The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
Open accessAbstract
The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This approach adjusted for confounding from all factors shared across individuals initiating analgesic treatment with opioids and with NSAIDs (eg, indications for pain treatment)”
From Association of opioid prescription initiation during adolescence and young adulthood with subsequent substance-related morbidity (2020) · cites this paper as [42]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The study maintained an active comparator, new-user design, with propensity score matching that adjusted for baseline use of other antidiabetic drugs to minimize confounding by cotreatment.39”
From Neurodegeneration and Stroke After Semaglutide and Tirzepatide in Patients With Diabetes and Obesity (2025) · cites this paper as [39]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The impact of this implementation was evaluated by comparing clinical outcomes before and after system deployment, using propensity score matching to account for potential confounding factors43.”
From Machine learning predicts sepsis deterioration trajectories. (2026) · cites this paper as [43]
The cited source says what the claim attributes to it.
In the citing paper
“PSM is considered a robust method (Austin, 2011; Benedetto et al., 2018).”
From Midwifery continuity of care, breastfeeding and neonatal hyperbilirubinemia: A retrospective cohort study (2024) · cites this paper as Austin, 2011
The cited source says what the claim attributes to it.
In the citing paper
“PSM is an analytic approach to reduce bias of measured confounding effects from covariates (Austin, 2011; Benedetto et al., 2018).”
From Midwifery continuity of care, breastfeeding and neonatal hyperbilirubinemia: A retrospective cohort study (2024) · cites this paper as Austin, 2011
In the citing paper
“All of these models, including more advanced techniques such as propensity scores [46] or inverse probability weighting, rely on the assumption of no unmeasured confounding for the validity of the effect estimate.”
From COSMOS-E: Guidance on conducting systematic reviews and meta-analyses of observational studies of etiology (2019) · cites this paper as [46]
In the citing paper
“Similarly, in cohort studies, vaccination against influenza not only protected against hospitalisation for pneumonia but also against hospitalisation for injury or trauma [46], indicating that the beneficial effect on pneumonia may have been overestimated.”
From COSMOS-E: Guidance on conducting systematic reviews and meta-analyses of observational studies of etiology (2019) · cites this paper as [46]
In the citing paper
“There are four principal ways to use the propensity score to obtain an estimated treatment effect: matching, stratification, inverse probability weighting, and covariate adjustment (7). Each method will adjust for differences in characteristics of the intervention and control groups and, in so doing, minimize the effects of confounding.”
From Natural Experiments: An Overview of Methods, Approaches, and Contributions to Public Health Intervention Research (2017) · cites this paper as [7]
In the citing paper
“The active comparator design cannot account for unmeasured factors that differentiate opioid and NSAID initiators”
From Association of opioid prescription initiation during adolescence and young adulthood with subsequent substance-related morbidity (2020) · cites this paper as [42]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
A simulation study of the number of events per variable in logistic regression analysis
Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(96)00236-3 · Cited by 9,655
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · Journal of Educational Psychology · doi:10.1037/h0037350 · Cited by 9,436
Austin, P. C. (2009) · Statistics in Medicine · doi:10.1002/sim.3697 · Cited by 6,758
Self-citationRosenbaum, P. R., & Rubin, D. B. (1985) · The American Statistician · doi:10.2307/2683903 · Cited by 6,472
Marginal Structural Models and Causal Inference in Epidemiology
Robins, J. M., Hernán, M. Á., & Brumback, B. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00011 · Cited by 5,581
MatchIt : Nonparametric Preprocessing for Parametric Causal Inference
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2011) · Journal of Statistical Software · doi:10.18637/jss.v042.i08 · Cited by 4,385
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007) · Political Analysis · doi:10.1093/pan/mpl013 · Cited by 4,285
Austin, P. C. (2011) · Pharmaceutical Statistics · doi:10.1002/pst.433 · Cited by 3,949
Self-citationCausal Diagrams for Epidemiologic Research
Greenland, S., Pearl, J., & Robins, J. M. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00008 · Cited by 3,728
Cited by
Sorted by citations
Benchimol, E. I., Smeeth, L., Guttmann, A., Harron, K., Moher, D., Petersen, I., Sørensen, H. T., von Elm, E., Langan, S. M., & RECORD Working Committee (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001885 · Cited by 5,749
Austin, P. C., & Stuart, E. A. (2015) · Statistics in Medicine · doi:10.1002/sim.6607 · Cited by 4,495
Self-citationTaquet, M., Geddes, J. R., Husain, M., Luciano, S., & Harrison, P. J. (2021) · The Lancet Psychiatry · doi:10.1016/s2215-0366(21)00084-5 · Cited by 2,189
“HRs for COVID-19 diagnosis compared with the additional four index events showed more variation with time, partly reflecting the natural history of the comparator condition (appendix, pp 16–19, 36).”
