Riley, K. J. (2007) · Urban Studies · doi:10.1080/00420980701558400 · Cited by 85
“Therefore, one would have to invoke “heroic modelling assumptions based on extrapolation” (Rubin, 2001) for this neighbourhood to provide information on the racial differences in perceptions.”
BackgroundBangalore, S., Guo, Y., Samadashvili, Z., Blecker, S., Xu, J., & Hannan, E. L. (2015) · Circulation Cardiovascular Interventions · doi:10.1161/circinterventions.115.002626 · Cited by 83
“The propensity score is a conditional probability of having a particular exposure (EES versus CABG) given a set of baseline measured covariates.(5,6) A nonparsimonious multivariable logistic regression model(7) with EES use as the dependent variable and all the baseline characteristics outlined in Table 1 and Table I…”
MethodRobinson, J. P., & Espelage, D. L. (2013) · American Journal of Public Health · doi:10.2105/ajph.2013.301387 · Cited by 88
Statistics in Brief: An Introduction to the Use of Propensity Scores
Inacio, M. C. S., Chen, Y., Paxton, E. W., Namba, R. S., Kurtz, S. M., & Cafri, G. (2015) · Clinical Orthopaedics and Related Research · doi:10.1007/s11999-015-4239-4 · Cited by 83
“Rubin, a pioneer in field of propensity scores, set forth guidelines for global assessment of balance between covariates [22].”
BackgroundLau, W. C. Y., Cheung, C., Man, K. K. C., Chan, E. W., Sing, C., Lip, G. Y. H., Siu, C., Lam, J. K. Y., Lee, A. C. H., & Wong, I. C. K. (2020) · Annals of Internal Medicine · doi:10.7326/m19-3671 · Cited by 82
Adegunsoye, A., Alqalyoobi, S., Linderholm, A., Bowman, W. S., Lee, C. T., Pugashetti, J. V., Sarma, N., Ma, S.-F., Haczku, A., Sperling, A., et al. (2020) · Chest · doi:10.1016/j.chest.2020.04.066 · Cited by 82
The Effects of Arrest, Reporting to the Police, and Victim Services on Intimate Partner Violence
Xie, M., & Lynch, J. P. (2016) · Journal of Research in Crime and Delinquency · doi:10.1177/0022427816678035 · Cited by 82
Mitani, H. (2018) · Educational Administration Quarterly · doi:10.1177/0013161x18785874 · Cited by 104
“standard deviation (Rosenbaum & Rubin, 1985), and use 25% or below as a criterion to determine whether a balance is achieved (Harder, Stuart, & Anthony, 2010; Rubin, 2001; Stuart, 2010).”
BackgroundMethodWill You Still Need Me, Will You Still Feed Me When I'm 64? The Health Impact of Caregiving
de Zwart, P. L., Bakx, P., & van Doorslaer, E. K. A. (2016) · Health Economics · doi:10.1002/hec.3542 · Cited by 89
“Matching yields an unbiased estimate of the treatment effect if two assumptions hold (Rosenbaum & Rubin, 1983). The first assumption is the Stable Unit Treatment Value Assumption, which requires that there is a unique health outcome hiT for individual i and caregiving activity T and that this outcome does not depend o…”
MethodImbens, G. W., & Rubin, D. B. (2010) · Palgrave Macmillan UK eBooks · doi:10.1057/9780230280816_28 · Cited by 85
Self-citationCorporate social responsibility and seasoned equity offerings
Dutordoir, M., Strong, N. C., & Sun, P. (2018) · Journal of Corporate Finance · doi:10.1016/j.jcorpfin.2018.03.005 · Cited by 81
“According to Rubin (2001), B should be less than 25 to indicate a sufficiently balanced sample.”
