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Regression analysis of recurrent‐event‐free time from multiple follow‐up windows
dc.contributor.authorXia, Meng
dc.contributor.authorMurray, Susan
dc.contributor.authorTayob, Nabihah
dc.date.accessioned2020-01-13T15:19:05Z
dc.date.availableWITHHELD_13_MONTHS
dc.date.available2020-01-13T15:19:05Z
dc.date.issued2020-01-15
dc.identifier.citationXia, Meng; Murray, Susan; Tayob, Nabihah (2020). "Regression analysis of recurrent‐event‐free time from multiple follow‐up windows." Statistics in Medicine 39(1): 1-15.
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.urihttps://hdl.handle.net/2027.42/153162
dc.publisherNew York University
dc.publisherWiley Periodicals, Inc.
dc.subject.otherpseudo‐observations
dc.subject.otherrecurrent events
dc.subject.othermultiple imputations
dc.subject.othermultivariable regression
dc.subject.othergeneralized estimating equation
dc.titleRegression analysis of recurrent‐event‐free time from multiple follow‐up windows
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelStatistics and Numeric Data
dc.subject.hlbsecondlevelPublic Health
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelSocial Sciences
dc.subject.hlbtoplevelHealth Sciences
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/153162/1/sim8385_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/153162/2/sim8385.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/153162/3/SIM_8385-supp-0001-Online_Supplementary_materials.pdf
dc.identifier.doi10.1002/sim.8385
dc.identifier.sourceStatistics in Medicine
dc.identifier.citedreferenceTukey J. Bias and confidence in not quite large samples. Ann Math Stat. 1958; 29: 614.
dc.identifier.citedreferenceRogers JK, Yaroshinsky A, Pocock SJ, Stokar D, Pogoda J. Analysis of recurrent events with an associated informative dropout time: application of the joint frailty model. Statist Med. 2016; 35 ( 13 ): 2195 ‐ 2205.
dc.identifier.citedreferenceDing J, Sun L. Additive mixed effect model for recurrent gap time data. Lifetime Data Anal. 2017; 23 ( 2 ): 223 ‐ 253.
dc.identifier.citedreferenceCook RJ, Lawless J. The Statistical Analysis of Recurrent Events. New York, NY: Springer Science & Business Media; 2007.
dc.identifier.citedreferenceXia M, Murray S. Commentary on Tayob and Murray (2014) with a useful update pertaining to study design. Biostatistics. 2018; 20 ( 3 ): 542 ‐ 545.
dc.identifier.citedreferenceLin D, Sun W, Ying Z. Nonparametric estimation of the gap time distribution for serial events with censored data. Biometrika. 1999; 86 ( 1 ): 59 ‐ 70.
dc.identifier.citedreferenceZeger SL, Liang K‐Y. Longitudinal data analysis for discrete and continuous outcomes. Biometrics. 1986: 121 ‐ 130.
dc.identifier.citedreferenceWestgate PM, Braun TM. The effect of cluster size imbalance and covariates on the estimation performance of quadratic inference functions. Statist Med. 2012; 31 ( 20 ): 2209 ‐ 2222.
dc.identifier.citedreferenceAndersen PK, Hansen MG, Klein JP. Regression analysis of restricted mean survival time based on pseudo‐observations. Lifetime Data Anal. 2004; 10 ( 4 ): 335 ‐ 350.
dc.identifier.citedreferenceKlein JP, Andersen PK. Regression modeling of competing risks data based on pseudovalues of the cumulative incidence function. Biometrics. 2005; 61 ( 1 ): 223 ‐ 229.
dc.identifier.citedreferenceAndersen PK, Klein JP. Regression analysis for multistate models based on a pseudo‐value approach, with applications to bone marrow transplantation studies. Scand J Stat. 2007; 34 ( 1 ): 3 ‐ 16.