Wang, X., & Cheng, Z. (2020) · CHEST · doi:10.1016/j.chest.2020.03.012 · Cited by 2,214
Taquet, M., Luciano, S., Geddes, J. R., & Harrison, P. J. (2021) · The Lancet Psychiatry · doi:10.1016/s2215-0366(20)30462-4 · Cited by 1,484
Balance diagnostics after propensity score matching
written on behalf of AME Big-Data Clinical Trial Collaborative Group, Zhang, Z., Kim, H. J., Lonjon, G., & Zhu, Y. (2019) · Annals of Translational Medicine · doi:10.21037/atm.2018.12.10 · Cited by 1,031
Burden of Diabetic Foot Ulcers for Medicare and Private Insurers
Rice, J. B., Desai, U., Cummings, A. K. G., Birnbaum, H. G., Skornicki, M., & Parsons, N. B. (2014) · Diabetes Care · doi:10.2337/dc13-2176 · Cited by 624
Craig, P., Katikireddi, S. V., Leyland, A., & Popham, F. (2017) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-031816-044327 · Cited by 570
On the Go: how mobile shopping affects customer purchase behavior.
Wang, R. J.-H., Malthouse, E. C., & Krishnamurthi, L. (2015) · Journal of Retailing · doi:10.1016/j.jretai.2015.01.002 · Cited by 559
Dekkers, O. M., Vandenbroucke, J. P., Cevallos, M., Renehan, A. G., Altman, D. G., & Egger, M. (2019) · PLOS Medicine · doi:10.1371/journal.pmed.1002742 · Cited by 525
Risks and burdens of incident diabetes in long COVID: a cohort study
Xie, Y., & Al-Aly, Z. (2022) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(22)00044-4 · Cited by 564
Karges, B., Schwandt, A., Heidtmann, B., Kordonouri, O., Binder, E., Schierloh, U., Boettcher, C., Kapellen, T., Rosenbauer, J., & Holl, R. W. (2017) · JAMA · doi:10.1001/jama.2017.13994 · Cited by 452
Daru, J., Zamora, J., Fernández-Félix, B. M., Vogel, J., Oladapo, O. T., Morisaki, N., Tunçalp, Ö., Torloni, M. R., Mittal, S., Jayaratne, K., et al. (2018) · The Lancet Global Health · doi:10.1016/s2214-109x(18)30078-0 · Cited by 436
A Survey of Learning Causality with Data: Problems and Methods
Guo, R., Cheng, L., Li, J., Hahn, P. R., & Liu, H. (2021) · ACM Computing Surveys · doi:10.1145/3397269 · Cited by 362
Causal Intervention for Weakly-Supervised Semantic Segmentation
Zhang, D., Zhang, H., Tang, J., Hua, X., & Sun, Q. (2020) · arXiv · doi:10.48550/arxiv.2009.12547 · Cited by 353
Grool, A. M., Aglipay, M., Momoli, F., Meehan, W. P., Freedman, S. B., Yeates, K. O., Gravel, J., Gagnon, I., Boutis, K., Meeuwisse, W., et al. (2016) · JAMA · doi:10.1001/jama.2016.17396 · Cited by 346
Ziff, O. J., Lane, D. A., Samra, M., Griffith, M., Kirchhof, P., Lip, G. Y. H., Steeds, R. P., Townend, J., & Kotecha, D. (2015) · BMJ · doi:10.1136/bmj.h4451 · Cited by 341
Kumbhani, D. J., Steg, P. G., Cannon, C. P., Eagle, K. A., Smith, S. C., Goto, S., Ohman, E. M., Elbez, Y., Sritara, P., Baumgartner, I., et al. (2014) · European Heart Journal · doi:10.1093/eurheartj/ehu080 · Cited by 318
Food insecurity and child health
Thomas, M. M. C., Miller, D. P., & Morrissey, T. W. (2019) · Pediatrics · doi:10.1542/peds.2019-0397 · Cited by 319
Integrated Omics of Metastatic Colorectal Cancer
Li, C., Sun, Y.-D., Yu, G.-Y., Cui, J.-R., Lou, Z., Zhang, H., Huang, Y., Bai, C.-G., Deng, L.-L., Liu, P., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.08.002 · Cited by 297
Rich-Edwards, J. W., Kaiser, U. B., Chen, G. L., Manson, J. E., & Goldstein, J. M. (2018) · Endocrine Reviews · doi:10.1210/er.2017-00246 · Cited by 273
Pishvaian, M. J., Bender, R. J., Halverson, D., Rahib, L., Hendifar, A. E., Mikhail, S., Chung, V., Picozzi, V. J., Sohal, D., Blais, E. M., et al. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-0531 · Cited by 245