Hassan, A. N., Le Foll, B., Imtiaz, S., & Rehm, J. (2017) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2017.07.012 · Cited by 80
Ben-David, D., Novikov, I., & Mermel, L. A. (2009) · Infection Control and Hospital Epidemiology · doi:10.1086/596731 · Cited by 80
“42 12 (28) 21 (9-35) 11 (4-24) 113,852 (48,961-55,001) 51,492 (24,535-104,499) 2,894 (1,902-3,553)”
Resource efficiency investments and firm performance: Evidence from European SMEs
Özbuğday, F. C., Fındık, D., Metin-Özcan, K., & Başçı, S. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.119824 · Cited by 91
Internal Control Quality and Investment Efficiency
Lai, S., Liu, C., & Chen, S. (2020) · Accounting Horizons · doi:10.2308/horizons-12-148 · Cited by 83
Ahmed, M. I., White, M., Ekundayo, O. J., Love, T. E., Aban, I., Liu, B., Aronow, W. S., & Ahmed, A. (2009) · European Heart Journal · doi:10.1093/eurheartj/ehp222 · Cited by 79
Estimating Causal Effects Using School-Level Data Sets
Stuart, E. A. (2007) · Educational Researcher · doi:10.3102/0013189x07303396 · Cited by 80
“Rubin (2001) gave three conditions for trustworthiness of a regression analysis and asserted, “If any of these conditions is not satisfied, the differences between the distributions of covariates in the two groups must be regarded as substantial, and regression adjustment will be unreliable and…”
BackgroundLoop Diuretic Prescription and 30-Day Outcomes in Older Patients With Heart Failure
Faselis, C. J., Arundel, C., Patel, S. S., Lam, P. H., Gottlieb, S. S., Zile, M. R., DEEDWANIA, P. C., Filippatos, G. S., Sheriff, H. M., Zeng, Q. T., et al. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.06.022 · Cited by 78
Henry, G. T., Gordon, C. S., & Rickman, D. K. (2006) · Educational Evaluation and Policy Analysis · doi:10.3102/01623737028001077 · Cited by 77
Isolated Systolic Hypertension and Incident Heart Failure in Older Adults
Ekundayo, O. J., Allman, R. M., Sanders, P. W., Aban, I., Love, T. E., Arnett, D. K., & Ahmed, A. (2009) · Hypertension · doi:10.1161/hypertensionaha.108.119792 · Cited by 80
Hypoalbuminaemia and Incident Heart Failure in Older Adults
Filippatos, G. S., Desai, R. V., Ahmed, M. I., Fonarow, G. C., Love, T. E., Aban, I., Iskandrian, A. E., Konstam, M. A., & Ahmed, A. (2011) · European Journal of Heart Failure · doi:10.1093/eurjhf/hfr088 · Cited by 77
“Propensity score is the conditional probability of having an exposure given a set of measured baseline characteristics.(20,21) Propensity scores for hypoalbuminaemia for each of the 5450 participants were estimated using a non-parsimonious multivariable logistic regression model.”
MethodRenin-Angiotensin Inhibition in Systolic Heart Failure and Chronic Kidney Disease
Ahmed, A., Fonarow, G. C., Zhang, Y., Sanders, P. W., Allman, R. M., Arnett, D. K., Feller, M. A., Love, T. E., Aban, I., Levesque, R., et al. (2012) · The American Journal of Medicine · doi:10.1016/j.amjmed.2011.10.013 · Cited by 77
“Calcium channel blockers 37 (17) 1 Potassium supplements 96 (45) 3 Nitrates and hydralazine 4 (2) Antiarrhythmic drugs 34 (16) Anticoagulants 60 (28) 2 Antiplatelet drugs 18 (8) Aspirin 64 (30) 3 Insulin 15 (7) Statins 24 (11) 1 Antidepressants 44 (21) 1 Nonsteroidal anti-inflammatory drugs 21 (10)”
Visual Impairment and Frailty: Examining an Understudied Relationship
Swenor, B. K., Lee, M. J., Tian, J., Varadaraj, V., & Bandeen‐Roche, K. (2019) · The Journals of Gerontology Series A · doi:10.1093/gerona/glz182 · Cited by 77
“All regression modeling to develop the propensity scores were weighted using study-specific survey weights (29).”
