Improving power in small-sample longitudinal studies when using generalized estimating equations
Generalized estimating equations (GEE) are often used for the marginal analysis of longitudinal data. Although much work has been performed to improve the validity of GEE for the analysis of data arising from small‐sample studies, little attention has been given to power in such settings. Therefore,...
Saved in:
| Published in | Statistics in medicine Vol. 35; no. 21; pp. 3733 - 3744 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
England
Blackwell Publishing Ltd
20.09.2016
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0277-6715 1097-0258 1097-0258 |
| DOI | 10.1002/sim.6967 |
Cover
Loading…
| Abstract | Generalized estimating equations (GEE) are often used for the marginal analysis of longitudinal data. Although much work has been performed to improve the validity of GEE for the analysis of data arising from small‐sample studies, little attention has been given to power in such settings. Therefore, we propose a valid GEE approach to improve power in small‐sample longitudinal study settings in which the temporal spacing of outcomes is the same for each subject. Specifically, we use a modified empirical sandwich covariance matrix estimator within correlation structure selection criteria and test statistics. Use of this estimator can improve the accuracy of selection criteria and increase the degrees of freedom to be used for inference. The resulting impacts on power are demonstrated via a simulation study and application example. Copyright © 2016 John Wiley & Sons, Ltd. |
|---|---|
| AbstractList | Generalized estimating equations (GEE) are often used for the marginal analysis of longitudinal data. Although much work has been performed to improve the validity of GEE for the analysis of data arising from small-sample studies, little attention has been given to power in such settings. Therefore, we propose a valid GEE approach to improve power in small-sample longitudinal study settings in which the temporal spacing of outcomes is the same for each subject. Specifically, we use a modified empirical sandwich covariance matrix estimator within correlation structure selection criteria and test statistics. Use of this estimator can improve the accuracy of selection criteria and increase the degrees of freedom to be used for inference. The resulting impacts on power are demonstrated via a simulation study and application example. Generalized estimating equations (GEE) are often used for the marginal analysis of longitudinal data. Although much work has been performed to improve the validity of GEE for the analysis of data arising from small‐sample studies, little attention has been given to power in such settings. Therefore, we propose a valid GEE approach to improve power in small‐sample longitudinal study settings in which the temporal spacing of outcomes is the same for each subject. Specifically, we use a modified empirical sandwich covariance matrix estimator within correlation structure selection criteria and test statistics. Use of this estimator can improve the accuracy of selection criteria and increase the degrees of freedom to be used for inference. The resulting impacts on power are demonstrated via a simulation study and application example. Copyright © 2016 John Wiley & Sons, Ltd. Generalized estimating equations (GEE) are often used for the marginal analysis of longitudinal data. Although much work has been performed to improve the validity of GEE for the analysis of data arising from small-sample studies, little attention has been given to power in such settings. Therefore, we propose a valid GEE approach to improve power in small-sample longitudinal study settings in which the temporal spacing of outcomes is the same for each subject. Specifically, we use a modified empirical sandwich covariance matrix estimator within correlation structure selection criteria and test statistics. Use of this estimator can improve the accuracy of selection criteria and increase the degrees of freedom to be used for inference. The resulting impacts on power are demonstrated via a simulation study and application example. Copyright © 2016 John Wiley & Sons, Ltd.Generalized estimating equations (GEE) are often used for the marginal analysis of longitudinal data. Although much work has been performed to improve the validity of