The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Reporting and Guidelines in Propensity Score Analysis: A Systematic Review of Cancer and Cancer Surgical Studies
Open accessAbstract
Background: : Propensity score (PS) analysis is increasingly being used in observational studies, especially in some cancer studies where random assignment is not feasible. This systematic review evaluates the use and reporting quality of PS analysis in oncology studies. Methods: : We searched PubMed to identify the use of PS methods in cancer studies (CS) and cancer surgical studies (CSS) in major medical, cancer, and surgical journals over time and critically evaluated 33 CS published in top medical and cancer journals in 2014 and 2015 and 306 CSS published up to November 26, 2015, without earlier date limits. The quality of reporting in PS analysis was evaluated. It was also compared over time and among journals with differing impact factors. All statistical tests were two-sided. Results: More than 50% of the publications with PS analysis from the past decade occurred within the past two years. Of the studies critically evaluated, a considerable proportion did not clearly provide the variables used to estimate PS (CS 12.1%, CSS 8.8%), incorrectly included non baseline variables (CS 3.4%, CSS 9.3%), neglected the comparison of baseline characteristics (CS 21.9%, CSS 15.6%), or did not report the matching algorithm utilized (CS 19.0%, CSS 36.1%). In CSS, the reporting of the matching algorithm improved in 2014 and 2015 ( P = .04), and the reporting of variables used to estimate PS was better in top surgery journals ( P = .008). However, there were no statistically significant differences for the inclusion of non baseline variables and reporting of comparability of baseline characteristics. Conclusions: The use of PS in cancer studies has dramatically increased recently, but there is substantial room for improvement in the quality of reporting even in top journals. Herein we have proposed reporting guidelines for PS analyses that are broadly applicable to different areas of medical research that will allow better evaluation and comparison across studies applying this approach.
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.
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 Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
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
von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., Vandenbroucke, J. P., & Initiative, F. T. S. (2007) · PLoS Medicine · doi:10.1371/journal.pmed.0040296 · Cited by 23,020
von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2008) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2007.11.008 · Cited by 17,893
von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., Vandenbroucke, J. P., & Initiative, F. T. S. (2007) · Annals of Internal Medicine · doi:10.7326/0003-4819-147-8-200710160-00010 · Cited by 17,796
SOME PRACTICAL GUIDANCE FOR THE IMPLEMENTATION OF PROPENSITY SCORE MATCHING
Caliendo, M., & Kopeinig, S. (2008) · Journal of Economic Surveys · doi:10.1111/j.1467-6419.2007.00527.x · Cited by 6,238
Matching Methods for Causal Inference: A Review and a Look Forward
Stuart, E. A. (2010) · Statistical Science · doi:10.1214/09-sts313 · Cited by 5,478
Estimating Causal Effects from Large Data Sets Using Propensity Scores
Rubin, D. B. (1997) · Annals of Internal Medicine · doi:10.7326/0003-4819-127-8_part_2-199710151-00064 · Cited by 2,877
Variable Selection for Propensity Score Models
Brookhart, M. A., Schneeweiss, S., Rothman, K. J., Glynn, R. J., Avorn, J., & Stürmer, T. (2006) · American Journal of Epidemiology · doi:10.1093/aje/kwj149 · Cited by 2,336
Modern Epidemiology 3rd edition
Rothman, K. J., Lash, T. L., & Greenland, S. (1986) · Cited by 1,735
Cited by
Statistical primer: propensity score matching and its alternatives
Benedetto, U., Head, S. J., Angelini, G. D., & Blackstone, E. H. (2018) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezy167 · Cited by 695
Chan, S. K. W., Chan, S. W. Y., Pang, H. H., Yan, K. K., Hui, C. L. M., Chang, W. C., Lee, E. H. M., & Chen, E. Y. H. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.0185 · Cited by 95
Self-citationWu, B., Luo, M., Wu, F., He, Z., Li, Y., & Xu, T. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.692828 · Cited by 63
Yi, W., Kim, B. H., Kim, M., Kim, J., Im, M., Ryang, S., Kim, E. H., Jeon, Y. K., Kim, S. S., & Kim, I. J. (2022) · Thyroid · doi:10.1089/thy.2021.0634 · Cited by 36
Lor, G. C. Y., Risch, H. A., Fung, W. T., Au Yeung, S. L., Wong, I. O. L., Zheng, W., & Pang, H. (2019) · Cancer Epidemiology · doi:10.1016/j.canep.2019.101577 · Cited by 27
Self-citation
Metadata
- Authors
- Yao, X. I., Wang, X., Speicher, P. J., Hwang, E. S., Cheng, P., Harpole, D. H., Berry, M. F., Schrag, D., & Pang, H. H.
- Venue
- JNCI: Journal of the National Cancer Institute
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-03-22
- Volume
- 109
- Issue
- 8
- ISSN
- 0027-8874 (ISSN-L)
- 1460-2105
- OpenAlex
- W2602458679
- Cited by
- 386
- References
- 41
- FWCI
- 5.84
- Type
- journal article
- Language
- en
- Open access
- bronze
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Reporting and Guidelines in Propensity Score Analysis: A Systematic Review of Cancer and Cancer Surgical Studies. https://app.cochat.ai/papers/10.1093/jnci/djw323
- The paper
- Yao, X. I., Wang, X., Speicher, P. J., Hwang, E. S., Cheng, P., Harpole, D. H., Berry, M. F., Schrag, D., & Pang, H. H. (2017). Reporting and Guidelines in Propensity Score Analysis: A Systematic Review of Cancer and Cancer Surgical Studies. JNCI: Journal of the National Cancer Institute. https://doi.org/10.1093/jnci/djw323
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.