Model Selection for Generalized Estimating Equations Accommodating Dropout Missingness
The generalized estimating equation (GEE) has been a popular tool for marginal regression analysis with longitudinal data, and its extension, the weighted GEE approach, can further accommodate data that are missing at random (MAR). Model selection methodologies for GEE, however, have not been system...
Saved in:
| Published in | Biometrics Vol. 68; no. 4; pp. 1046 - 1054 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Malden, USA
Blackwell Publishing Inc
01.12.2012
Wiley-Blackwell Blackwell Publishing Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0006-341X 1541-0420 1541-0420 |
| DOI | 10.1111/j.1541-0420.2012.01758.x |
Cover
| Abstract | The generalized estimating equation (GEE) has been a popular tool for marginal regression analysis with longitudinal data, and its extension, the weighted GEE approach, can further accommodate data that are missing at random (MAR). Model selection methodologies for GEE, however, have not been systematically developed to allow for missing data. We propose the missing longitudinal information criterion (MLIC) for selection of the mean model, and the MLIC for correlation (MLICC) for selection of the correlation structure in GEE when the outcome data are subject to dropout/monotone missingness and are MAR. Our simulation results reveal that the MLIC and MLICC are effective for variable selection in the mean model and selecting the correlation structure, respectively. We also demonstrate the remarkable drawbacks of naively treating incomplete data as if they were complete and applying the existing GEE model selection method. The utility of proposed method is further illustrated by two real applications involving missing longitudinal outcome data. |
|---|---|
| AbstractList | The generalized estimating equation (GEE) has been a popular tool for marginal regression analysis with longitudinal data, and its extension, the weighted GEE approach, can further accommodate data that are missing at random (MAR). Model selection methodologies for GEE, however, have not been systematically developed to allow for missing data. We propose the missing longitudinal information criterion (MLIC) for selection of the mean model, and the MLIC for correlation (MLICC) for selection of the correlation structure in GEE when the outcome data are subject to dropout/monotone missingness and are MAR. Our simulation results reveal that the MLIC and MLICC are effective for variable selection in the mean model and selecting the correlation structure, respectively. We also demonstrate the remarkable drawbacks of naively treating incomplete data as if they were complete and applying the existing GEE model selection method. The utility of proposed method is further illustrated by two real applications involving missing longitudinal outcome data. The generalized estimating equation (GEE) has been a popular tool for marginal regression analysis with longitudinal data, and its extension, the weighted GEE approach, can further accommodate data that are missing at random (MAR). Model selection methodologies for GEE, however, have not been systematically developed to allow for missing data. We propose the missing longitudinal information criterion (MLIC) for selection of the mean model, and the MLIC for correlation (MLICC) for selection of the correlation structure in GEE when the outcome data are subject to dropout/monotone missingness and are MAR. Our simulation results reveal that the MLIC and MLICC are effective for variable selection in the mean model and selecting the correlation structure, respectively. We also demonstrate the remarkable drawbacks of naively treating incomplete data as if they were complete and applying the existing GEE model selection method. The utility of