GEE for the analysis of data arising from small-sample studies, little attention has been given to power in such settings. Therefore, we propose a valid GEE approach to improve power in small-sample longitudinal study settings in which the temporal spacing of outcomes is the same for each subject. Specifically, we use a modified empirical sandwich covariance matrix estimator within correlation structure selection criteria and test statistics. Use of this estimator can improve the accuracy of selection criteria and increase the degrees of freedom to be used for inference. The resulting impacts on power are demonstrated via a simulation study and application example. Copyright © 2016 John Wiley & Sons, Ltd. Generalized estimating equations (GEE) are often used for the marginal analysis of longitudinal data. Although much work has been done to improve the validity of GEE for the analysis of data arising from small-sample studies, little attention has been given to power in such settings. Therefore, we propose a valid GEE approach to improve power in small-sample longitudinal study settings in which the temporal spacing of outcomes is the same for each subject. Specifically, we use a modified empirical sandwich covariance matrix estimator within correlation structure selection criteria and test statistics. Use of this estimator can improve the accuracy of selection criteria and increase the degrees of freedom to be used for inference. The resulting impacts on power are demonstrated via a simulation study and application example. |
| Author | Westgate, Philip M. Burchett, Woodrow W. |
| AuthorAffiliation | 1 Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY 40536, U.S.A 2 Department of Statistics, College of Arts and Sciences, University of Kentucky, Lexington, KY 40536, U.S.A |
| AuthorAffiliation_xml | – name: 1 Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY 40536, U.S.A – name: 2 Department of Statistics, College of Arts and Sciences, University of Kentucky, Lexington, KY 40536, U.S.A |
| Author_xml | – sequence: 1 givenname: Philip M. surname: Westgate fullname: Westgate, Philip M. email: Correspondence to: Philip M. Westgate, Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY 40536, U.S.A, philip.westgate@uky.edu organization: Department of Biostatistics, College of Public Health, University of Kentucky, KY, 40536, Lexington, U.S.A – sequence: 2 givenname: Woodrow W. surname: Burchett fullname: Burchett, Woodrow W. organization: Department of Statistics, College of Arts and Sciences, University of Kentucky, KY, 40536, Lexington, U.S.A |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27090375$$D View this record in MEDLINE/PubMed |
| BookMark | eNp1kU9v1DAQxS1URLcFiU-AInHhkq3_xHZyQUILbLdqiypAHI03md26OHZqJ92WT49D24VWcLLl-c3zmzd7aMd5Bwi9JHhKMKYH0bRTUQn5BE0IrmSOKS930ARTKXMhCd9FezFeYEwIp_IZ2qUSV5hJPkHfF20X_JVx66zzGwiZcVlstbV51G1nIbPerU0_NMZpm8XxAjHbnIPLhjh2rcFB0Nb8hCaD2JtW9-MzXA7p4l18jp6utI3w4u7cR18_fvgyO8yPP80Xs3fHeS0IlXmTxmCCVEIUklal1JSuOMZci0JURVkXhDdNQUqxFARWNSyLWhNCKhDAlgwY20dvb3W7YdlCU4Prky3VheQo3CivjXpYceZcrf2VKirBGSmTwJs7geAvhzSKak2swVrtwA9RkRLLUpYp2YS-foRe-CGkgEYqZSwxwzhRr_52tLVyH_6fH-vgYwyw2iIEq3GvKu1VjXtN6PQRWpv-d8BpFmP_1ZDfNmyMhZv_CqvPi5OHvIk9XG95HX6oVJVcfTudq9mRmJ3NT96rM_YL5pHDrA |
| CODEN | SMEDDA |
| CitedBy_id | crossref_primary_10_3390_jcm10061159 crossref_primary_10_1016_j_jpeds_2020_02_037 crossref_primary_10_1177_0962280220958735 crossref_primary_10_1177_0962280220986193 crossref_primary_10_1111_insr_12447 crossref_primary_10_1186_s12874_024_02368_2 crossref_primary_10_1002_sim_7917 crossref_primary_10_1080_03610918_2018_1484476 crossref_primary_10_1002_sim_7199 crossref_primary_10_1111_jan_13194 crossref_primary_10_3389_fnhum_2023_1286238 crossref_primary_10_1016_j_jbusres_2021_03_007 crossref_primary_10_1002_sim_9744 crossref_primary_10_1080_10543406_2020_1814795 crossref_primary_10_1002_sim_8670 |
| Cites_doi | 10.1093/biomet/86.2.459 10.1080/00949655.2015.1089873 10.1093/biomet/73.1.13 10.1212/01.wnl.0000167607.63000.38 10.1093/biomet/88.3.901 10.1198/016214501753382309 10.1080/01688639508405155 10.2307/2531733 10.1093/biomet/90.1.29 10.1111/j.0006-341X.2001.01198.x 10.1111/j.1541-0420.2007.00764.x 10.1002/sim.1142 10.1002/sim.2502 10.1002/sim.3622 10.2307/2532890 10.1002/sim.3489 10.1017/S1041610213000367 10.1002/sim.4150 10.2307/2533336 10.1198/000313007X245122 10.1002/sim.5709 10.1080/10543406.2013.813521 10.1111/j.0006-341X.2001.00126.x 10.1080/13854046.2012.690451 10.1093/biomet/82.2.407 10.1002/sim.6106 10.1002/bimj.201200237 10.1002/bimj.200390021 |
