A study of grouped failure time data and the misapplication of recurrent event modeling

ABSTRACT Clinical trials often assess whether or not subjects have a disease at predetermined follow-up times. When the response of interest is a recurrent event, a subject may respond at multiple follow-up times over the course of the study. Alternatively, when the response of interest is an irreversible event, a subject is typically only observed until the time at which the response is first detected. However, some recent studies have recorded subjects responses at follow-up times after an irreversible event is initially observed. This study compares how existing models perform when failure time data are treated as recurrent events.

[1]  Gordon Johnston,et al.  Statistical Models and Methods for Lifetime Data , 2003, Technometrics.

[2]  Georg Schett,et al.  Bone erosion in rheumatoid arthritis: mechanisms, diagnosis and treatment , 2012, Nature Reviews Rheumatology.

[3]  Patrick Royston,et al.  The design of simulation studies in medical statistics , 2006, Statistics in medicine.

[4]  S. R. Searle,et al.  Generalized, Linear, and Mixed Models: McCulloch/Generalized, Linear, and Mixed Models , 2005 .

[5]  S. Zeger,et al.  Longitudinal data analysis using generalized linear models , 1986 .

[6]  Jianwen Cai,et al.  Modelling recurrent events: a tutorial for analysis in epidemiology. , 2015, International journal of epidemiology.

[7]  P. McCullagh,et al.  Generalized Linear Models, 2nd Edn. , 1990 .

[8]  J Whitehead,et al.  The analysis of relapse clinical trials, with application to a comparison of two ulcer treatments. , 1989, Statistics in medicine.

[9]  H. Brenner,et al.  Analytic strategies for recurrent events in epidemiologic studies: background and application to hospitalization risk in the elderly. , 2000, Journal of clinical epidemiology.

[10]  Ralf Bender,et al.  Generating survival times to simulate Cox proportional hazards models , 2005, Statistics in medicine.

[11]  David R. Cox,et al.  Regression models and life tables (with discussion , 1972 .

[12]  A. Barton,et al.  The role of rheumatoid arthritis genetic susceptibility markers in the prediction of erosive disease in patients with early inflammatory polyarthritis: results from the Norfolk Arthritis Register , 2010, Rheumatology.

[13]  Jerald F. Lawless,et al.  Statistical Models and Methods for Lifetime Data: Lawless/Statistical , 2002 .

[14]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[15]  J. Ménard,et al.  Serum IgA rheumatoid factor and pyridinoline in very early arthritis as predictors of erosion(s) at two years: A simple model of prediction from a conservatively treated community‐based inception cohort , 2010, Arthritis care & research.

[16]  J. Twisk,et al.  Applied analysis of recurrent events: a practical overview , 2005, Journal of Epidemiology and Community Health.

[17]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[18]  K Y Liang,et al.  Longitudinal data analysis for discrete and continuous outcomes. , 1986, Biometrics.

[19]  Eric R. Ziegel,et al.  Generalized Linear Models , 2002, Technometrics.

[20]  D. Cox Regression Models and Life-Tables , 1972 .

[21]  A. Cambon-Thomsen,et al.  Association of IL-2RA and IL-2RB genes with erosive status in early rheumatoid arthritis patients (ESPOIR and RMP cohorts). , 2014, Joint, bone, spine : revue du rhumatisme.