MethodA Brief Guide to Decisions at Each Step of the Propensity Score Matching Process
Harris, H. D., & Horst, S. J. (2020) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/yq7r-4820 · Cited by 82
Generalizability of Randomized Trial Results to Target Populations
Stuart, E. A., Ackerman, B., & Westreich, D. J. (2017) · Research on Social Work Practice · doi:10.1177/1049731517720730 · Cited by 76
“Thus, a well-done nonexperimental study (such as described by Rosenbaum, 1999, or Rubin, 2001) that can be conducted in a sample representative of the target population of interest may be worth considering when interest is in informing decisions in that population.”
BackgroundDoes the old-age pension scheme improve household welfare? Evidence from India
Unnikrishnan, V., & Imai, K. S. (2020) · World Development · doi:10.1016/j.worlddev.2020.105017 · Cited by 76
Yang, J. Y., Webster‐Clark, M., Lund, J. L., Sandler, R. S., Dellon, E. S., & Stürmer, T. (2019) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2019.04.236 · Cited by 77
Systolic Blood Pressure and Outcomes in Patients With Heart Failure With Reduced Ejection Fraction
Arundel, C., Lam, P. H., Gill, G. S., Patel, S. S., Panjrath, G. S., Faselis, C. J., White, M., Morgan, C. J., Allman, R. M., Aronow, W. S., et al. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.04.022 · Cited by 75
Trust no more? On the lasting effects of corruption scandals
Solé‐Ollé, A., & Sorribas‐Navarro, P. (2017) · European Journal of Political Economy · doi:10.1016/j.ejpoleco.2017.12.003 · Cited by 87
Fertiliser adoption and sustainable rural livelihood improvement in Nigeria
Donkor, E., Onakuse, S., Bogue, J., & Carmenado, I. D. L. R. (2019) · Land Use Policy · doi:10.1016/j.landusepol.2019.104193 · Cited by 75
A Cure for Crime: Can Mental Health Treatment Diversion Reduce Crime among Youth?
Cuellar, A. E., McReynolds, L. S., & Wasserman, G. A. (2008) · doi:10.1002/9781444307399.ch16 · Cited by 75
An Illustrative Example of Propensity Score Matching with Education Research
Lane, F., To, Y. M., Shelley, K., & Henson, R. K. (2012) · Career and Technical Education Research · doi:10.5328/cter37.3.187 · Cited by 74
The high cost of unpaid care by young people:health and economic impacts of providing unpaid care
Brimblecombe, N., Knapp, M. R. J., King, D., Stevens, M., & Cartagena‐Farias, J. (2020) · BMC Public Health · doi:10.1186/s12889-020-09166-7 · Cited by 74
“5 and 2, this is interpreted as indicating that the samples are sufficiently balanced [38].”
BackgroundIyer, A., Ahmed, M., Filippatos, G., Ekundayo, O. J., Aban, I. B., Love, T., Nanda, N., Bakris, G., Fonarow, G., Aronow, W., et al. (2010) · Journal of the American Society of Hypertension : JASH · doi:10.1016/j.jash.2010.02.002 · Cited by 75
Campbell, R. C., Sui, X., Filippatos, G. S., Love, T. E., Wahle, C., Sanders, P. W., & Ahmed, A. (2008) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfn445 · Cited by 74
Zeng, F., Patel, B. V., Andrews, L., Frech‐Tamas, F., & Rudolph, A. E. (2010) · Current Medical Research and Opinion · doi:10.1185/03007995.2010.534129 · Cited by 73
Nickel, N. C., Labbok, M. H., Hudgens, M. G., & Daniels, J. L. (2012) · Journal of Human Lactation · doi:10.1177/0890334412464695 · Cited by 83
“The propensity score (PS) can be used in the potential outcomes framework to draw causal inferences from estimated relationships.(60-65) More information on causal inference is presented elsewhere.”
BackgroundMethodCannas, M., & Arpino, B. (2019) · Biometrical Journal · doi:10.1002/bimj.201800132 · Cited by 79
“Some researchers suggested assessing ASAM also on interactions among covariates (Ho et al., 2007; Imai et al., 2006; Rubin, 2001).”