proposed method is further illustrated by two real applications involving missing longitudinal outcome data.The generalized estimating equation (GEE) has been a popular tool for marginal regression analysis with longitudinal data, and its extension, the weighted GEE approach, can further accommodate data that are missing at random (MAR). Model selection methodologies for GEE, however, have not been systematically developed to allow for missing data. We propose the missing longitudinal information criterion (MLIC) for selection of the mean model, and the MLIC for correlation (MLICC) for selection of the correlation structure in GEE when the outcome data are subject to dropout/monotone missingness and are MAR. Our simulation results reveal that the MLIC and MLICC are effective for variable selection in the mean model and selecting the correlation structure, respectively. We also demonstrate the remarkable drawbacks of naively treating incomplete data as if they were complete and applying the existing GEE model selection method. The utility of proposed method is further illustrated by two real applications involving missing longitudinal outcome data. Summary The generalized estimating equation (GEE) has been a popular tool for marginal regression analysis with longitudinal data, and its extension, the weighted GEE approach, can further accommodate data that are missing at random (MAR). Model selection methodologies for GEE, however, have not been systematically developed to allow for missing data. We propose the missing longitudinal information criterion (MLIC) for selection of the mean model, and the MLIC for correlation (MLICC) for selection of the correlation structure in GEE when the outcome data are subject to dropout/monotone missingness and are MAR. Our simulation results reveal that the MLIC and MLICC are effective for variable selection in the mean model and selecting the correlation structure, respectively. We also demonstrate the remarkable drawbacks of naively treating incomplete data as if they were complete and applying the existing GEE model selection method. The utility of proposed method is further illustrated by two real applications involving missing longitudinal outcome data. The generalized estimating equation (GEE) has been a popular tool for marginal regression analysis with longitudinal data, and its extension, the weighted GEE approach, can further accommodate data that are missing at random (MAR). Model selection methodologies for GEE, however, have not been systematically developed to allow for missing data. We propose the missing longitudinal information criterion (MLIC) for selection of the mean model, and the MLIC for correlation (MLICC) for selection of the correlation structure in GEE when the outcome data are subject to dropout/monotone missingness and are MAR. Our simulation results reveal that the MLIC and MLICC are effective for variable selection in the mean model and selecting the correlation structure, respectively. We also demonstrate the remarkable drawbacks of naively treating incomplete data as if they were complete and applying the existing GEE model selection method. The utility of proposed method is further illustrated by two real applications involving missing longitudinal outcome data. [PUBLICATION ABSTRACT] Summary The generalized estimating equation (GEE) has been a popular tool for marginal regression analysis with longitudinal data, and its extension, the weighted GEE approach, can further accommodate data that are missing at random (MAR). Model selection methodologies for GEE, however, have not been systematically developed to allow for missing data. We propose the missing longitudinal information criterion (MLIC) for selection of the mean model, and the MLIC for correlation (MLICC) for selection of the correlation structure in GEE when the outcome data are subject to dropout/monotone missingness and are MAR. Our simulation results reveal that the MLIC and MLICC are effective for variable selection in the mean model and selecting the correlation structure, respectively. We also demonstrate the remarkable drawbacks of naively treating incomplete data as if they were complete and applying the existing GEE model selection method. The utility of proposed method is further illustrated by two real applications involving missing longitudinal outcome data. |