Association of Statin Dose With Amputation and Survival in Patients With Peripheral Artery Disease
Arya, S., Khakharia, A., Binney, Z. O., DeMartino, R. R., Brewster, L. P., Goodney, P. P., & Wilson, P. W. F. (2018) · Circulation · doi:10.1161/circulationaha.117.032361 · Cited by 257
Vaughn, V. M., Flanders, S. A., Snyder, A., Conlon, A., Rogers, M. A. M., Malani, A. N., McLaughlin, E., Bloemers, S., Srinivasan, A., Nagel, J., et al. (2019) · Annals of Internal Medicine · doi:10.7326/m18-3640 · Cited by 247
ED door-to-antibiotic time and long-term mortality in sepsis.
Peltan, I. D., Brown, S. M., Bledsoe, J. R., Sorensen, J., Samore, M. H., Allen, T. L., & Hough, C. L. (2019) · Chest · doi:10.1016/j.chest.2019.02.008 · Cited by 243
Taquet, M., Dercon, Q., & Harrison, P. J. (2022) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2022.04.013 · Cited by 246
Coping styles and mental health in response to societal changes during the COVID-19 pandemic
Gurvich, C., Thomas, N., Thomas, E. H., Hudaib, A.-R., Sood, L., Fabiatos, K., Sutton, K., Isaacs, A., Arunogiri, S., Sharp, G., et al. (2021) · International Journal of Social Psychiatry · doi:10.1177/0020764020961790 · Cited by 238
Diabetes medications and risk of Parkinson’s disease: a cohort study of patients with diabetes
Brauer, R., Wei, L., Ma, T., Athauda, D., Girges, C., Vijiaratnam, N., Auld, G., Whittlesea, C., Wong, I., & Foltynie, T. (2020) · Brain · doi:10.1093/brain/awaa262 · Cited by 233
Causal Inference About the Effects of Interventions From Observational Studies in Medical Journals
Dahabreh, I. J., & Bibbins-Domingo, K. (2024) · JAMA · doi:10.1001/jama.2024.7741 · Cited by 242
Hanauer, D. I., Graham, M. J., SEA-PHAGES, Betancur, L., Bobrownicki, A., Cresawn, S. G., Garlena, R. A., Jacobs-Sera, D., Kaufmann, N., Pope, W. H., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1718188115 · Cited by 217
Doumouras, A. G., Wong, J. A., Paterson, J. M., Lee, Y., Sivapathasundaram, B., Tarride, J.-E., Thabane, L., Hong, D., Yusuf, S., & Anvari, M. (2021) · Circulation · doi:10.1161/circulationaha.120.052386 · Cited by 209
Ballard, P. J., Hoyt, L. T., & Pachucki, M. C. (2019) · Child Development · doi:10.1111/cdev.12998 · Cited by 204
Glucagon-Like Peptide-1 Receptor Agonist Use and Residual Gastric Content Before Anesthesia
Sen, S., Potnuru, P. P., Hernandez, N., Goehl, C., Praestholm, C., Sridhar, S., & Nwokolo, O. O. (2024) · JAMA Surgery · doi:10.1001/jamasurg.2024.0111 · Cited by 202
Ali, M. S., Prieto-Alhambra, D., Lopes, L. C., Ramos, D., Bispo, N., Ichihara, M. Y., Pescarini, J. M., Williamson, E., Fiaccone, R. L., Barreto, M. L., et al. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00973 · Cited by 200
Best practices for analyzing large-scale health data from wearables and smartphone apps
Hicks, J. L., Althoff, T., Sosic, R., Kuhar, P., Bostjancic, B., King, A. C., Leskovec, J., & Delp, S. L. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0121-1 · Cited by 183
Behr, J., Prasse, A., Wirtz, H., Koschel, D., Pittrow, D., Held, M., Klotsche, J., Andreas, S., Claussen, M., Grohé, C., et al. (2020) · European Respiratory Journal · doi:10.1183/13993003.02279-2019 · Cited by 198
Karter, A. J., Parker, M. M., Moffet, H. H., Gilliam, L. K., & Dlott, R. (2021) · JAMA · doi:10.1001/jama.2021.6530 · Cited by 191
Westin, G. G., Armstrong, E. J., Bang, H., Yeo, K.-K., Anderson, D., Dawson, D. L., Pevec, W. C., Amsterdam, E. A., & Laird, J. R. (2014) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.09.073 · Cited by 189