BackgroundPromoting or preventing labor migration? Revisiting the role of language
Liu, Y., Jiao, Y., & Xu, X. (2020) · China Economic Review · doi:10.1016/j.chieco.2020.101407 · Cited by 73
Does Sensationalism Affect Executive Compensation? Evidence from Pay Ratio Disclosure Reform
CHANG, W., Dambra, M., Schonberger, B., & Suk, I. (2019) · Journal of Accounting Research · doi:10.1111/1475-679x.12458 · Cited by 73
Inverse probability weighting with error-prone covariates
McCaffrey, D. F., Lockwood, J. R., & Setodji, C. M. (2013) · Biometrika · doi:10.1093/biomet/ast022 · Cited by 72
ECMO as a Bridge to Left Ventricular Assist Device or Heart Transplantation
DeFilippis, E. M., Clerkin, K. J., Truby, L., Francke, M., Fried, J., Masoumi, A., Garan, A. R., FARR, M. A., Takayama, H., Takeda, K., et al. (2021) · JACC Heart Failure · doi:10.1016/j.jchf.2020.12.012 · Cited by 72
Poon, M., Cortegiano, M., Abramowicz, A. J., Hines, M., Singer, A. J., Henry, M. C., Viccellio, P., Hellinger, J. C., Ferraro, S., Poon, A., et al. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.04.040 · Cited by 72
“To control for this difference, we used logistic regression analysis to calculate propensity scores for receiving CCTA using the following risk factors routinely evaluated during ED workup as binomial variables: hypertension, hyperlipidemia, diabetes, patient current/historical smoking status, sex, BMI, renal status,…”
MethodAhmed, A., Young, J. B., Love, T. E., Levesque, R., & Pitt, B. (2007) · International Journal of Cardiology · doi:10.1016/j.ijcard.2007.05.032 · Cited by 72
Effect of market production on rural household food consumption: evidence from Uganda
Ntakyo, P. R., & van den Berg, M. (2019) · Food Security · doi:10.1007/s12571-019-00959-2 · Cited by 71
“…of the means of the linear index of the propensity score in the treated and matched control group (B) and the ratio of treated to matched control group variances of the propensity score index(R) conform to Rubins’ recommendation (B is less than 25% and R is within the range 0.5–2) (Rubin 2001).”
BackgroundInfluentialSzücs, F. (2020) · Research Policy · doi:10.1016/j.respol.2020.103923 · Cited by 71
“Rubin (2001) proposes that the standardized bias (defined as X̄ 1−X̄0 1 2 (σ 1+σ0) , that is the difference in means of treated and control groups divided by the mean of their standard deviations) of covariates should be below 0.25 after matching.”
BackgroundInfluentialClampless Off-Pump Versus Conventional Coronary Artery Revascularization
Börgermann, J., Hakim, K., Renner, A., Parsa, A., Aboud, A., Becker, T., Masshoff, M., Zittermann, A., Gummert, J. F., & Kuß, O. (2012) · Circulation · doi:10.1161/circulationaha.111.084285 · Cited by 72
Impact of voluntary green certification on building energy performance
Qiu, Y. L., & Kahn, M. E. (2019) · Energy Economics · doi:10.1016/j.eneco.2019.01.035 · Cited by 71
Baráth, L., Fertő, I., & Bojnec, Š. (2020) · Journal of Agricultural Economics · doi:10.1111/1477-9552.12374 · Cited by 70
Wen, X., Leow, C., Hahs‐Vaughn, D. L., Korfmacher, J., & Marcus, S. M. (2011) · Early Childhood Research Quarterly · doi:10.1016/j.ecresq.2011.07.006 · Cited by 70
Townsend, S. R., Phillips, G. S., Duseja, R., Tefera, L., Cruikshank, D., Dickerson, R., Nguyen, H. B., Schorr, C. A., Levy, M. M., Dellinger, R. P., et al. (2022) · CHEST · doi:10.1016/j.chest.2021.07.2167 · Cited by 69
“We calculated the treated-to-control variance ratio.(26,27)”
Wonde, K. M., Tsehay, A. S., & Lemma, S. E. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e09837 · Cited by 69