| Author | Shen, Chung‐Wei Chen, Yi‐Hau |
| Author_xml | – sequence: 1 fullname: Shen, Chung‐Wei – sequence: 2 fullname: Chen, Yi‐Hau |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22463099$$D View this record in MEDLINE/PubMed |
| BookMark | eNqVkU1v1DAQhi1URLeFnwBE4sIly_gzzgWpLdulUrc99ANuljeZVFmy8dZOxJZfj9OUPfQC-GKP32fesWcOyF7rWiQkoTClcX1aTakUNAXBYMqAsinQTOrp9gWZ7IQ9MgEAlXJBv--TgxBWMcwlsFdknzGhOOT5hNwuXIlNcoUNFl3t2qRyPplji9429S8sk1no6rXt6vYumd33dmBCclQUbr125Xj_xbuN67tkUYcQ4xZDeE1eVrYJ-OZpPyQ3p7Prk6_p-eX87OToPC0klTqliLZUWV4pWeRUMU2XtpJY5goEZlzzpcoZaK4lUKwsE4VWFV9iCaKQFi0_JB9H34139z2GzqzrUGDT2BZdHwxVmZYZVYr9HWUZp0P3ZEQ_PENXrvdt_EikBGMqPlVE6t0T1S_XWJqNj43yD-ZPcyOgR6DwLgSP1Q6hYIY5mpUZCpphXGaYo3mco9nG1M_PUou6e2x-523d_IfBz7rBh38ubI7PLhfDMRq8HQ1WoXN-ZyCoBgWMRz0d9Tp0uN3p1v8wKuOZNN8u5ubidn4tM7gwp5F_P_KVdcbe-TqYm6tYWgJQoQEE_w0WVdc0 |
| CODEN | BIOMA5 |
| CitedBy_id | crossref_primary_10_1002_env_2847 crossref_primary_10_1161_STROKEAHA_123_043605 crossref_primary_10_1002_bimj_201400045 crossref_primary_10_1016_j_jpsychires_2015_07_027 crossref_primary_10_1111_biom_12869 crossref_primary_10_1002_sim_7801 crossref_primary_10_1016_j_spasta_2020_100476 crossref_primary_10_1214_15_AOAS899 crossref_primary_10_1080_03610918_2018_1468457 crossref_primary_10_1093_biostatistics_kxv024 crossref_primary_10_1016_j_drugalcdep_2019_06_018 crossref_primary_10_3390_ijerph18136940 crossref_primary_10_1093_bib_bbad309 crossref_primary_10_1002_pst_2261 crossref_primary_10_1097_JCP_0000000000000633 crossref_primary_10_2478_fcds_2021_0019 crossref_primary_10_1155_2014_303728 crossref_primary_10_1186_s12936_019_2885_9 crossref_primary_10_1080_24754269_2018_1522481 crossref_primary_10_1111_sjos_12160 crossref_primary_10_1007_s00362_019_01115_w crossref_primary_10_1080_28338073_2024_2363550 crossref_primary_10_1111_biom_13060 crossref_primary_10_1002_wics_1402 crossref_primary_10_1186_s12859_018_2023_7 crossref_primary_10_1111_1475_6773_13230 crossref_primary_10_1097_MD_0000000000003488 crossref_primary_10_29220_CSAM_2023_30_4_355 crossref_primary_10_1016_j_apmr_2018_01_022 crossref_primary_10_1002_bimj_201200236 crossref_primary_10_9758_cpn_2020_18_1_136 crossref_primary_10_1002_sim_9411 crossref_primary_10_1080_02664763_2016_1238049 crossref_primary_10_1080_03610918_2022_2098332 crossref_primary_10_1016_j_drugalcdep_2018_06_024 crossref_primary_10_1111_acps_12324 crossref_primary_10_1111_biom_13330 crossref_primary_10_1002_bimj_201600195 crossref_primary_10_1007_s10182_014_0239_z crossref_primary_10_1093_ijnp_pyac069 crossref_primary_10_1111_pcn_12508 crossref_primary_10_1371_journal_pone_0066847 |
| Cites_doi | 10.1111/j.1541-0420.2005.00331.x 10.1198/016214504000000692 10.1093/biomet/73.1.13 10.1198/016214508000000184 10.1111/j.1467-9876.2005.00531.x 10.1080/01621459.1995.10476493 10.1037/0022-006X.62.2.285 10.1007/978-1-4899-3242-6 10.1002/sim.1241 10.1093/biomet/63.3.581 10.1002/sim.3489 10.2307/1267380 10.1111/1467-9469.00091 10.1111/j.0006-341X.2001.00120.x 10.1080/01621459.1998.10474094 |
| ContentType | Journal Article |
| Copyright | 2012 The International Biometric Society 2012, The International Biometric Society 2012, The International Biometric Society. |
| Copyright_xml | – notice: 2012 The International Biometric Society – notice: 2012, The International Biometric Society – notice: 2012, The International Biometric Society. |