| ContentType | Journal Article |
| Copyright | Copyright © 2016 John Wiley & Sons, Ltd. Copyright Wiley Subscription Services, Inc. Sep 20, 2016 |
| Copyright_xml | – notice: Copyright © 2016 John Wiley & Sons, Ltd. – notice: Copyright Wiley Subscription Services, Inc. Sep 20, 2016 |
| DBID | BSCLL AAYXX CITATION CGR CUY CVF ECM EIF NPM K9. 7X8 5PM |
| DOI | 10.1002/sim.6967 |
| DatabaseName | Istex CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Health & Medical Complete (Alumni) MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest Health & Medical Complete (Alumni) MEDLINE - Academic |
| DatabaseTitleList | ProQuest Health & Medical Complete (Alumni) CrossRef MEDLINE MEDLINE - Academic |
| 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 |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Statistics Public Health |
| EISSN | 1097-0258 |
| EndPage | 3744 |
| ExternalDocumentID | PMC4965318 4148297751 27090375 10_1002_sim_6967 SIM6967 ark_67375_WNG_CJ6CQGMD_Q |
| Genre | article Journal Article Feature |
| GrantInformation_xml | – fundername: National Institutes of Health funderid: UL1TR000117 – fundername: National Center for Research Resources and the National Center for Advancing Translational Sciences – fundername: National Institute on Aging funderid: R01 AG019241 – fundername: NCATS NIH HHS grantid: UL1 TR001998 – fundername: NIA NIH HHS grantid: R01 AG019241 – fundername: NCATS NIH HHS grantid: UL1 TR000117 |
| GroupedDBID | --- .3N .GA 05W 0R~ 10A 123 1L6 1OB 1OC 1ZS 33P 3SF 3WU 4.4 4ZD 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 53G 5RE 5VS 66C 6PF 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AAESR AAEVG AAHHS AANLZ AAONW AASGY AAWTL AAXRX AAZKR ABCQN ABCUV ABIJN ABJNI ABOCM ABPVW ACAHQ ACBWZ ACCFJ ACCZN ACGFS ACPOU ACXBN ACXQS ADBBV ADEOM ADIZJ ADKYN ADMGS ADOZA ADXAS ADZMN AEEZP AEIGN AEIMD AENEX AEQDE AEUQT AEUYR AFBPY AFFPM AFGKR AFPWT AFZJQ AHBTC AHMBA AITYG AIURR AIWBW AJBDE AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN AMBMR AMYDB ATUGU AUFTA AZBYB AZVAB BAFTC BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BSCLL BY8 CS3 D-E D-F DCZOG DPXWK DR2 DRFUL DRSTM DU5 EBD EBS EJD EMOBN F00 F01 F04 F5P G-S G.N GNP GODZA H.T H.X HBH HGLYW HHY HHZ HZ~ IX1 J0M JPC KQQ LATKE LAW LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ NNB O66 O9- OIG P2P P2W P2X P4D PALCI PQQKQ Q.N Q11 QB0 QRW R.K ROL RWI RX1 RYL SUPJJ SV3 TN5 UB1 V2E W8V W99 WBKPD WH7 WIB WIH WIK WJL WOHZO WQJ WRC WUP WWH WXSBR WYISQ XBAML XG1 XV2 ZZTAW ~IA ~WT AAHQN AAMNL AAYCA ACYXJ AFWVQ ALVPJ AAYXX AEYWJ AGHNM AGQPQ AGYGG AMVHM CITATION AAMMB AEFGJ AGXDD AIDQK AIDYY CGR CUY CVF ECM EIF NPM K9. 7X8 5PM |
| ID | FETCH-LOGICAL-c6127-d002361966472987a22f5005a646948c415dd4186b61efceb4ca1119e6e3b3e33 |
| IEDL.DBID | DR2 |
| ISSN | 0277-6715 1097-0258 |
| IngestDate | Thu Aug 21 17:56:42 EDT 2025 Fri Jul 11 16:03:31 EDT 2025 Sun Jul 13 04:13:43 EDT 2025 Mon Jul 21 06:01:55 EDT 2025 Thu Apr 24 22:58:24 EDT 2025 Tue Jul 01 03:28:09 EDT 2025 Wed Jan 22 16:46:21 EST 2025 Wed Oct 30 09:51:48 EDT 2024 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 21 |
| Keywords | empirical covariance matrix efficiency degrees of freedom correlation selection |
| Language | English |
| License | http://onlinelibrary.wiley.com/termsAndConditions#vor Copyright © 2016 John Wiley & Sons, Ltd. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c6127-d002361966472987a22f5005a646948c415dd4186b61efceb4ca1119e6e3b3e33 |
| Notes | National Institutes of Health - No. UL1TR000117 ArticleID:SIM6967 istex:376CA02E89C7CA44979D27882214F3264B54E445 National Institute on Aging - No. R01 AG019241 Supporting Info ItemSupporting Info Item National Center for Research Resources and the National Center for Advancing Translational Sciences ark:/67375/WNG-CJ6CQGMD-Q SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0003-2991-268X |
| OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/4965318 |
| PMID | 27090375 |
| PQID | 1811570300 |
| PQPubID | 48361 |
| PageCount | 12 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_4965318 proquest_miscellaneous_1807878258 proquest_journals_1811570300 pubmed_primary_27090375 crossref_primary_10_1002_sim_6967 crossref_citationtrail_10_1002_sim_6967 wiley_primary_10_1002_sim_6967_SIM6967 istex_primary_ark_67375_WNG_CJ6CQGMD_Q |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20 September 2016 |