Mokhles, S., Verstegen, N. E., Maat, A. P. W. M., Birim, Ö., Bogers, A. J. J. C., Mokhles, M. M., Lagerwaard, F. J., Senan, S., & Takkenberg, J. J. M. (2015) · Lung Cancer · doi:10.1016/j.lungcan.2015.01.005 · Cited by 69
Estimating the causal effects of marketing interventions using propensity score methodologyPreprint
Rubin, D. B., & Waterman, R. P. (2006) · Cited by 69
Self-citationMedia attention and its impact on corporate commitment to climate change action
Tavakolifar, M., Omar, A., Lemma, T., & Samkin, G. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.127833 · Cited by 73
Müller, S., Piontek, D., Pabst, A., Baumeister, S. E., & Kraus, L. (2010) · Addiction · doi:10.1111/j.1360-0443.2010.02956.x · Cited by 67
“In order to balance the covariables in the two groups, propensity scores, defined as the conditional probability of being exposed given the covariables, were used [33–35].”
MethodRubin, D. B., & Stuart, E. A. (2006) · The Annals of Statistics · doi:10.1214/009053606000000407 · Cited by 67
“For a specific example here, the implications of our results were the basis for the applied diagnostics in [15] used to assess the quality of the matched samples of smokers and never smokers in the National Medical Expenditure Survey, based on decomposing the comparisons of the distributions in the matched samples int…”
BackgroundMethodInfluentialSelf-citationPropensity score analysis with missing data.
Cham, H., & West, S. G. (2016) · Psychological Methods · doi:10.1037/met0000076 · Cited by 71
Tanaka, K., Koga, M., Lee, K., Kim, B. J., Park, E. L., Lee, J., Mizoguchi, T., Yoshimura, S., Cha, J., Lee, B., et al. (2020) · Stroke · doi:10.1161/strokeaha.119.027275 · Cited by 66
“25.(17) We assessed the proportional hazards assumption through visual inspection of log-log plots of the log cumulative hazard against log time.”
MethodBlumberg, S. J., Zablotsky, B., Avila, R. M., Colpe, L. J., Pringle, B. A., & Kogan, M. D. (2015) · Autism · doi:10.1177/1362361315607724 · Cited by 74
“The use of PSM further enhanced this study by reducing the risk for bias that could have been introduced by logistic regressions with unbalanced distributions of covariates, uneven sample sizes, and unequal variances of given covariates between groups (Rubin, 2001).”
Effect of early grade retention on school completion: A prospective study.
Hughes, J. N., West, S. G., Kim, H., & Bauer, S. S. (2017) · Journal of Educational Psychology · doi:10.1037/edu0000243 · Cited by 70
ESG Reputational Risk, Corporate Payouts and Firm Value
Chasiotis, I., Gounopoulos, D., Konstantios, D., & Patsika, V. (2023) · British Journal of Management · doi:10.1111/1467-8551.12745 · Cited by 69
Salata, K., Hussain, M. A., de Mestral, C., Greco, E., Aljabri, B. A., Mamdani, M., Forbes, T. L., Bhatt, D. L., Verma, S., & Al-Omran, M. A. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.6578 · Cited by 70
Milrinone for refractory cerebral vasospasm with delayed cerebral ischemia
Abulhasan, Y. B., Jiménez, J. O., Teitelbaum, J. S., Simoneau, G., & Angle, M. R. (2020) · Journal of neurosurgery · doi:10.3171/2020.1.jns193107 · Cited by 65
“Mild radiological vasospasm* 32/153 (21) 35/88 (40) 4 (19)”
Boen, C. (2020) · Journal of Health and Social Behavior · doi:10.1177/0022146520936208 · Cited by 68
Ekundayo, O. J., Markland, A. D., Lefante, C. M., Sui, X., Goode, P. S., Allman, R. M., Ali, M., Wahle, C., Thornton, P. L., & Ahmed, A. (2008) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2008.05.002 · Cited by 64
“The propensity score for diuretic use for a patient is the conditional probability of receiving a diuretic given that patient’s baseline characteristics (Rosenbaum and Rubin, 1983, 1984; Rubin, 1997, 2001, 2004).”