dc.identifier.citedreferenceAndrei A‐C, Murray S. Regression models for the mean of the quality‐of‐life‐adjusted restricted survival time using pseudo‐observations. Biometrics. 2007; 63 ( 2 ): 398 ‐ 404.
dc.identifier.citedreferenceGraw F, Gerds TA, Schumacher M. On pseudo‐values for regression analysis in competing risks models. Lifetime Data Anal. 2009; 15 ( 2 ): 241 ‐ 255.
dc.identifier.citedreferenceXiang F, Murray S. Restricted mean models for transplant benefit and urgency. Statist Med. 2012; 31 ( 6 ): 561 ‐ 576.
dc.identifier.citedreferenceTayob N, Murray S. Statistical consequences of a successful lung allocation system – recovering information and reducing bias in models for urgency. Statist Med. 2017; 36: 2435 ‐ 2451.
dc.identifier.citedreferenceQuenouille MH. Approximate tests of correlation in time‐series 3. Math Proc Camb Philos Soc. 1949; 45: 483 ‐ 484.
dc.identifier.citedreferenceQuenouille MH. Notes on bias in estimation. Biometrika. 1956; 43 ( 3/4 ): 353 ‐ 360.
dc.identifier.citedreferenceFaucett CL, Schenker N, Taylor JMG. Survival analysis using auxiliary variables via multiple imputation, with application to AIDS clinical trial data. Biometrics. 2002; 58 ( 1 ): 37 ‐ 47.
dc.identifier.citedreferenceTaylor JMG, Murray S, Hsu C‐H. Survival estimation and testing via multiple imputation. Stat Probab Lett. 2002; 58 ( 3 ): 221 ‐ 232.
dc.identifier.citedreferenceHsu C‐H, Taylor JMG, Murray S, Commenges D. Survival analysis using auxiliary variables via non‐parametric multiple imputation. Statist Med. 2006; 25 ( 20 ): 3503 ‐ 3517.
dc.identifier.citedreferenceLiu LX, Murray S, Tsodikov A. Multiple imputation based on restricted mean model for censored data. Statist Med. 2011; 30 ( 12 ): 1339 ‐ 1350.
dc.identifier.citedreferenceXiang F, Murray S, Liu X. Analysis of transplant urgency and benefit via multiple imputation. Statist Med. 2014; 33 ( 26 ): 4655 ‐ 4670.
dc.identifier.citedreferenceLittle RJ, Rubin DB. Statistical Analysis With Missing Data. New York, NY: John Wiley & Sons, Inc; 1986.
dc.identifier.citedreferenceLi DX. On default correlation: a copula function approach. 1999. https://ssrn.com/abstract=187289
dc.identifier.citedreferenceTayob N, Murray S. Nonparametric tests of treatment effect based on combined endpoints for mortality and recurrent events. Biostatistics. 2014; 16 ( 1 ): 73 ‐ 83.
dc.identifier.citedreferenceXia M. Regression‐Recurrent‐Event‐Free‐Time. GitHub. 2019.
dc.identifier.citedreferenceAlbert RK, Connett J, Bailey WC, et al. Azithromycin for prevention of exacerbations of COPD. N Engl J Med. 2011; 365 ( 8 ): 689 ‐ 698.
dc.identifier.citedreferenceSchwartz GG, Steg PG, Szarek M, et al. Alirocumab and cardiovascular outcomes after acute coronary syndrome. N Engl J Med. 2018; 379 ( 22 ): 2097 ‐ 2107. https://doi.org/10.1056/NEJMoa1801174
dc.identifier.citedreferenceWilcox MH, Gerding DN, Poxton IR, et al. Bezlotoxumab for prevention of recurrent clostridium difficile infection. N Engl J Med. 2017; 376 ( 4 ): 305 ‐ 317. https://doi.org/10.1056/NEJMoa1602615
dc.identifier.citedreferenceDeKosky ST, Ikonomovic MD, Gandy S. Traumatic brain injury football, warfare, and long‐term effects. N Engl J Med. 2010; 363 ( 14 ): 1293 ‐ 1296.