| PublicationDateYYYYMMDD | 2016-09-20 |
| PublicationDate_xml | – month: 09 year: 2016 text: 20 September 2016 day: 20 |
| PublicationDecade | 2010 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: New York |
| PublicationTitle | Statistics in medicine |
| PublicationTitleAlternate | Statist. Med |
| PublicationYear | 2016 |
| Publisher | Blackwell Publishing Ltd Wiley Subscription Services, Inc |
| Publisher_xml | – name: Blackwell Publishing Ltd – name: Wiley Subscription Services, Inc |
| References | Pan W. On the robust variance estimator in generalised estimating equations. Biometrika 2001; 88:901-906. Mancl LA, DeRouen TA. A covariance estimator for GEE with improved small-sample properties. Biometrics 2001; 57:126-134. Liang K-Y, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73:13-22. Hin L-Y, Wang Y-G. Working-correlation-structure identification in generalized estimating equations. Statistics in Medicine 2009; 28:642-658. Caban-Holt A, Abner E, Kryscio RJ, Crowley JJ, Schmitt FA. Age-expanded normative data for the Ruff 2&7 Selective Attention Test: evaluating cognition in older males. The Clinical Neuropsychologist 2012; 26:751-768. Hin L-Y, Carey VJ, Wang Y-G. Criteria for working-correlation-structure selection in GEE. The American Statistician 2007; 61:360-364. Pan W, Wall MM. Small-sample adjustments in using the sandwich variance estimator in generalized estimating equations. Statistics in Medicine 2002; 21:1429-1441. Prentice RL. Correlated binary regression with covariates specific to each binary observation. Biometrics 1988; 44:1033-1048. McCaffrey DF, Bell RM. Improved hypothesis testing for coefficients in generalized estimating equations with small samples of clusters. Statistics in Medicine 2006; 25:4081-4098. Fan C, Zhang D, Zhang CH. A comparison of bias-corrected covariance estimators for generalized estimating equations. Journal of Biopharmaceutical Statistics 2013; 23:1172-1187. Fay MP, Graubard BI. Small-sample adjustments for wald-type tests using sandwich estimators. Biometrics 2001; 57:1198-1206. Wang M, Long Q. Modified robust variance estimator for generalized estimating equations with improved small-sample performance. Statistics in Medicine 2011; 30:1278-1291. Crowder M. On the use of a working correlation matrix in using generalised linear models for repeated measures. Biometrika 1995; 82:407-410. Westgate PM. That use an unstructured correlation matrix. Statistics in Medicine 2013; 32:2850-2858. Kauermann G, Carroll RJ. A note on the efficiency of sandwich covariance matrix estimation. Journal of the American Statistical Association 2001; 96:1387-1396. Westgate PM. A covariance correction that accounts for correlation estimation to improve finite-sample inference with generalized estimating equations: a study on its applicability with structured correlation matrices. Journal of Statistical Computation and Simulation 2016; 86:1891-1900. Morel JG, Bokossa MC, Neerchal NK. Small sample correction for the variance of GEE estimators. Biometrical Journal 2003; 45:395-409. Lu B, Preisser JS, Qaqish BF, Suchindran C, Bangdiwala SI, Wolfson M. A comparison of two bias-corrected covariance estimators for generalized estimating equations. Biometrics 2007; 63:935-941. Boekamp JR, Strauss ME, Adams N. Estimating premorbid intelligence in African-American and white elderly veterans using the American version of the national adult reading test. Journal of Clinical and Experimental Neuropsychology 1995; 17:645-653. Westgate PM. Improving the correlation structure selection approach for generalized estimating equations and balanced longitudinal data. Statistics in Medicine 2014; 33:2222-2237. Westgate PM. On small-sample inference in group randomized trials with binary outcomes and cluster-level covariates. Biometrical Journal 2013; 55:789-806. Chandler MJ, Lacritz LH, Hynan LS, Barnard HD, Allen G, Deschner M, Weiner MF, Cullum CM. A total score for the CERAD neuropsychological battery. Neurology 2005; 65:102-106. Shults J, Sun W, Tu X, Kim H, Amsterdam J, Hilbe JM, Ten-Have T. A comparison of several approaches for choosing between working correlation structures in generalized estimating equation analysis of longitudinal