BackgroundPropensity score balance measures in pharmacoepidemiology: a simulation study
Ali, M. S., Groenwold, R. H. H., Pestman, W. R., Belitser, S. V., Roes, K. C. B., Hoes, A. W., de Boer, A., & Klungel, O. H. (2014) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.3574 · Cited by 64
Addressing unmeasured confounding in comparative observational research
Zhang, X., Faries, D. E., Li, H., Stamey, J. D., & Imbens, G. W. (2018) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4394 · Cited by 65
Lacoe, J. R., & Steinberg, M. P. (2018) · Peabody Journal of Education · doi:10.1080/0161956x.2018.1435047 · Cited by 66
“…pool of Pennsylvania districts based on existing prereform outcome trends (Abadie & Gardeazabal, 2003; Abadie, Diamond, & Hainmueller, 2010; Bassok, Fitzpatrick, & Loeb, 2014; Eren & Ozbeklik, 2016; Grindal, West, Willett, & Yoshikawa, 2015; Rubin, 2001; Rudolph, Stuart, Vernick, &Webster, 2015).”
BackgroundOutcomes in ambulatory chronic systolic and diastolic heart failure: A propensity score analysis
Ahmed, A., Perry, G. J., Fleg, J. L., Love, T. E., Goff, D. C., & Kitzman, D. W. (2006) · American Heart Journal · doi:10.1016/j.ahj.2006.06.020 · Cited by 63
“data.(14) Furthermore, any residual imbalance in baseline covariates after PS matching can be objectively estimated using absolute standardized differences between two groups.”
MethodInacio, M. C. S., Cafri, G., Paxton, E. W., Kurtz, S. M., & Namba, R. S. (2013) · Acta Orthopaedica · doi:10.3109/17453674.2013.784660 · Cited by 63
“Regression adjustment is the most traditional method of analytical confounding adjustment, but it only works well if certain restrictive distributional assumptions are made (Rubin 2001).”
MethodHypertensive Patients Show Delayed Wound Healing following Total Hip Arthroplasty
Ahmed, A. A., Mooar, P., Kleiner, M., Torg, J., & Miyamoto, C. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023224 · Cited by 62
“5 and 2, which are the standards suggested by [20] for accurate regression adjustment after matching.”
BackgroundMethodInfluentialAhmed, A., Bourge, R. C., Fonarow, G. C., Patel, K., Morgan, C. J., Fleg, J. L., Aban, I., Love, T. E., Yancy, C. W., DEEDWANIA, P. C., et al. (2013) · The American Journal of Medicine · doi:10.1016/j.amjmed.2013.08.027 · Cited by 63
“61 African American 1117 (27) 247 (23) .”
Analyzing Individual and Average Causal Effects via Structural Equation Models
Steyer, R. (2005) · Methodology · doi:10.1027/1614-1881.1.1.39 · Cited by 63
Komro, K. A., Flay, B. R., Biglan, A., & Wagenaar, A. C. (2016) · Translational Behavioral Medicine · doi:10.1007/s13142-015-0358-4 · Cited by 62
“Such methods include (a) ordinary linear regression, predicting outcome from condition and a rich set of observed covariates, and (b) propensity score stratification following careful weighting and covariance adjustment criteria to maximize balance [20].”
MethodUnbiased Causal Inference From an Observational Study: Results of a Within-Study Comparison
Pohl, S., Steiner, P. M., Eisermann, J., Soellner, R., & Cook, T. D. (2009) · Educational Evaluation and Policy Analysis · doi:10.3102/0162373709343964 · Cited by 64
“We estimated PSs using logistic regression and selected the PS model according to two balance metrics—the standardized mean difference and the variance ratio between treatment groups (Rubin, 2001b).”