AI Companions Reduce Loneliness
De Freitas, J., Oğuz-Uğuralp, Z., Uğuralp, A. K., & Puntoni, S. (2026) · Journal of Consumer Research · doi:10.1093/jcr/ucaf040 · Cited by 165
Chang, H.-Y., Singh, S., Mansour, O., Baksh, S., & Alexander, G. C. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.3034 · Cited by 163
Lim, L.-A., Gentili, S., Pardo, A., Kovanović, V., Whitelock-Wainwright, A., Gašević, D., & Dawson, S. (2021) · Learning and Instruction · doi:10.1016/j.learninstruc.2019.04.003 · Cited by 159
Evaluating artificial intelligence in medicine: phases of clinical research
Park, Y., Jackson, G. P., Foreman, M. A., Gruen, D., Hu, J., & Das, A. K. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa033 · Cited by 150
Transitions in romantic relationships and development of self-esteem.
Luciano, E. C., & Orth, U. (2017) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000109 · Cited by 149
Nduka, C. U., Stranges, S., Sarki, A. M., Kimani, P. K., & Uthman, O. A. (2016) · Journal of Human Hypertension · doi:10.1038/jhh.2015.97 · Cited by 147
Hwang, Y. J., Dixon, S. N., Reiss, J. P., Wald, R., Parikh, C. R., Gandhi, S., Shariff, S. Z., Pannu, N., Nash, D. M., Rehman, F., et al. (2014) · Annals of Internal Medicine · doi:10.7326/m13-2796 · Cited by 136
Lin, P., Zhong, Y., Fillit, H. M., Chen, E., & Neumann, P. J. (2016) · Journal of the American Geriatrics Society · doi:10.1111/jgs.14227 · Cited by 127
Purgato, M., Carswell, K., Tedeschi, F., Acarturk, C., Anttila, M., Au, T., Bajbouj, M., Baumgartner, J., Biondi, M., Churchill, R., et al. (2021) · Psychotherapy and Psychosomatics · doi:10.1159/000517504 · Cited by 132
Nielsen, S. F., & Nordestgaard, B. G. (2014) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(14)70173-1 · Cited by 125
EMPLOYEE FINANCIAL LITERACY AND RETIREMENT PLAN BEHAVIOR: A CASE STUDY
Clark, R., Lusardi, A., & Mitchell, O. S. (2017) · Economic Inquiry · doi:10.1111/ecin.12389 · Cited by 120
Fernández-Ruiz, M., Aguado, J. M., Almirante, B., Lora-Pablos, D., Padilla, B., Puig-Asensio, M., Montejo, M., García-Rodríguez, J., Pemán, J., Ruiz Pérez de Pipaón, M., et al. (2014) · Clinical Infectious Diseases · doi:10.1093/cid/ciu158 · Cited by 122
Stereotactic body radiotherapy for unresected pancreatic cancer: A nationwide review
de Geus, S. W. L., Eskander, M. F., Kasumova, G. G., Ng, S. C., Kent, T. S., Mancias, J. D., Callery, M. P., Mahadevan, A., & Tseng, J. F. (2017) · Cancer · doi:10.1002/cncr.30856 · Cited by 118
Alatorre, C., Fernández Landó, L., Yu, M., Brown, K., Montejano, L., Juneau, P., Mody, R., & Swindle, R. (2017) · Diabetes, Obesity and Metabolism · doi:10.1111/dom.12902 · Cited by 111
The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables
Lakkaraju, H., Kleinberg, J., Leskovec, J., Ludwig, J., & Mullainathan, S. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098066 · Cited by 138
Anderson, K. K., Norman, R., MacDougall, A., Edwards, J., Palaniyappan, L., Lau, C., & Kurdyak, P. (2018) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2017.17050480 · Cited by 103
Functional Status Impairment Is Associated With Unplanned Readmissions
Hoyer, E. H., Needham, D. M., Miller, J., Deutschendorf, A., Friedman, M., & Brotman, D. J. (2013) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2013.05.028 · Cited by 98
Thompson, D. R., Zurakowski, D., Haberkern, C. M., Stricker, P. A., & Meier, P. M. (2018) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000002454 · Cited by 100