binary data. Statistics in Medicine 2009; 28:2338-2355. Fitzmaurice GM. A caveat concerning independence estimating equations with multivariate binary data. Biometrics 1995; 51:309-317. Wang Y-G, Carey V. Working correlation structure misspecification, estimation and covariate design: implications for generalised estimating equations performance. Biometrika 2003; 90:29-41. Mancl LA, Leroux BG. Efficiency of regression estimates for clustered data. Biometrics 1996; 52:500-511. Sutradhar BC, Das K. On the efficiency of regression estimators in generalised linear models for longitudinal data. Biometrika 1999; 86:459-465. Mathews M, Abner E, Caban-Holt A, Kryscio R, Schmitt F. CERAD practice effects and attrition bias in a dementia prevention trial. International Psychogeriatrics 2013; 25:1115-1123. 1995; 51 2013; 25 1986; 73 1995; 17 2013; 23 2011; 30 1996; 52 2005; 65 1999; 86 2001; 88 2009; 28 1995; 82 2003; 90 2013; 55 2013; 32 2006; 25 2002; 21 2016; 86 1988; 44 2007; 61 2007; 63 2012; 26 2001; 57 2001; 96 2014; 33 2003; 45 e_1_2_8_28_1 e_1_2_8_29_1 e_1_2_8_24_1 e_1_2_8_25_1 e_1_2_8_26_1 e_1_2_8_27_1 e_1_2_8_3_1 e_1_2_8_2_1 e_1_2_8_5_1 e_1_2_8_4_1 e_1_2_8_7_1 e_1_2_8_6_1 e_1_2_8_9_1 e_1_2_8_8_1 e_1_2_8_20_1 e_1_2_8_21_1 e_1_2_8_22_1 e_1_2_8_23_1 e_1_2_8_17_1 e_1_2_8_18_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_14_1 e_1_2_8_15_1 e_1_2_8_16_1 e_1_2_8_10_1 e_1_2_8_11_1 e_1_2_8_12_1 |
| References_xml | – reference: Westgate PM. A covariance correction that accounts for correlation estimation to improve finite-sample inference with generalized estimating equations: a study on its applicability with structured correlation matrices. Journal of Statistical Computation and Simulation 2016; 86:1891-1900. – reference: Shults J, Sun W, Tu X, Kim H, Amsterdam J, Hilbe JM, Ten-Have T. A comparison of several approaches for choosing between working correlation structures in generalized estimating equation analysis of longitudinal binary data. Statistics in Medicine 2009; 28:2338-2355. – reference: Sutradhar BC, Das K. On the efficiency of regression estimators in generalised linear models for longitudinal data. Biometrika 1999; 86:459-465. – reference: Prentice RL. Correlated binary regression with covariates specific to each binary observation. Biometrics 1988; 44:1033-1048. – reference: Boekamp JR, Strauss ME, Adams N. Estimating premorbid intelligence in African-American and white elderly veterans using the American version of the national adult reading test. Journal of Clinical and Experimental Neuropsychology 1995; 17:645-653. – reference: Hin L-Y, Carey VJ, Wang Y-G. Criteria for working-correlation-structure selection in GEE. The American Statistician 2007; 61:360-364. – reference: Mancl LA, DeRouen TA. A covariance estimator for GEE with improved small-sample properties. Biometrics 2001; 57:126-134. – reference: Crowder M. On the use of a working correlation matrix in using generalised linear models for repeated measures. Biometrika 1995; 82:407-410. – reference: Chandler MJ, Lacritz LH, Hynan LS, Barnard HD, Allen G, Deschner M, Weiner MF, Cullum CM. A total score for the CERAD neuropsychological battery. Neurology 2005; 65:102-106. – reference: Fitzmaurice GM. A caveat concerning independence estimating equations with multivariate binary data. Biometrics 1995; 51:309-317. – reference: Caban-Holt A, Abner E, Kryscio RJ, Crowley JJ, Schmitt FA. Age-expanded normative data for the Ruff 2&7 Selective Attention Test: evaluating cognition in older males. The Clinical Neuropsychologist 2012; 26:751-768. – reference: Kauermann G, Carroll RJ. A note on the efficiency of sandwich covariance matrix estimation. Journal of the American Statistical Association 2001; 96:1387-1396. – reference: Wang M, Long Q. Modified robust variance estimator for generalized estimating equations with improved small-sample performance. Statistics in Medicine 2011; 30:1278-1291. – reference: Mathews M, Abner E, Caban-Holt A, Kryscio R, Schmitt F. CERAD practice effects and attrition bias in a dementia prevention trial. International Psychogeriatrics 2013; 25:1115-1123. – reference: Mancl LA, Leroux BG. Efficiency of regression estimates for clustered data. Biometrics 1996; 52:500-511. – reference: Lu B, Preisser JS, Qaqish BF, Suchindran C, Bangdiwala SI, Wolfson M. A comparison of two bias-corrected covariance estimators for generalized