BackgroundMethodKawasaki, R., Konta, T., & Nishida, K. (2018) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13372 · Cited by 62
Anindya, K., Lee, J. T., McPake, B., Wilopo, S. A., Millett, C. J., & Carvalho, N. (2020) · Journal of Global Health · doi:10.7189/jogh.10.010429 · Cited by 63
Bias Amplification and Bias Unmasking
Middleton, J. A., Scott, M. A., Diakow, R., & Hill, J. (2016) · Political Analysis · doi:10.1093/pan/mpw015 · Cited by 61
“One recommendation has been that conditioning on more, rather than fewer, available (pretreatment) covariates is the best way to minimize bias associated with unobserved sources of heterogeneity (Rosenbaum 2002; Rubin 2002).”
BackgroundThe Post‐Reform Effectiveness of the New German Start‐Up Subsidy for the Unemployed
Bellmann, L., Caliendo, M., & Tübbicke, S. (2018) · Labour · doi:10.1111/labr.12126 · Cited by 61
“15 as described in Rubin (2001).11 Rubin’s R is within the suggested bounds to indicate covariate balancing, while Rubin’s B only slightly exceeds the suggested threshold of 25% for the male sample.”
Bowling, C. B., Fonarow, G. C., Patel, K., Zhang, Y., Feller, M. A., Sui, X., Blair, S. N., Alagiakrishnan, K., Aban, I., Love, T. E., et al. (2012) · European Journal of Heart Failure · doi:10.1093/eurjhf/hfs034 · Cited by 61
Day, J. C., Zahn, M. A., & Tichavsky, L. P. (2014) · Journal of Research in Crime and Delinquency · doi:10.1177/0022427814538033 · Cited by 67
“According to Rubin (2001), transforming the propensity score to the logit scale is preferable for three reasons: ‘‘First, relative to the raw propensity (probability scale, the linear propensity (logit probability) is more relevant for assessing the efficacy of linear modeling adjustments…”
BackgroundCaliendo, M., & Tübbicke, S. (2020) · Evaluation Review · doi:10.1177/0193841x20927237 · Cited by 63
“Following Sianesi (2004), pseudo R(2) and p value of joint significance from a logit estimation on the unmatched and the matched sample are also calculated.”
MethodAn Introduction to Propensity Scores
Beal, S. J., & Kupzyk, K. A. (2013) · The Journal of Early Adolescence · doi:10.1177/0272431613503215 · Cited by 68
“Rubin (2001) identifies three guidelines for using propensity scores in regression models: (a) differences in means of propensity scores for treatment and control conditions must be less than ½ standard deviation apart (with some exceptions); (b) the variance of the treatment condition divided by…”
BackgroundMethodInfluentialThomson, D. R., Amoroso, C. L., Atwood, S., Bonds, M. H., Rwabukwisi, F. C., Drobac, P. C., Finnegan, K. E., Farmer, D. B., Farmer, P. E., Habinshuti, A., et al. (2018) · BMJ Global Health · doi:10.1136/bmjgh-2017-000674 · Cited by 60
Mangner, N., del Val, D., Abdel‐Wahab, M., Crusius, L., Durand, É., Ihlemann, N., Ureña, M., Pellegrini, C., Giannini, F., Gąsior, T., et al. (2022) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2021.11.056 · Cited by 60
Giamouzis, G., Sui, X., Love, T. E., Butler, J., Young, J. B., & Ahmed, A. (2008) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2007.08.039 · Cited by 60
Best (but oft-forgotten) practices: propensity score methods in clinical nutrition research
Ali, M. S., Groenwold, R. H. H., & Klungel, O. H. (2016) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.115.125914 · Cited by 60
“…creates an acceptable balance on important covariates and interactions or square terms by design, causal treatment effects can be estimated by direct comparison of outcomes between matched groups by using differences in means or proportions without the need to rely on parametric models (3, 47).”
