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For each sampling fraction, we form samples of patients from which a physician (\u201cpatient\u2010sharing\u201d) network is constructed in which an edge between two physicians depicts that at least one patient in the sample encountered both of those physicians. The network is summarized using 18 network measures. For each network measure and sampling fraction, we compare the values determined from the sample and the full cohort of patients. Finally, we assess the sampling fraction that is needed to measure each network measure to specified levels of accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Collection\/Extraction Methods<\/jats:title><jats:p>We utilized administrative claims from the traditional (fee\u2010for\u2010service) Medicare.<\/jats:p><\/jats:sec><jats:sec><jats:title>Principal Findings<\/jats:title><jats:p>We found that measures of physician degree (the number of ties to other physicians) in the network and physician centrality (importance or prominence in the network) are learned quickly in the sense that a small sampling fraction suffices to accurately compute the measure. At the network level, network density (the proportion of possible edges that are present) was learned quickly while measures based on more complex configurations (subnetworks involving multiple actors) are learned relatively slowly with relative rates of learning depending on network size (the number of nodes).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The sampling fraction applied to Medicare patients has a highly heterogeneous effect across different network measures on the extent to which sample\u2010based network measures resemble those evaluated using the full cohort. Even random sampling of patients may yield physician networks that distort descriptive features of the network based on the full cohort, potentially resulting in biased results.<\/jats:p><\/jats:sec>","DOI":"10.1111\/1475-6773.13568","type":"journal-article","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T18:18:48Z","timestamp":1602699528000},"page":"323-333","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["The impact of sampling patients on measuring physician patient\u2010sharing networks using Medicare data"],"prefix":"10.1111","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5553-8874","authenticated-orcid":false,"given":"A. James","family":"O'Malley","sequence":"first","affiliation":[{"name":"Department of Biomedical Data Science Geisel School of Medicine at Dartmouth  Lebanon New Hampshire USA"},{"name":"The Dartmouth Institute for Health Policy and Clinical Practice Geisel School of Medicine at Dartmouth  Lebanon New Hampshire USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jukka\u2010Pekka","family":"Onnela","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Harvard T. H. Chan School of Public Health  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nancy L.","family":"Keating","sequence":"additional","affiliation":[{"name":"Department of Health Care Policy Harvard Medical School  Boston Massachusetts USA"},{"name":"Division of General Internal Medicine Brigham and Women's Hospital  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bruce E.","family":"Landon","sequence":"additional","affiliation":[{"name":"Department of Health Care Policy Harvard Medical School  Boston Massachusetts USA"},{"name":"Division of General Medicine Beth Israel Deaconess Medical Center  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,10,14]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.7565"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/MLR.0b013e31822dcef7"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1475-6773.2011.01262.x"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2012.7615"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/MLR.0000000000000107"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10742-016-0152-x"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1097\/MLR.0000000000000883"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0036250"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4190"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.12172"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-150-4-200902170-00004"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1475-6773.2011.01331.x"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djw330"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0204826"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8466"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10742-008-0041-z"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/tbm\/ibx015"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11606-019-04978-9"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41109-018-0084-1"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.29409"},{"key":"e_1_2_7_22_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2019.18586"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.1177\/004912418101000205"},{"key":"e_1_2_7_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/0378-3758(82)90059-3"},{"key":"e_1_2_7_25_1","unstructured":"EbbesP HuangZ RangaswamyA ThadakamallaHP.Sampling of large\u2010scale social networks: Insights from simulated networks. 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