dc.identifier.citedreferenceFrome EL, Kutner MH, Beauchamp JJ. Regression analysis of poisson‐distributed data. J Am Stat Assoc. 1973; 68 ( 344 ): 935 ‐ 940.
dc.identifier.citedreferenceLawless J. Negative binomial and mixed Poisson regression. Can J Stat. 1987; 15 ( 3 ): 209 ‐ 225.
dc.identifier.citedreferenceLambert D. Zero‐inflated Poisson regression, with an application to defects in manufacturing. Technometrics. 1992; 34 ( 1 ): 1 ‐ 14.
dc.identifier.citedreferenceGreene WH. Accounting for excess zeros and sample selection in Poisson and negative binomial regression models. Working paper. New York, NY: New York University; 1994.
dc.identifier.citedreferenceOzga A‐K, Kieser M, Rauch G. A systematic comparison of recurrent event models for application to composite endpoints. BMC Med Res Methodol. 2018; 18 ( 1 ): 2.
dc.identifier.citedreferenceAndersen PK, Gill RD. Cox’s regression model for counting processes: a large sample study. Ann Stat. 1982: 1100 ‐ 1120.
dc.identifier.citedreferencePrentice RL, Williams BJ, Peterson AV. On the regression analysis of multivariate failure time data. Biometrika. 1981; 68 ( 2 ): 373 ‐ 379.
dc.identifier.citedreferenceWei L‐J, Lin DY, Weissfeld L. Regression analysis of multivariate incomplete failure time data by modeling marginal distributions. J Am Stat Assoc. 1989; 84 ( 408 ): 1065 ‐ 1073.
dc.identifier.citedreferencePepe MS, Cai J. Some graphical displays and marginal regression analyses for recurrent failure times and time dependent covariates. J Am Stat Assoc. 1993; 88 ( 423 ): 811 ‐ 820.
dc.identifier.citedreferenceLawless JF, Nadeau C. Some simple robust methods for the analysis of recurrent events. Technometrics. 1995; 37 ( 2 ): 158 ‐ 168.
dc.identifier.citedreferenceLin DY, Wei LJ, Yang I, Ying Z. Semiparametric regression for the mean and rate functions of recurrent events. J Royal Stat Soc B. 2000; 62 ( 4 ): 711 ‐ 730.
dc.identifier.citedreferenceAalen OO, Husebye E. Statistical analysis of repeated events forming renewal processes. Statist Med. 1991; 10 ( 8 ): 1227 ‐ 1240.
dc.identifier.citedreferenceHougaard P. Frailty models for survival data. Lifetime Data Anal. 1995; 1 ( 3 ): 255 ‐ 273.
dc.identifier.citedreferenceLiu L, Wolfe RA, Huang X. Shared frailty models for recurrent events and a terminal event. Biometrics. 2004; 60 ( 3 ): 747 ‐ 756.
dc.identifier.citedreferenceChang S‐H. Estimating marginal effects in accelerated failure time models for serial sojourn times among repeated events. Lifetime Data Anal. 2004; 10 ( 2 ): 175 ‐ 190.
dc.identifier.citedreferenceRondeau V, Mathoulin‐Pelissier S, Jacqmin‐Gadda H, Brouste V, Soubeyran P. Joint frailty models for recurring events and death using maximum penalized likelihood estimation: application on cancer events. Biostatistics. 2007; 8 ( 4 ): 708 ‐ 721.
dc.identifier.citedreferenceMazroui Y, Mathoulin‐Pélissier S, MacGrogan G, Brouste V, Rondeau V. Multivariate frailty models for two types of recurrent events with a dependent terminal event: application to breast cancer data. Biometrical Journal. 2013; 55 ( 6 ): 866 ‐ 884.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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