estimating equations. Biometrics 2007; 63:935-941. – reference: Westgate PM. On small-sample inference in group randomized trials with binary outcomes and cluster-level covariates. Biometrical Journal 2013; 55:789-806. – reference: Fay MP, Graubard BI. Small-sample adjustments for wald-type tests using sandwich estimators. Biometrics 2001; 57:1198-1206. – reference: Morel JG, Bokossa MC, Neerchal NK. Small sample correction for the variance of GEE estimators. Biometrical Journal 2003; 45:395-409. – reference: Wang Y-G, Carey V. Working correlation structure misspecification, estimation and covariate design: implications for generalised estimating equations performance. Biometrika 2003; 90:29-41. – reference: Westgate PM. That use an unstructured correlation matrix. Statistics in Medicine 2013; 32:2850-2858. – reference: Westgate PM. Improving the correlation structure selection approach for generalized estimating equations and balanced longitudinal data. Statistics in Medicine 2014; 33:2222-2237. – reference: Hin L-Y, Wang Y-G. Working-correlation-structure identification in generalized estimating equations. Statistics in Medicine 2009; 28:642-658. – reference: Pan W, Wall MM. Small-sample adjustments in using the sandwich variance estimator in generalized estimating equations. Statistics in Medicine 2002; 21:1429-1441. – reference: McCaffrey DF, Bell RM. Improved hypothesis testing for coefficients in generalized estimating equations with small samples of clusters. Statistics in Medicine 2006; 25:4081-4098. – reference: Liang K-Y, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73:13-22. – reference: Fan C, Zhang D, Zhang CH. A comparison of bias-corrected covariance estimators for generalized estimating equations. Journal of Biopharmaceutical Statistics 2013; 23:1172-1187. – reference: Pan W. On the robust variance estimator in generalised estimating equations. Biometrika 2001; 88:901-906. – 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 – volume: 86 start-page: 459 year: 1999 end-page: 465 article-title: On the efficiency of regression estimators in generalised linear models for longitudinal data publication-title: Biometrika – volume: 61 start-page: 360 year: 2007 end-page: 364 article-title: Criteria for working‐correlation‐structure selection in GEE publication-title: The American Statistician – volume: 45 start-page: 395 year: 2003 end-page: 409 article-title: Small sample correction for the variance of GEE estimators publication-title: Biometrical Journal – volume: 28 start-page: 2338 year: 2009 end-page: 2355 article-title: A comparison of several approaches for choosing between working correlation structures in generalized estimating equation analysis of longitudinal binary data publication-title: Statistics in Medicine – volume: 52 start-page: 500 year: 1996 end-page: 511 article-title: Efficiency of regression estimates for clustered data publication-title: Biometrics – volume: 25 start-page: 1115 year: 2013 end-page: 1123 article-title: CERAD practice effects and attrition bias in a dementia prevention trial publication-title: International Psychogeriatrics – volume: 55 start-page: 789 year: 2013 end-page: 806 article-title: On small‐sample inference in group randomized trials with binary outcomes and cluster‐level covariates publication-title: Biometrical Journal – volume: 44 start-page: 1033 year: 1988 end-page: 1048 article-title: Correlated binary regression with covariates specific to each binary observation publication-title: Biometrics – volume: 26 start-page: 751 year: 2012 end-page: 768 article-title: Age‐expanded normative data for the Ruff 2&7 Selective Attention Test: evaluating cognition in older males publication-title: The Clinical Neuropsychologist – volume: 82 start-page: 407 year: 1995 end-page: 410 article-title: On the use of a working correlation matrix in using generalised linear models for repeated measures publication-title: Biometrika – volume: 63 start-page: 935 year: 2007 end-page: 941 article-title: A comparison of two bias‐corrected covariance estimators for generalized estimating equations publication-title: Biometrics – volume: 65 start-page: 102 year: 2005 end-page: 106 article-title: A total score for the CERAD neuropsychological battery publication-title: Neurology – volume: 88 start-page: 901 year: 2001 end-page: 906 article-title: On the robust variance estimator in