{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T04:08:06Z","timestamp":1792555686972,"version":"4.5.0"},"reference-count":30,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2018,10,23]],"date-time":"2018-10-23T00:00:00Z","timestamp":1540252800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["106680\/B\/14\/Z"],"award-info":[{"award-number":["106680\/B\/14\/Z"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Biometrical J"],"published-print":{"date-parts":[[2019,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Many approaches for variable selection with multiply imputed data in the development of a prognostic model have been proposed. However, no method prevails as uniformly best. We conducted a simulation study with a binary outcome and a logistic regression model to compare two classes of variable selection methods in the presence of MI data: (I) Model selection on bootstrap data, using backward elimination based on AIC or lasso, and fit the final model based on the most frequently (e.g. <jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/bimj1946-math-0001.png\" xlink:title=\"urn:x-wiley:03233847:media:bimj1946:bimj1946-math-0001\"\/>) selected variables over all MI and bootstrap data sets; (II) Model selection on original MI data, using lasso. The final model is obtained by (i) averaging estimates of variables that were selected in any MI data set or (ii) in 50% of the MI data; (iii) performing lasso on the stacked MI data, and (iv) as in (iii) but using individual weights as determined by the fraction of missingness. In all lasso models, we used both the optimal penalty and the 1\u2010se rule. We considered recalibrating models to correct for overshrinkage due to the suboptimal penalty by refitting the linear predictor or all individual variables. We applied the methods on a real dataset of 951 adult patients with tuberculous meningitis to predict mortality within nine months. Overall, applying lasso selection with the 1\u2010se penalty shows the best performance, both in approach I and II. Stacking MI data is an attractive approach because it does not require choosing a selection threshold when combining results from separate MI data sets<\/jats:p>","DOI":"10.1002\/bimj.201700232","type":"journal-article","created":{"date-parts":[[2018,10,24]],"date-time":"2018-10-24T07:00:30Z","timestamp":1540364430000},"page":"343-356","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["A comparison of model selection methods for prediction in the presence of multiply imputed data"],"prefix":"10.1002","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9661-6835","authenticated-orcid":false,"given":"Le Thi Phuong","family":"Thao","sequence":"first","affiliation":[{"name":"Biostatistics group Oxford University Clinical Research Unit Ho Chi Minh City Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronald","family":"Geskus","sequence":"additional","affiliation":[{"name":"Biostatistics group Oxford University Clinical Research Unit Ho Chi Minh City Vietnam"},{"name":"Nuffield Department of Medicine University of Oxford Oxford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,10,23]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/71.1.1"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280214558972"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1198\/0003130043277"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.5783"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v033.i01"},{"key":"e_1_2_8_7_1","first-page":"1","article-title":"A simulation based method for assessing the statistical significance of logistic regression models after common variable selection procedures","volume":"918","author":"Grogan T. 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