Kinship and Nonrelative Foster Care: The Effect of Placement Type on Child Well-Being
Font, S. A. (2014) · Child Development · doi:10.1111/cdev.12241 · Cited by 95
O'Brien, E. C., Durheim, M. T., Gamerman, V., Garfinkel, S., Anstrom, K. J., Palmer, S. M., & Conoscenti, C. S. (2016) · BMJ Open Respiratory Research · doi:10.1136/bmjresp-2015-000108 · Cited by 75
Tang, A. S., Rankin, K. P., Cerono, G., Miramontes, S., Mills, H., Roger, J., Zeng, B., Nelson, C., Soman, K., Woldemariam, S., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00573-8 · Cited by 86
Harnessing EHR data for health research
Tang, A. S., Woldemariam, S. R., Miramontes, S., Norgeot, B., Oskotsky, T. T., & Sirota, M. (2024) · Nature Medicine · doi:10.1038/s41591-024-03074-8 · Cited by 87
Qeadan, F., McCunn, A., & Tingey, B. (2025) · Addiction · doi:10.1111/add.16679 · Cited by 90
Levis, M., Leonard Westgate, C., Gui, J., Watts, B. V., & Shiner, B. (2021) · Psychological Medicine · doi:10.1017/s0033291720000173 · Cited by 87
Self‐esteem development and life events: A review and integrative process framework
Reitz, A. K. (2022) · Social and Personality Psychology Compass · doi:10.1111/spc3.12709 · Cited by 90
“Ideally, propensity-score matching should be used, which allows to analyze life event data so that it mimics features of randomized controlled trials and thereby reduces the effects of confounding factors (Austin, 2011).”
MethodDigital health intervention in acute myocardial infarction
Marvel, F. A., Spaulding, E. M., Lee, M. A., Yang, W. E., Demo, R., Ding, J., Wang, J., Xun, H., Shah, L. M., Weng, D., et al. (2021) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.121.007741 · Cited by 84
Tsai, P.-C., Kuo, H.-T., Hung, C.-H., Tseng, K.-C., Lai, H.-C., Peng, C.-Y., Wang, J.-H., Chen, J.-J., Lee, P.-L., Chien, R.-N., et al. (2023) · Journal of Hepatology · doi:10.1016/j.jhep.2022.09.019 · Cited by 82
Saunders, R., Buckman, J. E. J., Stott, J., Leibowitz, J., Aguirre, E., John, A., Lewis, G., Cape, J., & Pilling, S. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.06.084 · Cited by 81
AI Companions Reduce Loneliness
De Freitas, J., Uğuralp, A. K., Uğuralp, Z., & Puntoni, S. (2024) · doi:10.2139/ssrn.4893097 · Cited by 80
J Carter, D., Daniel, R., Torrens, A. W., N Sanchez, M., Maciel, E. L. N., Bartholomay, P., Barreira, D. C., Rasella, D., Barreto, M. L., Rodrigues, L. C., et al. (2019) · BMJ Global Health · doi:10.1136/bmjgh-2018-001029 · Cited by 69
Weimann, H., Rylander, L., Albin, M., Skärbäck, E., Grahn, P., Östergren, P.-O., & Björk, J. (2015) · Health & Place · doi:10.1016/j.healthplace.2015.02.003 · Cited by 76
Lipska, K. J., Parker, M. M., Moffet, H. H., Huang, E. S., & Karter, A. J. (2018) · JAMA · doi:10.1001/jama.2018.7993 · Cited by 76
HTA methodology and value frameworks for evaluation and policy making for cell and gene therapies
Coyle, D., Durand-Zaleski, I., Farrington, J., Garrison, L., Graf von der Schulenburg, J.-M., Greiner, W., Longworth, L., Meunier, A., Moutié, A.-S., Palmer, S., et al. (2020) · The European Journal of Health Economics · doi:10.1007/s10198-020-01212-w · Cited by 71
Zuin, M., Cherubini, A., Volpato, S., Ferrucci, L., & Zuliani, G. (2022) · Scientific Reports · doi:10.1038/s41598-022-16476-w · Cited by 72
Olsen, S. J., Schirmer, H., Wilsgaard, T., Bønaa, K. H., & Hanssen, T. A. (2018) · European Journal of Preventive Cardiology · doi:10.1177/2047487318778088 · Cited by 72
Fendrich, M., Ives, M., Kurz, B., Becker, J., Vanderploeg, J., Bory, C., Lin, H.-J., & Plant, R. (2019) · Psychiatric Services · doi:10.1176/appi.ps.201800450 · Cited by 70