generalised estimating equations publication-title: Biometrika – volume: 86 start-page: 1891 year: 2016 end-page: 1900 article-title: A covariance correction that accounts for correlation estimation to improve finite‐sample inference with generalized estimating equations: a study on its applicability with structured correlation matrices publication-title: Journal of Statistical Computation and Simulation – volume: 32 start-page: 2850 year: 2013 end-page: 2858 article-title: That use an unstructured correlation matrix publication-title: Statistics in Medicine – volume: 17 start-page: 645 year: 1995 end-page: 653 article-title: Estimating premorbid intelligence in African‐American and white elderly veterans using the American version of the national adult reading test publication-title: Journal of Clinical and Experimental Neuropsychology – volume: 33 start-page: 2222 year: 2014 end-page: 2237 article-title: Improving the correlation structure selection approach for generalized estimating equations and balanced longitudinal data publication-title: Statistics in Medicine – volume: 30 start-page: 1278 year: 2011 end-page: 1291 article-title: Modified robust variance estimator for generalized estimating equations with improved small‐sample performance publication-title: Statistics in Medicine – volume: 51 start-page: 309 year: 1995 end-page: 317 article-title: A caveat concerning independence estimating equations with multivariate binary data publication-title: Biometrics – volume: 90 start-page: 29 year: 2003 end-page: 41 article-title: Working correlation structure misspecification, estimation and covariate design: implications for generalised estimating equations performance publication-title: Biometrika – volume: 57 start-page: 126 year: 2001 end-page: 134 article-title: A covariance estimator for GEE with improved small‐sample properties publication-title: Biometrics – volume: 57 start-page: 1198 year: 2001 end-page: 1206 article-title: Small‐sample adjustments for wald‐type tests using sandwich estimators publication-title: Biometrics – volume: 25 start-page: 4081 year: 2006 end-page: 4098 article-title: Improved hypothesis testing for coefficients in generalized estimating equations with small samples of clusters publication-title: Statistics in Medicine – volume: 73 start-page: 13 year: 1986 end-page: 22 article-title: Longitudinal data analysis using generalized linear models publication-title: Biometrika – volume: 96 start-page: 1387 year: 2001 end-page: 1396 article-title: A note on the efficiency of sandwich covariance matrix estimation publication-title: Journal of the American Statistical Association – volume: 21 start-page: 1429 year: 2002 end-page: 1441 article-title: Small‐sample adjustments in using the sandwich variance estimator in generalized estimating equations publication-title: Statistics in Medicine – volume: 23 start-page: 1172 year: 2013 end-page: 1187 article-title: A comparison of bias‐corrected covariance estimators for generalized estimating equations publication-title: Journal of Biopharmaceutical Statistics – ident: e_1_2_8_20_1 doi: 10.1093/biomet/86.2.459 – ident: e_1_2_8_12_1 doi: 10.1080/00949655.2015.1089873 – ident: e_1_2_8_2_1 doi: 10.1093/biomet/73.1.13 – ident: e_1_2_8_27_1 doi: 10.1212/01.wnl.0000167607.63000.38 – ident: e_1_2_8_3_1 doi: 10.1093/biomet/88.3.901 – ident: e_1_2_8_4_1 doi: 10.1198/016214501753382309 – ident: e_1_2_8_29_1 doi: 10.1080/01688639508405155 – ident: e_1_2_8_25_1 doi: 10.2307/2531733 – ident: e_1_2_8_16_1 doi: 10.1093/biomet/90.1.29 – ident: e_1_2_8_6_1 doi: 10.1111/j.0006-341X.2001.01198.x – ident: e_1_2_8_21_1 doi: 10.1111/j.1541-0420.2007.00764.x – ident: e_1_2_8_13_1 doi: 10.1002/sim.1142 – ident: e_1_2_8_8_1 doi: 10.1002/sim.2502 – ident: e_1_2_8_24_1 doi: 10.1002/sim.3622 – ident: e_1_2_8_15_1 doi: 10.2307/2532890 – ident: e_1_2_8_18_1 doi: 10.1002/sim.3489 – ident: e_1_2_8_28_1 doi: 10.1017/S1041610213000367 – ident: e_1_2_8_9_1 doi: 10.1002/sim.4150 – ident: e_1_2_8_14_1 doi: 10.2307/2533336 – ident: e_1_2_8_23_1 doi: 10.1198/000313007X245122 – ident: e_1_2_8_11_1 doi: 10.1002/sim.5709 – ident: e_1_2_8_10_1 doi: 10.1080/10543406.2013.813521 – ident: e_1_2_8_5_1 doi: 10.1111/j.0006-341X.2001.00126.x – ident: e_1_2_8_26_1 doi: 10.1080/13854046.2012.690451 – ident: e_1_2_8_19_1 doi: 10.1093/biomet/82.2.407 – ident: e_1_2_8_17_1 doi: 10.1002/sim.6106 – ident: e_1_2_8_22_1 doi: 10.1002/bimj.201200237 – ident: e_1_2_8_7_1 doi: 10.1002/bimj.200390021 |