| DBID | FBQ BSCLL AAYXX CITATION CGR CUY CVF ECM EIF NPM JQ2 7X8 7S9 L.6 |
| DOI | 10.1111/j.1541-0420.2012.01758.x |
| DatabaseName | AGRIS Istex CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Computer Science Collection MEDLINE - Academic AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest Computer Science Collection MEDLINE - Academic AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA MEDLINE - Academic ProQuest Computer Science Collection CrossRef MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: FBQ name: AGRIS url: http://www.fao.org/agris/Centre.asp?Menu_1ID=DB&Menu_2ID=DB1&Language=EN&Content=http://www.fao.org/agris/search?Language=EN sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Statistics Biology Mathematics |
| EISSN | 1541-0420 |
| EndPage | 1054 |
| ExternalDocumentID | 2848053401 22463099 10_1111_j_1541_0420_2012_01758_x BIOM1758 41806023 ark_67375_WNG_NVGT570N_F US201500148004 |
| Genre | article Research Support, Non-U.S. Gov't Journal Article Feature |
| GroupedDBID | --- -~X .3N .4S .DC .GA .GJ .Y3 05W 0R~ 10A 1OC 23N 2AX 2QV 3-9 31~ 33P 36B 3SF 4.4 44B 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 53G 5GY 5HH 5LA 5RE 5VS 66C 6J9 702 7PT 7X7 8-0 8-1 8-3 8-4 8-5 88E 88I 8AF 8C1 8FE 8FG 8FH 8FI 8FJ 8R4 8R5 8UM 930 A03 A8Z AAESR AAEVG AAHBH AAHHS AANHP AANLZ AAONW AASGY AAUAY AAWIL AAXRX AAYCA AAZKR AAZSN ABAWQ ABBHK ABCQN ABCUV ABDBF ABDFA ABEJV ABEML ABFAN ABGNP ABJCF ABJNI ABLJU ABMNT ABPPZ ABPVW ABTAH ABUWG ABXSQ ABXVV ABYWD ACAHQ ACBWZ ACCFJ ACCZN ACFBH ACGFO ACGFS ACGOD ACHJO ACIWK ACKIV ACMTB ACNCT ACPOU ACPRK ACRPL ACSCC ACTMH ACUHS ACXBN ACXQS ACYXJ ADBBV ADEOM ADIPN ADIZJ ADKYN ADMGS ADNBA ADNMO ADODI ADOZA ADULT ADVOB ADXAS ADZMN ADZOD AEEZP AEGXH AEIGN AEIMD AENEX AEOTA AEQDE AEUPB AEUYR AFBPY AFDVO AFEBI AFGKR AFKRA AFVYC AFWVQ AFZJQ AGLNM AGTJU AHMBA AIAGR AIHAF AIURR AIWBW AJAOE AJBDE AJNCP AJXKR ALAGY ALEEW ALIPV ALMA_UNASSIGNED_HOLDINGS ALRMG ALUQN AMBMR AMYDB APXXL ARAPS ARCSS ASPBG AS~ ATUGU AUFTA AVWKF AZBYB AZFZN AZQEC AZVAB BAFTC BBNVY BCRHZ BDRZF BENPR BFHJK BGLVJ BHBCM BHPHI BMNLL BMXJE BNHUX BPHCQ BROTX BRXPI BVXVI BY8 CAG CCPQU COF CS3 D-E D-F DCZOG DPXWK DQDLB DR2 DRFUL DRSTM DSRWC DWQXO DXH EAD EAP EBC EBD EBS ECEWR EDO EJD EMB EMK EMOBN EST ESX F00 F01 F04 F5P FBQ FD6 FEDTE FXEWX FYUFA G-S G.N GNUQQ GODZA GS5 H.T H.X HCIFZ HF~ HGD HMCUK HQ6 HVGLF HZI HZ~ IHE IPSME IX1 J0M JAAYA JAC JBMMH JBZCM JENOY JHFFW JKQEH JLEZI JLXEF JMS JPL JST K48 K6V K7- KOP L6V LATKE LC2 LC3 LEEKS LH4 LITHE LK8 LOXES LP6 LP7 LUTES LW6 LYRES M1P M2P M7P M7S MK4 MRFUL MRSTM MSFUL MSSTM MVM MXFUL MXSTM N04 N05 N9A NF~ NHB NU- O66 O9- OIG OJZSN OWPYF P0- P2P P2W P2X P4D P62 PHGZT PQQKQ PROAC PSQYO PTHSS Q.N Q11 Q2X QB0 R.K RNS ROL ROX RWL RX1 RXW SA0 SUPJJ SV3 TAE TN5 TUS UAP UB1 UKHRP V8K W8V W99 WBKPD WH7 WIH WIK WOHZO WQJ WYISQ X6Y XBAML XG1 XSW ZGI ZXP ZY4 ZZTAW ~02 ~IA ~KM ~WT 3V. AAPXW ADACV AELPN AEUQT AFFTP AFPWT AIBGX BSCLL ESTFP JSODD VQA WRC AAMMB AEFGJ AGORE AGQPQ AGXDD AIDQK AIDYY AAYXX AHGBF AJBYB CITATION PHGZM CGR CUY CVF ECM EIF NPM H13 JQ2 7X8 7S9 L.6 |
| ID | FETCH-LOGICAL-c5158-1eead679f65c916281baf5ed9604e7383b6920838501efa24c86f3bed04c5aea3 |
| IEDL.DBID | DR2 |
| ISSN | 0006-341X 1541-0420 |
| IngestDate | Fri Sep 05 17:19:29 EDT 2025 Mon Sep 08 13:37:47 EDT 2025 Wed Aug 13 08:14:02 EDT 2025 Thu Apr 03 06:55:10 EDT 2025 Thu Apr 24 22:53:53 EDT 2025 Tue Jul 01 00:58:02 EDT 2025 Wed Jan 22 16:58:15 EST 2025 Thu Jul 03 21:22:34 EDT 2025 Wed Oct 30 10:05:42 EDT 2024 Thu Apr 03 09:44:07 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Language | English |
| License | http://onlinelibrary.wiley.com/termsAndConditions#vor 2012, The International Biometric Society. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c5158-1eead679f65c916281baf5ed9604e7383b6920838501efa24c86f3bed04c5aea3 |
