The semiparametric accelerated trend-renewal process for recurrent event data.

Tytuł:: The semiparametric accelerated trend-renewal process for recurrent event data.
Autorzy:: Su CL; Department of Mathematics and Statistics, McGill University, Montréal, QC, Canada. .; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada. .
Steele RJ; Department of Mathematics and Statistics, McGill University, Montréal, QC, Canada.
Shrier I; Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada.
Źródło:: Lifetime data analysis [Lifetime Data Anal] 2021 Jul; Vol. 27 (3), pp. 357-387. Date of Electronic Publication: 2021 Mar 25.
Typ publikacji:: Journal Article; Research Support, Non-U.S. Gov't
Język:: English
Imprint Name(s):: Publication: <2007->: Boston : Springer
Original Publication: Boston : Kluwer Academic Publishers, 1995-
MeSH Terms:: Longitudinal Studies*
Computer Simulation ; Humans
References:: Andersen PK, Gill RD (1982) Cox’s regression model for counting processes: a large sample study. Ann Stat 10(4):1100–1120.
Bain L (2017) Statistical analysis of reliability and life-testing models: theory and methods, 2nd edn. Routledge, New York.
Baltazar-Aban I, Peña EA (1995) Properties of hazard-based residuals and implications in model diagnostics. J Am Stat Assoc 90(429):185–197.
Buckley J, James I (1979) Linear regression with censored data. Biometrika 66(3):429–436.
Cai T, Wei LJ, Wilcox M (2000) Semiparametric regression analysis for clustered failure time data. Biometrika 87(4):867–878.
Chang SH (2004) Estimating marginal effects in accelerated failure time models for serial sojourn times among repeated events. Lifetime Data Anal 10(2):175–190.
Chiou SH, Kang S, Kim J, Yan J (2014) Marginal semiparametric multivariate accelerated failure time model with generalized estimating equations. Lifetime Data Anal 20(4):599–618.
Clement DY, Strawderman RL (2009) Conditional GEE for recurrent event gap times. Biostatistics 10(3):451–467.
Cook RJ, Lawless J (2007) The statistical analysis of recurrent events. Springer, New York.
Cook RJ, Lawless JF, Lakhal-Chaieb L, Lee KA (2009) Robust estimation of mean functions and treatment effects for recurrent events under event-dependent censoring and termination: application to skeletal complications in cancer metastatic to bone. J Am Stat Assoc 104(485):60–75.
Darlington G, Dixon S (2013) Event-weighted proportional hazards modelling for recurrent gap time data. Stat Med 32(1):124–130.
Du P (2009) Nonparametric modeling of the gap time in recurrent event data. Lifetime Data Anal 15(2):256–277.
Gail M, Santner T, Brown C (1980) An analysis of comparative carcinogenesis experiments based on multiple times to tumor. Biometrics 36(2):255–266.
Gámiz ML, Lindqvist BH (2016) Nonparametric estimation in trend-renewal processes. Reliab Eng Syst Saf 145:38–46.
Gaudoin O, Yang B, Xie M (2003) A simple goodness-of-fit test for the power-law process, based on the Duane plot. IEEE Trans Reliab 52(1):69–74.
González JR, Fernandez E, Moreno V, Ribes J, Peris M, Navarro M, Cambray M, Borràs JM (2005) Sex differences in hospital readmission among colorectal cancer patients. J Epidemiol Commun Health 59(6):506–511.
Hamilton GM, Meeuwisse WH, Emery CA, Shrier I (2011) Subsequent injury definition, classification, and consequence. Clin J Sport Med 21(6):508–514.
Heggland K, Lindqvist BH (2007) A non-parametric monotone maximum likelihood estimator of time trend for repairable system data. Reliab Eng Syst Saf 92(5):575–584.
Huang Y, Chen YQ (2003) Marginal regression of gaps between recurrent events. Lifetime Data Anal 9(3):293–303.
Jokiel-Rokita A, Magiera R (2012) Estimation of parameters for trend-renewal processes. Stat Comput 22(2):625–637.
Kang F, Sun L, Zhao X (2015) A class of transformed hazards models for recurrent gap times. Comput Stat Data Anal 83:151–167.
Kvaløy JT, Lindqvist BH (1998) TTT-based tests for trend in repairable systems data. Reliab Eng Syst Saf 60(1):13–28.
Lawless JF, Nadeau C (1995) Some simple robust methods for the analysis of recurrent events. Technometrics 37(2):158–168.
Lawless JF, Çiğşar C, Cook RJ (2012) Testing for monotone trend in recurrent event processes. Technometrics 54(2):147–158.
Lawless JF (1987) Regression methods for Poisson process data. J Am Stat Assoc 82(399):808–815.
Lin D, Wei L, Yang I, Ying Z (2000) Semiparametric regression for the mean and rate functions of recurrent events. J R Stat Soc Ser B (Stat Methodol) 62(4):711–730.
Lindqvist B, Elvebakk G, Heggland K (2003) The trend-renewal process for statistical analysis of repairable systems. Technometrics 45(1):31–44.
Lindqvist BH (2006) On the statistical modeling and analysis of repairable systems. Stat Sci 21:532–551.
