PublisherDOIYearVolumeIssuePageTitleAuthor(s)Link
Genomics & Informatics10.5808/gi.2019.17.4.e472019174e47Pure additive contribution of genetic variants to a risk prediction model using propensity score matching: application to type 2 diabetesChanwoo Park, Nan Jiang, Taesung Parkhttp://genominfo.org/upload/pdf/gi-2019-17-4-e47.pdf, http://genominfo.org/journal/view.php?doi=10.5808/GI.2019.17.4.e47, http://genominfo.org/upload/pdf/gi-2019-17-4-e47.pdf
Antioxidants10.3390/antiox1106119620221161196Early Prediction for Prediabetes and Type 2 Diabetes Using the Genetic Risk Score and Oxidative Stress ScoreXimei Huang, Youngmin Han, Kyunghye Jang, Minjoo Kimhttps://www.mdpi.com/2076-3921/11/6/1196/pdf
Journal of Diabetes Investigation10.1111/jdi.12167201354428-434Risk of hospitalization for diabetic macrovascular complications and in-hospital mortality with irregular physician visits using propensity score matchingTakumi Nishi, Akira Babazono, Toshiki Maedahttps://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fjdi.12167, https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fjdi.12167, http://onlinelibrary.wiley.com/wol1/doi/10.1111/jdi.12167/fullpdf
Statistics in Medicine10.1002/sim.6880201635122074-2091Propensity score matching with clustered data. An application to the estimation of the impact of caesarean section on the Apgar scoreBruno Arpino, Massimo Cannashttps://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fsim.6880, http://onlinelibrary.wiley.com/wol1/doi/10.1002/sim.6880/fullpdf
PLOS ONE10.1371/journal.pone.020919720181312e0209197Assessing the dose-response relationship between number of office-based visits and hospitalizations for patients with type II diabetes using generalized propensity score matchingMichele Cecchini, Peter Smithhttp://dx.plos.org/10.1371/journal.pone.0209197
Pancreatology10.1016/j.pan.2020.05.0232020205860-866Comparing diabetes due to diseases of the exocrine pancreas to type 1 and type 2 diabetes using propensity score matchingStefanie Lanzinger, Wolfram Karges, Sigrun Merger, Markus Laimer, Ursula Lück, Christian Wagner, Karsten Milek, Reinhard W. Hollhttps://api.elsevier.com/content/article/PII:S1424390320301939?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S1424390320301939?httpAccept=text/plain
Diabetes Care10.2337/dc21-09742022453734-741Genetic Risk Score Enhances Coronary Artery Disease Risk Prediction in Individuals With Type 1 DiabetesRaija Lithovius, Anni A. Antikainen, Stefan Mutter, Erkka Valo, Carol Forsblom, Valma Harjutsalo, Niina Sandholm, Per-Henrik Groophttps://diabetesjournals.org/care/article-pdf/45/3/734/670481/dc210974.pdf, https://diabetesjournals.org/care/article-pdf/45/3/734/670481/dc210974.pdf
10.21203/rs.3.rs-42654/v12020Health behaviors and health services accessibility factors associated with diabetes: a propensity score matching analysisSongul Cinarogluhttps://www.researchsquare.com/article/rs-42654/v1, https://www.researchsquare.com/article/rs-42654/v1.html
Scientific Reports10.1038/s41598-018-26106-z201881Genetic risk score of common genetic variants for impaired fasting glucose and newly diagnosed type 2 diabetes influences oxidative stressMinjoo Kim, Minkyung Kim, Limin Huang, Sun Ha Jee, Jong Ho Leehttp://www.nature.com/articles/s41598-018-26106-z.pdf, http://www.nature.com/articles/s41598-018-26106-z, http://www.nature.com/articles/s41598-018-26106-z.pdf
Healthcare10.3390/healthcare10101894202210101894Effect of Household Type on the Prevalence of Metabolic Syndrome in Korea: Using Propensity Score MatchingJisu Park, Ilsu Parkhttps://www.mdpi.com/2227-9032/10/10/1894/pdf