| Notes | http://dx.doi.org/10.1111/j.1541-0420.2012.01758.x ArticleID:BIOM1758 ark:/67375/WNG-NVGT570N-F istex:05B337E28E91C92D7368EFC08F2C0E935DF517EC SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 |
| PMID | 22463099 |
| PQID | 1242261624 |
| PQPubID | 35366 |
| PageCount | 9 |
| ParticipantIDs | proquest_miscellaneous_1678571662 proquest_miscellaneous_1273115415 proquest_journals_1242261624 pubmed_primary_22463099 crossref_primary_10_1111_j_1541_0420_2012_01758_x crossref_citationtrail_10_1111_j_1541_0420_2012_01758_x wiley_primary_10_1111_j_1541_0420_2012_01758_x_BIOM1758 jstor_primary_41806023 istex_primary_ark_67375_WNG_NVGT570N_F fao_agris_US201500148004 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | December 2012 |
| PublicationDateYYYYMMDD | 2012-12-01 |
| PublicationDate_xml | – month: 12 year: 2012 text: December 2012 |
| PublicationDecade | 2010 |
| PublicationPlace | Malden, USA |
| PublicationPlace_xml | – name: Malden, USA – name: United States – name: Washington |
| PublicationTitle | Biometrics |
| PublicationTitleAlternate | Biometrics |
| PublicationYear | 2012 |
| Publisher | Blackwell Publishing Inc Wiley-Blackwell Blackwell Publishing Ltd |
| Publisher_xml | – name: Blackwell Publishing Inc – name: Wiley-Blackwell – name: Blackwell Publishing Ltd |
| References | Gibbons, R. D. and Hedeker, D. (1994). Application of random effects probit regression model. Journal of Consulting and Clinical Psychology 62, 285-296. McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models , 2nd edition. London: Chapman and Hall. Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1995). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association 90, 106-121. Rubin, D. B. (1976). Inference and missing data. Biometrika 63, 581-592. Ye, J. M. (1998). On measuring and correcting the effects of data mining and model selection. Journal of the American Statistical Association 93, 120-131. Cantoni, E., Mills Flemming, J., and Ronchetti, E. (2005). Variable selection for marginal longitudinal generalized linear models. Biometrics 61, 507-514. Liang, K.-Y. and Zeger, S. L. (1986). Longitudinal data analysis with generalized linear models. Biometrika 73, 13-22. Wang, L., Zhou, J., and Qu, A. (2011). Penalized generalized estimating equations for high-dimensional longitudinal data analysis. Biometrics 67, DOI: 10.1111/j.1541-0420.2011.01678.x. Efron, B. (2004). The estimation of prediction error: Covariance penalties and cross-validation. Journal of the American Statistical Association 99, 619-632. Hin, L.-Y. and Wang, Y.-G. (2009). Working-correlation-structure identification in generalized estimating equations. Statistics in Medicine 28, 642-658. Johnson, B., Lin, D. Y., and Zeng, D. (2008). Penalized estimating functions and variable selection in semiparametric regression models. Journal of the American Statistical Association 103, 672-680. Pan, W. (2001). Akaike's information criterion in generalized estimating equations. Biometrics 57, 120-125. Preisser, J. S., Lohman, K. K., and Rathouz, P. J. (2002). Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random. Statistics in Medicine 21, 3035-3054. Mallows, C. L. (1973). Some comments on Cp. Technometrics 15, 661-675. Pan, W. (2002). Goodness-of-fit tests for GEE with correlated binary data. Scandinavian Journal of Statistics 29, 101-110. Verbeke, G., Fieuws, S., Lesaffre, E., Kato, B. S., Foreman, M. D., Broos, P. L. O., and Millisen, K. (2006). A comparison of procedures to correct for baseline differences in the analysis of continuous longitudinal data: A case study. Applied Statistics 55, 93-102. 2004; 99 1976; 63 2002; 29 1986; 73 1995; 90 1961; VI 2006; 55 1973; 15 2002; 21 2011; 67 2008; 103 2005; 61 1998; 93 2001; 57 1994; 62 1989 2009; 28 Bahadur R. R. (e_1_2_9_2_1) 1961 Wang L. (e_1_2_9_17_1) 2011; 67 e_1_2_9_11_1 e_1_2_9_10_1 e_1_2_9_13_1 e_1_2_9_12_1 e_1_2_9_8_1 e_1_2_9_7_1 e_1_2_9_6_1 e_1_2_9_5_1 e_1_2_9_4_1 e_1_2_9_3_1 e_1_2_9_9_1 e_1_2_9_15_1 e_1_2_9_14_1 e_1_2_9_16_1 e_1_2_9_18_1 |