The Effects of Financial Literacy Overconfidence on the Mortgage Delinquency of US Households
Kim, K. T., Lee, J., & Hanna, S. D. (2019) · Journal of Consumer Affairs · doi:10.1111/joca.12287 · Cited by 64
“…order to mitigate a possible selection bias of overconfidence, we also conducted a propensity score matching (PSM) analysis (Li 2013; Rosenbaum and Rubin 1983; Rubin 2001), which produces less biased estimates of the relation between overconfidence and mortgage loan delinquency.”
MethodChen, W.-C., Wang, C., Li, H., Lu, N. T., Tiwari, R. C., Xu, Y., & Yue, L. Q. (2020) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2020.1730877 · Cited by 62
“…number of potentially confounding covariates (Agostino and Rubin 2000; Austin 2011; Lunceford and Davidian 2004; Rosenbaum and Rubin 1983, 1984; Rubin 1997, 2001, 2007, 2008; Stuart 2010), the propensity score methodology refers to a collection of versatile statistical tools based on the…”
BackgroundMethodMild hyperkalemia and outcomes in chronic heart failure: A propensity matched study
Ahmed, M. I., Ekundayo, O. J., Mujib, M. U., Campbell, R. C., Sanders, P. W., Pitt, B., Perry, G. J., Bakris, G. L., Aban, I., Love, T. E., et al. (2009) · International Journal of Cardiology · doi:10.1016/j.ijcard.2009.04.041 · Cited by 59
Yue, L. Q., Lu, N. T., & Xu, Y. (2014) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2014.926367 · Cited by 59
“This process needs to be outcome free to avoid potential bias and to ensure the validity of study conclusion (Rubin 2001).”
BackgroundMethodResultInfluentialRaoof, M., Ituarte, P. H. G., Woo, Y., Warner, S. G., Singh, G. P., Fong, Y., & Melstrom, L. G. (2018) · British journal of surgery · doi:10.1002/bjs.10747 · Cited by 59
Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology
Rubin, D., & Waterman, R. P. (2006) · Statistical Science · doi:10.1214/088342306000000259 · Cited by 94
“This is imperative for the honesty of the cloning and is in stark contrast to model-based predictive approaches (e.g., see the discussion in Rubin, 2001, 2005; Imbens and Rubin, 2006); this point is emphasized and discussed further in Section 9.”
BackgroundMethodResultSelf-citationBangalore, S., Gupta, N., Guo, Y., Lala, A., Balsam, L. B., Roswell, R. O., Reyentovich, A., & Hochman, J. S. (2014) · The American Journal of Medicine · doi:10.1016/j.amjmed.2014.12.009 · Cited by 58
Patel, K., Fonarow, G. C., Ekundayo, O. J., Aban, I., Kilgore, M. L., Love, T. E., Kitzman, D. W., Gheorghiade, M., Allman, R. M., & Ahmed, A. (2014) · International Journal of Cardiology · doi:10.1016/j.ijcard.2014.03.005 · Cited by 59
Metadata
- Authors
- Rubin, D. B.
- Affiliations
- Harvard University (US)
- Corresponding author
- Rubin, Donald B.
- ORCID
- Rubin, Donald B.
- Venue
- Health Services and Outcomes Research Methodology
- Published
- 2001-12
- Volume
- 2
- Issue
- 3-4
- Pages
- 169-188
- ISSN
- 1387-3741 (ISSN-L)
- 1572-9400
- OpenAlex
- W2597503617
- Cited by
- 2,228
- References
- 51
- Citations by year
- 2026: 174 · 2025: 193 · 2024: 196 · 2023: 175 · 2022: 172 · 2021: 166 · 2020: 173 · 2019: 163 · 2018: 123 · 2017: 100 · 2016: 93 · 2015: 75 · 2014: 77 · 2013: 72 · 2012: 44
- FWCI
- 4.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Health Services and Outcomes Research Methodology (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- propensity score, observational study design, covariate balance, matching, subclassification, weighting, diagnostic tables, treatment effect estimation, tobacco litigation, National Medical Expenditure Survey, causal inference
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