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Semiparametric regression modeling for this type of data has received much attention as it provides more flexibility in studying the association between regression factors and a longitudinal outcome. An important problem here is how to select relevant variables and estimate their coefficients in semiparametric regression models when the number of covariates at baseline is large. The current penalization procedures in semiparametric regression models for longitudinal data do not account for informative observation times. We propose a variable selection procedure that is suitable for the estimation methods based on pseudo\u2010score functions. We investigate the asymptotic properties of penalized estimators and conduct simulation studies to illustrate the theoretical results. We also use the procedure for variable selection in semiparametric regression models for the STAR*D dataset from a multistage randomized clinical trial for treating major depressive disorder.<\/jats:p>","DOI":"10.1002\/sim.9417","type":"journal-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T23:49:03Z","timestamp":1650930543000},"page":"3281-3298","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Variable selection in semiparametric regression models for longitudinal data with informative observation times"],"prefix":"10.1002","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6191-4082","authenticated-orcid":false,"given":"Omidali","family":"Aghababaei Jazi","sequence":"first","affiliation":[{"name":"Department of Mathematical and Computational Sciences University of Toronto Mississauga Mississauga Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4265-1330","authenticated-orcid":false,"given":"Eleanor","family":"Pullenayegum","sequence":"additional","affiliation":[{"name":"Department of Child Health Evaluative Sciences The Hospital for Sick Children Toronto Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2008.01104.x"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/73.1.13"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006-341X.2002.00621.x"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxj019"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxv008"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.2307\/2532783"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/86.3.691"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214501750333018"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214505000000060"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jspi.2007.10.013"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3496"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6262"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000001060"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/tri.12895"},{"issue":"1","key":"e_1_2_8_16_1","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani R","year":"1996","journal-title":"J Royal Stat Soc Ser B (Methodol)"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214501753382273"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1015362185"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000000735"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214508000000184"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-AOS729"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006-341X.2001.00120.x"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214508000000788"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2009.01240.x"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1111\/1541-0420.00015"},{"key":"e_1_2_8_27_1","unstructured":"DziakJJ.Penalized quadratic inference functions for variable selection in longitudinal research; 2006."},{"key":"e_1_2_8_28_1","unstructured":"XuP WuP WangY ZhuL.A GEE based shrinkage estimation for the generalized linear model in longitudinal data analysis. 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