| References_xml | – reference: Efron, B. (2004). The estimation of prediction error: Covariance penalties and cross-validation. Journal of the American Statistical Association 99, 619-632. – reference: Verbeke, G., Fieuws, S., Lesaffre, E., Kato, B. S., Foreman, M. D., Broos, P. L. O., and Millisen, K. (2006). A comparison of procedures to correct for baseline differences in the analysis of continuous longitudinal data: A case study. Applied Statistics 55, 93-102. – reference: Wang, L., Zhou, J., and Qu, A. (2011). Penalized generalized estimating equations for high-dimensional longitudinal data analysis. Biometrics 67, DOI: 10.1111/j.1541-0420.2011.01678.x. – reference: Hin, L.-Y. and Wang, Y.-G. (2009). Working-correlation-structure identification in generalized estimating equations. Statistics in Medicine 28, 642-658. – reference: Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1995). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association 90, 106-121. – reference: Johnson, B., Lin, D. Y., and Zeng, D. (2008). Penalized estimating functions and variable selection in semiparametric regression models. Journal of the American Statistical Association 103, 672-680. – reference: Mallows, C. L. (1973). Some comments on Cp. Technometrics 15, 661-675. – reference: McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models , 2nd edition. London: Chapman and Hall. – reference: Preisser, J. S., Lohman, K. K., and Rathouz, P. J. (2002). Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random. Statistics in Medicine 21, 3035-3054. – reference: Rubin, D. B. (1976). Inference and missing data. Biometrika 63, 581-592. – reference: Pan, W. (2002). Goodness-of-fit tests for GEE with correlated binary data. Scandinavian Journal of Statistics 29, 101-110. – reference: Ye, J. M. (1998). On measuring and correcting the effects of data mining and model selection. Journal of the American Statistical Association 93, 120-131. – reference: Gibbons, R. D. and Hedeker, D. (1994). Application of random effects probit regression model. Journal of Consulting and Clinical Psychology 62, 285-296. – reference: Cantoni, E., Mills Flemming, J., and Ronchetti, E. (2005). Variable selection for marginal longitudinal generalized linear models. Biometrics 61, 507-514. – reference: Liang, K.-Y. and Zeger, S. L. (1986). Longitudinal data analysis with generalized linear models. Biometrika 73, 13-22. – reference: Pan, W. (2001). Akaike's information criterion in generalized estimating equations. Biometrics 57, 120-125. – volume: 73 start-page: 13 year: 1986 end-page: 22 article-title: Longitudinal data analysis with generalized linear models publication-title: Biometrika – volume: 57 start-page: 120 year: 2001 end-page: 125 article-title: Akaike’s information criterion in generalized estimating equations publication-title: Biometrics – volume: 55 start-page: 93 year: 2006 end-page: 102 article-title: A comparison of procedures to correct for baseline differences in the analysis of continuous longitudinal data: A case study publication-title: Applied Statistics – year: 1989 publication-title: Generalized Linear Models – volume: 93 start-page: 120 year: 1998 end-page: 131 article-title: On measuring and correcting the effects of data mining and model selection publication-title: Journal of the American Statistical Association – volume: 67 year: 2011 article-title: Penalized generalized estimating equations for high‐dimensional longitudinal data analysis publication-title: Biometrics – volume: 103 start-page: 672 year: 2008 end-page: 