Louzada F, Macera MA, Cancho VG (2017) A gap time model based on a multiplicative marginal rate function that accounts for zero-recurrence units. Stat Methods Med Res 26(5):2000–2010.
Luo X, Huang CY (2011) Analysis of recurrent gap time data using the weighted risk-set method and the modified within-cluster resampling method. Stat Med 30(4):301–311.
Miller RG (1976) Least squares regression with censored data. Biometrika 63(3):449–464.
Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7(4):308–313.
Nelson W (1988) Graphical analysis of system repair data. J Qual Technol 20(1):24–35.
Pan W, Connett JE (2001) A multiple imputation approach to linear regression with clustered censored data. Lifetime Data Anal 7(2):111–123.
Peña EA, Strawderman RL, Hollander M (2001) Nonparametric estimation with recurrent event data. J Am Stat Assoc 96(456):1299–1315.
Pietzner D, Wienke A (2013) The trend-renewal process: a useful model for medical recurrence data. Stat Med 32(1):142–152.
Prentice RL, Williams BJ, Peterson AV (1981) On the regression analysis of multivariate failure time data. Biometrika 68(2):373–379.
Rondeau V, Mazroui Y, Gonzalez JR (2012) frailtypack: an r package for the analysis of correlated survival data with frailty models using penalized likelihood estimation or parametrical estimation. J Stat Softw 47(4):1–28.
Schaubel DE, Cai J (2004a) Non-parametric estimation of gap time survival functions for ordered multivariate failure time data. Stat Med 23(12):1885–1900.
Schaubel DE, Cai J (2004b) Regression methods for gap time hazard functions of sequentially ordered multivariate failure time data. Biometrika 91(2):291–303.
Strawderman RL (2005) The accelerated gap times model. Biometrika 92(3):647–666.
Strawderman RL (2006) A regression model for dependent gap times. Int J Biostat 2(1):1–32.
Su CL, Lin FC (2020) Analysis of cyclic recurrent event data with multiple event types. Jpn J Stat Data Sci 1–21.
Su CL, Steele R, Shrier I (2020a) Doubly robust estimation and causal inference for recurrent event data. Stat Med 39(17):2324–2338.
Su CL, Platt RW, Plante JF (2020b) Causal inference for recurrent event data using pseudo-observations. Biostatistics.
Sun L, Park DH, Sun J (2006) The additive hazards model for recurrent gap times. Statistica Sinica 16(3):919–932.
Team RC, Worldwide C (2002) The r stats package. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org.
Varadhan R, Gilbert P (2009) BB: an R package for solving a large system of nonlinear equations and for optimizing a high-dimensional nonlinear objective function. J Stat Softw 32(4):1–26.
Wang MC, Chang SH (1999) Nonparametric estimation of a recurrent survival function. J Am Stat Assoc 94(445):146–153.
Ye Y, Kalbfleisch JD, Schaubel DE (2007) Semiparametric analysis of correlated recurrent and terminal events. Biometrics 63(1):78–87.
Yin G, Cai J (2005) Quantile regression models with multivariate failure time data. Biometrics 61(1):151–161.
Ying Z, Wei LJ (1994) The Kaplan–Meier estimate for dependent failure time observations. J Multivar Anal 50(1):17–29.
Zeng D, Lin D (2006) Efficient estimation of semiparametric transformation models for counting processes. Biometrika 93(3):627–640.
Zeng D, Lin D (2008) Efficient resampling methods for nonsmooth estimating functions. Biostatistics 9(2):355–363.
Zhao X, Zhou X (2012) Modeling gap times between recurrent events by marginal rate function. Comput Stat Data Anal 56(2):370–383.
Grant Information:: CHRPJ/478521-2015 Canada CIHR
Contributed Indexing:: Keywords: Accelerated transformed gap time model; Buckley–James imputation; Model diagnostic plots; Prediction; Recurrent events; Trend-renewal process
Entry Date(s):: Date Created: 20210326 Date Completed: 20211028 Latest Revision: 20211028
Update Code:: 20240105
DOI:: 10.1007/s10985-021-09519-3
PMID:: 33768490
: Czasopismo naukowe

Pełny tekst

Recurrent event data arise in many biomedical longitudinal studies when health-related events can occur repeatedly for each subject during the follow-up time. In this article, we examine the gap times between recurrent events. We propose a new semiparametric accelerated gap time model based on the trend-renewal process which contains trend and renewal components that allow for the intensity function to vary between successive events. We use the Buckley-James imputation approach to deal with censored transformed gap times. The proposed estimators are shown to be consistent and asymptotically normal. Model diagnostic plots of residuals and a method for predicting number of recurrent events given specified covariates and follow-up time are also presented. Simulation studies are conducted to assess finite sample performance of the proposed method. The proposed technique is demonstrated through an application to two real data sets.

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