Marini, S., Crawford, K., Morotti, A., Lee, M. J., Pezzini, A., Moomaw, C. J., Flaherty, M. L., Montaner, J., Roquer, J., Jimenez-Conde, J., et al. (2019) · JAMA Neurology · doi:10.1001/jamaneurol.2018.4519 · Cited by 68
Goto, A., Goto, M., Terauchi, Y., Yamaguchi, N., & Noda, M. (2016) · Journal of the American Heart Association · doi:10.1161/jaha.115.002875 · Cited by 67
Gray, C. M., Grimson, F., Layton, D., Pocock, S., & Kim, J. (2020) · Drug Safety · doi:10.1007/s40264-020-00944-1 · Cited by 65
Ellis, T. E., Rufino, K. A., & Allen, J. G. (2017) · Psychiatry Research · doi:10.1016/j.psychres.2017.01.032 · Cited by 59
The Impact of Academic Library Resources on Undergraduates’ Degree Completion
Soria, K., & Fransen, J. (2017) · College & Research Libraries · doi:10.5860/crl.78.6.812 · Cited by 59
Desai, U., Kirson, N. Y., Ye, W., Mehta, N. R., Wen, J., & Andrews, J. S. (2019) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1016/j.dadm.2018.12.005 · Cited by 50
Digoxin discontinuation and outcomes in patients with heart failure with reduced ejection fraction
Malik, A., Masson, R., Singh, S., Wu, W.-C., Packer, M., Pitt, B., Waagstein, F., Morgan, C. J., Allman, R. M., Fonarow, G. C., et al. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.05.064 · Cited by 55
Risks of peri‐ and postoperative complications with glucagon‐like peptide‐1 receptor agonists
Klonoff, D. C., Kim, S. H., Galindo, R. J., Joseph, J. I., Garrett, V., Gombar, S., Aaron, R. E., Tian, T., & Kerr, D. (2024) · Diabetes, Obesity and Metabolism · doi:10.1111/dom.15636 · Cited by 58
Specialty Palliative Care Consultations for Nursing Home Residents With Dementia
Miller, S. C., Lima, J. C., Intrator, O., Martin, E., Bull, J., & Hanson, L. C. (2017) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2017.03.005 · Cited by 53
Clemens, T., Popham, F., & Boyle, P. (2015) · European Journal of Public Health · doi:10.1093/eurpub/cku136 · Cited by 52
Sugiura‐Ogasawara, M., Ebara, T., Yamada, Y., Shoji, N., Matsuki, T., Kano, H., Kurihara, T., Omori, T., Tomizawa, M., Miyata, M., et al. (2019) · American Journal of Reproductive Immunology · doi:10.1111/aji.13072 · Cited by 51
Primary care continuity and potentially avoidable hospitalization in persons with dementia
Godard‐Sebillotte, C., Strumpf, E., Sourial, N., Rochette, L., Pelletier, E., & Vedel, I. (2021) · Journal of the American Geriatrics Society · doi:10.1111/jgs.17049 · Cited by 50
Voight, A., & Velez, V. (2018) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2018.1431345 · Cited by 49
Casillas, R. A., Kim, B., Fischer, H., Zelada Getty, J. L., Um, S. S., & Coleman, K. J. (2017) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2017.03.011 · Cited by 46
Nori-Sarma, A., Benmarhnia, T., Rajiva, A., Azhar, G. S., Gupta, P., Pednekar, M. S., & Bell, M. L. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16122089 · Cited by 47
Barbosa, C., Cowell, A., Dowd, W., Landwehr, J., Aldridge, A., & Bray, J. (2017) · Addiction · doi:10.1111/add.13658 · Cited by 46
Furry animal allergen component sensitization and clinical outcomes in adult asthma and rhinitis
Nwaru, B. I., Suzuki, S., Ekerljung, L., Sjölander, S., Mincheva, R., Rönmark, E. P., Rådinger, M., Rönmark, E., Borres, M. P., Lundbäck, B., et al. (2019) · The Journal of Allergy and Clinical Immunology: In Practice · doi:10.1016/j.jaip.2018.12.018 · Cited by 45
Do Agile Managed Information Systems Projects Fail Due to a Lack of Emotional Intelligence?