680 article-title: Penalized estimating functions and variable selection in semiparametric regression models publication-title: Journal of the American Statistical Association – volume: 63 start-page: 581 year: 1976 end-page: 592 article-title: Inference and missing data publication-title: Biometrika – volume: 29 start-page: 101 year: 2002 end-page: 110 article-title: Goodness‐of‐fit tests for GEE with correlated binary data publication-title: Scandinavian Journal of Statistics – volume: VI start-page: 158 year: 1961 end-page: 168 – volume: 99 start-page: 619 year: 2004 end-page: 632 article-title: The estimation of prediction error: Covariance penalties and cross‐validation publication-title: Journal of the American Statistical Association – volume: 62 start-page: 285 year: 1994 end-page: 296 article-title: Application of random effects probit regression model publication-title: Journal of Consulting and Clinical Psychology – volume: 90 start-page: 106 year: 1995 end-page: 121 article-title: Analysis of semiparametric regression models for repeated outcomes in the presence of missing data publication-title: Journal of the American Statistical Association – volume: 61 start-page: 507 year: 2005 end-page: 514 article-title: Variable selection for marginal longitudinal generalized linear models publication-title: Biometrics – volume: 15 start-page: 661 year: 1973 end-page: 675 article-title: Some comments on Cp publication-title: Technometrics – volume: 21 start-page: 3035 year: 2002 end-page: 3054 article-title: Performance of weighted estimating equations for longitudinal binary data with drop‐outs missing at random publication-title: Statistics in Medicine – volume: 28 start-page: 642 year: 2009 end-page: 658 article-title: Working‐correlation‐structure identification in generalized estimating equations publication-title: Statistics in Medicine – ident: e_1_2_9_3_1 doi: 10.1111/j.1541-0420.2005.00331.x – volume: 67 year: 2011 ident: e_1_2_9_17_1 article-title: Penalized generalized estimating equations for high‐dimensional longitudinal data analysis publication-title: Biometrics – ident: e_1_2_9_4_1 doi: 10.1198/016214504000000692 – ident: e_1_2_9_8_1 doi: 10.1093/biomet/73.1.13 – ident: e_1_2_9_7_1 doi: 10.1198/016214508000000184 – ident: e_1_2_9_16_1 doi: 10.1111/j.1467-9876.2005.00531.x – ident: e_1_2_9_14_1 doi: 10.1080/01621459.1995.10476493 – ident: e_1_2_9_5_1 doi: 10.1037/0022-006X.62.2.285 – ident: e_1_2_9_10_1 doi: 10.1007/978-1-4899-3242-6 – ident: e_1_2_9_13_1 doi: 10.1002/sim.1241 – ident: e_1_2_9_15_1 doi: 10.1093/biomet/63.3.581 – start-page: 158 volume-title: Studies in Item Analysis and Prediction year: 1961 ident: e_1_2_9_2_1 – ident: e_1_2_9_6_1 doi: 10.1002/sim.3489 – ident: e_1_2_9_9_1 doi: 10.2307/1267380 – ident: e_1_2_9_12_1 doi: 10.1111/1467-9469.00091 – ident: e_1_2_9_11_1 doi: 10.1111/j.0006-341X.2001.00120.x – ident: e_1_2_9_18_1 doi: 10.1080/01621459.1998.10474094 |
| SSID | ssj0009502 |
| Score | 2.2391145 |
| Snippet | The generalized estimating equation (GEE) has been a popular tool for marginal regression analysis with longitudinal data, and its extension, the weighted GEE... Summary The generalized estimating equation (GEE) has been a popular tool for marginal regression analysis with longitudinal data, and its extension, the... Summary The generalized estimating equation (GEE) has been a popular tool for marginal regression analysis with longitudinal data, and its extension, the... |
| SourceID | proquest pubmed crossref wiley jstor istex fao |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1046 |
| SubjectTerms | Algorithms Analytical estimating BIOMETRIC METHODOLOGY Biometrics biometry Biometry - methods Clinical outcomes Correlations Data Interpretation, Statistical Drug design Epidemiologic Methods equations Estimating techniques Longitudinal data Longitudinal Studies Missing data Modeling Models, Statistical Parametric models Regression Analysis Repeated measures Sample Size School dropouts |
