{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T14:04:48Z","timestamp":1792159488315,"version":"4.4.0"},"reference-count":15,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2020,1,24]],"date-time":"2020-01-24T00:00:00Z","timestamp":1579824000000},"content-version":"am","delay-in-days":365,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#am"},{"start":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T00:00:00Z","timestamp":1548288000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["K01NS099343"],"award-info":[{"award-number":["K01NS099343"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS\u20101608540"],"award-info":[{"award-number":["DMS\u20101608540"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Can J Statistics"],"published-print":{"date-parts":[[2019,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>We propose a consistent and locally efficient method of estimating the model parameters of a logistic mixed effect model with random slopes. Our approach relaxes two typical assumptions: the random effects being normally distributed, and the covariates and random effects being independent of each other. Adhering to these assumptions is particularly difficult in health studies where, in many cases, we have limited resources to design experiments and gather data in long\u2010term studies, while new findings from other fields might emerge, suggesting the violation of such assumptions. So it is crucial to have an estimator that is robust to such violations; then we could make better use of current data harvested using various valuable resources. Our method generalizes the framework presented in Garcia &amp; Ma (2016) which also deals with a logistic mixed effect model but only considers a random intercept. A simulation study reveals that our proposed estimator remains consistent even when the independence and normality assumptions are violated. This contrasts favourably with the traditional maximum likelihood estimator which is likely to be inconsistent when there is dependence between the covariates and random effects. Application of this work to a study of Huntington's disease reveals that disease diagnosis can be enhanced using assessments of cognitive performance. <jats:italic>The Canadian Journal of Statistics<\/jats:italic> 47: 140\u2013156; 2019 \u00a9 2019 Statistical Society of Canada<\/jats:p>","DOI":"10.1002\/cjs.11482","type":"journal-article","created":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T23:11:36Z","timestamp":1548371496000},"page":"140-156","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A consistent estimator for logistic mixed effect models"],"prefix":"10.1002","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6520-554X","authenticated-orcid":false,"given":"Yizheng","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Statistics University of South Carolina  Columbia SC 29208 U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanyuan","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Statistics The Pennsylvania State University  University Park PA 16802 U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tanya P.","family":"Garcia","sequence":"additional","affiliation":[{"name":"Department of Statistics Texas A&amp;M University  College Station TX 77843 U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samiran","family":"Sinha","sequence":"additional","affiliation":[{"name":"Department of Statistics Texas A&amp;M University  College Station TX 77843 U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,1,24]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2003.12.009"},{"key":"e_1_2_8_3_1","unstructured":"Bates D. 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