| SSID | ssj0011527 |
| Score | 2.2838483 |
| Snippet | Generalized estimating equations (GEE) are often used for the marginal analysis of longitudinal data. Although much work has been performed to improve the... Generalized estimating equations (GEE) are often used for the marginal analysis of longitudinal data. Although much work has been done to improve the validity... |
| SourceID | pubmedcentral proquest pubmed crossref wiley istex |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 3733 |
| SubjectTerms | Biometry Computer Simulation correlation selection Data analysis degrees of freedom efficiency empirical covariance matrix Estimating techniques Humans Longitudinal Studies Models, Statistical Sample size Simulation Statistical inference |
| Title | Improving power in small-sample longitudinal studies when using generalized estimating equations |
| URI | https://api.istex.fr/ark:/67375/WNG-CJ6CQGMD-Q/fulltext.pdf https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fsim.6967 https://www.ncbi.nlm.nih.gov/pubmed/27090375 https://www.proquest.com/docview/1811570300 https://www.proquest.com/docview/1807878258 https://pubmed.ncbi.nlm.nih.gov/PMC4965318 |
| Volume | 35 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnZ3fb9MwEMdPaEhoEhpQfgUGMhKCp3RJHDvpIypsY1InDZiYxINlx-5WrUu3pRVoT_wJ-xv3l3AXJ4HCkBBPlZJLU7t3zjf2-XMAL3kqcQi049DZqJ6tcqEZ2Ch0XKI8HgueadqcPNqV2_vpzoE4aLIqaS-M50N0E24UGfV4TQGuTbXxExpaTU76ciBpI3mMX0x66ENHjorbaq20QimzWLTc2SjZaC9cehLdpE79dp3M_DNb8lcVWz-GNu_Al7YBPvvkuL-Ym35x8Rvb8f9aeBfWGnXK3nh3ugc3XNmDW6Nm_b0Ht_0sH_Obl3qwSlrVo57vg-kmKNgp1V5jk5JVJ3o6vfp-WWmiELPpjMojLSyV4mKVT2FkX49cySj__pAdegj25MJZRvgPktN42J15IHn1APY3330abodNCYewQOmUhbYm1GOUE6V-kGc6ScYCA19LfC1P8wLlg7VpnEsjYzcunEkLjaPvwEnHDXecP4SVcla6x8BMUvDcGi1tqlPheI4jM8pLqUUi8ljaAF63f6cqGr45ldmYKk9mThT2p6L-DOBFZ3nqmR7X2LyqPaIz0OfHlAOXCfV5d0sNd-Rwb2v0Vu0FsN66jGrCv1Iom2JCm0UR3qs7jYFLqzG6dLMF2aA6Q30m8gAeeQ_rbpZkNH2WiQCyJd_rDAgKvnymnBzVcHDi_-M4jb-_dq2_NlB9fD-izyf_avgUVlEsSsqVSaJ1WJmfL9wzFGRz87wOvR86kjUT |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3bbtQwEB2VVoJKiMtyCxQwEoKnbHOzkxVPaEu7Lc1KhVb0AclyYm-76jZbml2B-sQn8I18CTNxElgoEuIpUjKRY3tmcjwenwF4HkYCXaAeuUZ7VbTKuFlPe64JBcLjEQ9jRYeT06EYHEQ7h_xwCV41Z2EsP0QbcCPLqPw1GTgFpNd_soaW49Ou6In4CqxEiDNo5bXxruWO8pt6rbRHKWKfN8yzXrDevLnwL1qhYf1yGdD8M1_yVxxb_Yg2b8LHpgs2_-SkO59l3fziN3bH_-zjLbhRA1T22mrUbVgyRQeupvUWfAeu20Afs-eXOrBKcNWyPd-BrI1RsDMqv8bGBStP1WTy_eu3UhERMZtMqULSXFM1LlbaLEb2-dgUjFLwj9iR5cEeXxjNiAGEEDXeNp8sJ3l5Fw423-z3B25dxcHNET3Frq5I6tHQiai-l8QqCEYcbV8JXJlHSY4IQuvIT0QmfDPKTRblCh1wzwgTZqEJw3uwXEwL8wBYFuRhojMldKQibsIEnTPOvFA84IkvtAMvm_mUeU1xTpU2JtKSMwcSx1PSeDrwrJU8s7Qel8i8qFSiFVDnJ5QGF3P5Ybgl-zuiv7eVbsg9B9YanZG1ByglIief2M08D9tqH6Pt0oaMKsx0TjII0BCi8cSB-1bF2saCmCJoMXcgXlC-VoB4wRefFOPjih-cSgCgq8bvr3Trrx2U77dTuj78V8GncG2wn-7K3e3h20ewithRUOpM4K3B8ux8bh4jPptlTyo7_AFmeTky |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnZ3bbtNAEIZH0EpVJcQhnAwFFgnBlVOfdu1copT0AIkoUFGpF6u1d91GTZ1QJwL1ikfgGXkSZry2IVAkxFWkeCJnNzPrP7Oz3wA8CyOBS6DOXaO9Kltl3LSnPdeEAuVxzsNY0eHk4UjsHER7h_ywrqqkszCWD9Em3CgyqvWaAnym882f0NByfNYVPRFfhdVIoJAgQfSuRUf5TbtW2qIUsc8b8KwXbDafXHoUrdKsfrlMZ_5ZLvmrjK2eQ4MbcNSMwJafnHYX87SbXfwGd_y_Id6E67U8ZS-tP92CK6bowNqw3oDvwDWb5mP29FIH1kmsWtbzbUjbDAWbUfM1Ni5YeaYmk-9fv5WKMMRsMqX-SAtNvbhYaWsY2ecTUzAqwD9mx5aCPb4wmhH_g_Q0vm0-WSJ5eQcOBq8-9HfcuoeDm6F2il1dIeoxzAlT30tiFQQ5x8hXAv-XR0mG-kHryE9EKnyTZyaNMoXLb88IE6ahCcO7sFJMC3MfWBpkYaJTJXSkIm7CBJdm1JdC8YAnvtAOvGh-TpnVgHPqszGRFs0cSJxPSfPpwNPWcmahHpfYPK88ojVQ56dUBBdz-XG0Lft7or-_PdyS-w5sNC4j6_gvJeomn9hmnof3ai9j5NJ2jCrMdEE2KM9QoPHEgXvWw9qbBTHlz2LuQLzke60BUcGXrxTjk4oOTg0AcKHG71-51l8HKN_vDun1wb8aPoG1t1sD-WZ39PohrKNwFFQ3E3gbsDI_X5hHKM7m6eMqCn8AnUY34Q |
| 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=Improving+Power+in+Small-Sample+Longitudinal+Studies+when+using+Generalized+Estimating+Equations&rft.jtitle=Statistics+in+medicine&rft.au=Westgate%2C+Philip+M.&rft.au=Burchett%2C+Woodrow+W.&rft.date=2016-09-20&rft.issn=0277-6715&rft.eissn=1097-0258&rft.volume=35&rft.issue=21&rft.spage=3733&rft.epage=3744&rft_id=info:doi/10.1002%2Fsim.6967&rft_id=info%3Apmid%2F27090375&rft.externalDocID=PMC4965318 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0277-6715&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0277-6715&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0277-6715&client=summon |