| Title | Model Selection for Generalized Estimating Equations Accommodating Dropout Missingness |
| URI | https://api.istex.fr/ark:/67375/WNG-NVGT570N-F/fulltext.pdf https://www.jstor.org/stable/41806023 https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fj.1541-0420.2012.01758.x https://www.ncbi.nlm.nih.gov/pubmed/22463099 https://www.proquest.com/docview/1242261624 https://www.proquest.com/docview/1273115415 https://www.proquest.com/docview/1678571662 |
| Volume | 68 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Rb9MwED7BENJ4GFAYC4wpSIi3VHESO8njStsNpBaJraNvlh0709QphbWVtv167uI0WtGEJsRbk9pJc_3u8p1z-Q7go4gLrlMVBYYXeZAYrgOVKxMobowoLStUTm8jj8bieJJ8nfJpU_9E78I4fYh2wY08o47X5OBKLzadnCeYCidRSBVaURexxbMu8UkWC5LR73-P7ujvhk44nEq9EjbdLOq590Abd6rHpZojfyXTX69LF-8jpZsct75JDZ_DbH15rjZl1l0tdbe4_UP58f9c_wvYabisf-jA9xIe2aoDT113y5sOPBu1krCLDmwTrXWq0K_gjFqwXfondRMeRIaP1NlvFLAvbq3xBziQZlbn_uCXUyNf-NTZAt3GuP196u-wWvojhA5uU8h-DZPh4PTzcdB0eAgK5FGYvloEskjzUiBSmIiQQ6uSW0OKMTbF5FmLPEKSmPGQ2VJFSZGJMtbWhEnBlVXxLmxV88ruga9DZGJW57Wgu84SbWwZM1FqxoxOSutBuv43ZdHIn1MXjkt5Jw1Cg0oyqCSDytqg8toD1s786SRAHjBnDwEj1TlGajk5iWhdidZuMSR58KlGUXssdTWj6rqUyx_jIzk-OzrlaTiWQw92a5i1AxOWhQI5lgf7a9zJJuIsJPI0ZNJoQzzBh_ZrjBX0AEhVdr6iMSmJKyFn-8sYZC8ck2gRefDGYbr9ASQ-GGNKgbaskflgc8jel28j-vj2n2e-g23a7yqJ9mFrebWy75EPLvUBPDns9XvDg9rjfwM3t00U |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9NAEB5BEaIceARKDQWMhLg58mN3bR-BJk2hCRJNSm6rXe-6Qo0caBKp9Ncz43WsBlWoQtzymLWTyTfjbzbjbwDeiqTgOlVxYHiRB8xwHahcmUBxY0Rpo0LldDfycCQGE_ZpyqfNOCC6F8bpQ7QbbhQZdb6mAKcN6c0o5wxrYRaH1KIVdxFcPOsiobzDkHdQJbb_Nb6iwBs66XBq9mLRdLOt59ojbVyrbpdqjgyWnH-xbl68jpZustz6MtV_CLP1F3TdKWfd1VJ3i8s_tB__kwcewYOGzvrvHf4ewy1bdeCuG3D5qwP3h60q7KID28RsnTD0EzihKWwz_7iew4Pg8JE9-40I9vdLa_weGtLK6tTv_XSC5Aufhltg5Bj3-j6NeFgt_SGiB59T1n4Kk35v_HEQNEMeggKpFFawFrEs0rwUCJZIxEijVcmtIdEYm2L9rEUeI0_MeBjZUsWsyESZaGtCVnBlVbIDW9W8srvg6xDJmNV5remuM6aNLZNIlDqKjGal9SBd_5yyaBTQaRDHTF6phNChkhwqyaGydqi88CBqV_5wKiA3WLOLiJHqFJO1nBzHtLVE27eYlTx4V8OoPZY6P6MGu5TLb6MDOTo5GPM0HMm-Bzs1zlpDFmWhQJrlwd4aeLJJOguJVA3JNPoQT_CmfRvTBf0HpCo7X5FNSvpKSNv-YoMEhmMdLWIPnjlQtx-A9AcTrCrQlzU0b-wO-eHwy5AePv_nla_h3mA8PJJHh6PPL2CbbFxj0R5sLc9X9iXSw6V-VYf9b6DTT8E |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9NAEB5BEag98Ai0NRQwEuLmyI_dtX0EmrQFEhBt2txWu951hVI5pUmk0l_PjNexGlShCnHLY9ZJxt-Mv9mMvwF4K5KC61TFgeFFHjDDdaByZQLFjRGljQqV093Ig6HYH7FPYz5u-p_oXhinD9FuuFFk1PmaAvzclKtBzhmWwiwOqUMr7iK2eNZFPnmPCSQWRJC-x9cEeEOnHE69Xiwar3b13HiklUvV3VJNkcCS7y-XvYs3sdJVkltfpfqPYLL8fa45ZdJdzHW3uPpD-vH_OOAxPGzIrP_eoe8J3LFVB-678Za_OrAxaDVhZx1YJ17rZKGfwjHNYDvzD-spPAgNH7mz30hg_7iyxu-hIa2sTv3eTydHPvNptAXGjXGv79KAh8XcHyB28Dnl7Gcw6veOPu4HzYiHoEAihfWrRSSLNC8FQiUSMZJoVXJrSDLGplg9a5HHeDIzHka2VDErMlEm2pqQFVxZlWzCWjWt7Db4OkQqZnVeK7rrjGljyyQSpY4io1lpPUiXZ1MWjf45jeE4k9fqIHSoJIdKcqisHSovPYjaledOA-QWa7YRMFKdYqqWo8OYNpZo8xZzkgfvahS1x1IXE2qvS7k8Ge7J4fHeEU_Doex7sFnDrDVkURYKJFke7CxxJ5uUM5NI1JBKow_xA960b2OyoH-AVGWnC7JJSV0JSdtfbJC-cKyiRezBlsN0-wVIfTDBmgJ9WSPz1u6QHw6-Dujh839e-RoefNvtyy8Hw88vYJ1MXFfRDqzNLxb2JXLDuX5VB_1vT3JOcA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Model+Selection+for+Generalized+Estimating+Equations+Accommodating+Dropout+Missingness&rft.jtitle=Biometrics&rft.au=Shen%2C+Chung%E2%80%90Wei&rft.au=Chen%2C+Yi%E2%80%90Hau&rft.date=2012-12-01&rft.issn=0006-341X&rft.eissn=1541-0420&rft.volume=68&rft.issue=4&rft.spage=1046&rft.epage=1054&rft_id=info:doi/10.1111%2Fj.1541-0420.2012.01758.x&rft.externalDBID=n%2Fa&rft.externalDocID=10_1111_j_1541_0420_2012_01758_x |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0006-341X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0006-341X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0006-341X&client=summon |