Luong, T. T., Sivarajah, U., & Weerakkody, V. (2021) · Information Systems Frontiers · doi:10.1007/s10796-019-09962-6 · Cited by 45
Neurodegeneration and Stroke After Semaglutide and Tirzepatide in Patients With Diabetes and Obesity
Lin, H.-T., Tsai, Y.-F., Liao, P.-L., & Wei, J. C.-C. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.21016 · Cited by 43
Reputation Burning: Analyzing the Impact of Brand Sponsorship on Social Influencers
Cheng, M. (., & Zhang, S. (2025) · Management Science · doi:10.1287/mnsc.2023.00193 · Cited by 43
Assessing the economic burden of Alzheimer’s disease patients first diagnosed by specialists
Kirson, N. Y., Desai, U., Ristovska, L., Cummings, A. K. G., Birnbaum, H. G., Ye, W., Andrews, J. S., Ball, D., & Kahle-Wrobleski, K. (2016) · BMC Geriatrics · doi:10.1186/s12877-016-0303-5 · Cited by 42
Tramadol for noncancer pain and the risk of hyponatremia
Fournier, J.-P., Yin, H., Nessim, S. J., Montastruc, J.-L., & Azoulay, L. (2015) · The American Journal of Medicine · doi:10.1016/j.amjmed.2014.10.046 · Cited by 41
Graham, A. L., Papandonatos, G. D., Erar, B., & Stanton, C. A. (2015) · Health Psychology · doi:10.1037/hea0000278 · Cited by 40
Lega, I. C., Campitelli, M. A., Austin, P. C., Na, Y., Zahedi, A., Leung, F., Yu, C., Bronskill, S. E., Rochon, P. A., & Lipscombe, L. L. (2021) · Diabetologia · doi:10.1007/s00125-020-05370-7 · Cited by 40
Self-citationQuinn, P. D., Fine, K. L., Rickert, M. E., Sujan, A. C., Boersma, K., Chang, Z., Franck, J., Lichtenstein, P., Larsson, H., & D’Onofrio, B. M. (2020) · JAMA Pediatrics · doi:10.1001/jamapediatrics.2020.2539 · Cited by 38
Single-case experimental research designs.
Kazdin, A. E. (2015) · Methodological issues and strategies in clinical research (4th ed.). · doi:10.1037/14805-029 · Cited by 39
Metadata
- Authors
- Austin, P. C.
- Affiliations
- University of Toronto (CA)
- Corresponding author
- Austin, Peter C.
- ORCID
- Austin, Peter C.
- Venue
- Multivariate Behavioral Research
- Publisher
- Informa UK Limited
- Published
- 2011-05-31
- Volume
- 46
- Issue
- 3
- Pages
- 399-424
- ISSN
- 0027-3171 (ISSN-L)
- 1532-7906
- PMC
- PMC3144483
- OpenAlex
- W2036193982
- Cited by
- 12,242
- References
- 95
- Citations by year
- 2026: 1460 · 2025: 1302 · 2024: 1187 · 2023: 1145 · 2022: 1277 · 2021: 1253 · 2020: 1065 · 2019: 850 · 2018: 781 · 2017: 628 · 2016: 560 · 2015: 353 · 2014: 221 · 2013: 122 · 2012: 28
- FWCI
- 104.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Multivariate Behavioral Research (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Institute for Clinical Evaluative Sciences
- Ontario Ministry of Health and Long-Term Care
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- propensity score, confounding, observational studies, propensity score matching, stratification, inverse probability of treatment weighting, covariate adjustment, regression-based methods
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
