Longitudinal studies 3: Data modeling using standard regression models and extensions.

In longitudinal studies the relationship between exposure and disease can be measured once or multiple times while participants are monitored over time. Traditional regression techniques are used to model outcome data when each epidemiological unit is observed once. These models include generalized linear models for quantitative continuous, discrete, or qualitative outcome responses, and models for time-to-event data. When data come from the same subjects or group of subjects, observations are not independent and the underlying correlation needs to be addressed in the analysis. In these circumstances extended models are necessary to handle complexities related to clustered data, and repeated measurements of time-varying predictors and/or outcomes.

[1]  M Lunn,et al.  Applying Cox regression to competing risks. , 1995, Biometrics.

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

[3]  G. Heine,et al.  Do ultrasound renal resistance indices reflect systemic rather than renal vascular damage in chronic kidney disease? , 2006, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[4]  L. J. Wei,et al.  Regression analysis of multivariate incomplete failure time data by modeling marginal distributions , 1989 .

[5]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[6]  C. Zoccali,et al.  Urotensin II is an inverse predictor of death and fatal cardiovascular events in chronic kidney disease. , 2008, Kidney international.

[7]  Charles E. McCulloch,et al.  CHRONIC KIDNEY DISEASE AND THE RISKS OF DEATH, CARDIOVASCULAR EVENTS, AND HOSPITALIZATION , 2004 .

[8]  P. Hougaard,et al.  Frailty models for survival data , 1995, Lifetime data analysis.

[9]  R. Bersin,et al.  Prevention of contrast-induced nephropathy with sodium bicarbonate: a randomized controlled trial. , 2004, JAMA.

[10]  J. Box-Steffensmeier,et al.  Repeated events survival models: the conditional frailty model , 2006, Statistics in medicine.

[11]  R Crouchley,et al.  A comparison of frailty models for multivariate survival data. , 1995, Statistics in medicine.

[12]  Analysis of recurrent failure times data: should the baseline hazard be stratified , 2001 .

[13]  P. Parfrey,et al.  Clinical research of kidney diseases IV: Standard regression models. , 2007, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[14]  R. Wolfe,et al.  A Frailty Model for Informative Censoring , 2002, Biometrics.

[15]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

[16]  H. White Maximum Likelihood Estimation of Misspecified Models , 1982 .

[17]  A. V. Peterson,et al.  On the regression analysis of multivariate failure time data , 1981 .

[18]  F. D. de Charro,et al.  Economic impact of extended time on peritoneal dialysis as a result of using polyglucose: the application of a Markov chain model to forecast changes in the development of the ESRD programme over time. , 2003, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[19]  Johanna T Dwyer,et al.  Effect of dialysis dose and membrane flux in maintenance hemodialysis. , 2002, The New England journal of medicine.

[20]  David G. Kleinbaum,et al.  Survival Analysis: A Self-Learning Text , 1997 .

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

[22]  R. Huisman,et al.  The effects of a low-to-moderate intensity pre-conditioning exercise programme linked with exercise counselling for sedentary haemodialysis patients in The Netherlands: results of a randomized clinical trial. , 2005, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[23]  P. Parfrey,et al.  Clinical research of kidney diseases V: extended analytic models. , 2008, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[24]  Lee-Jen Wei,et al.  Cox-Type Regression Analysis for Large Numbers of Small Groups of Correlated Failure Time Observations , 1992 .

[25]  P. Aljama,et al.  Marked reduction in the prevalence of hepatitis C virus infection in hemodialysis patients: causes and consequences. , 2004, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[26]  Carmine Zoccali,et al.  Asymmetrical dimethylarginine predicts progression to dialysis and death in patients with chronic kidney disease: a competing risks modeling approach. , 2005, Journal of the American Society of Nephrology : JASN.

[27]  Robert Gray,et al.  A Proportional Hazards Model for the Subdistribution of a Competing Risk , 1999 .

[28]  D. Bluemke,et al.  Traditional cardiovascular risk factors in relation to left ventricular mass, volume, and systolic function by cardiac magnetic resonance imaging: the Multiethnic Study of Atherosclerosis. , 2006, Journal of the American College of Cardiology.

[29]  R. Gill,et al.  Cox's regression model for counting processes: a large sample study : (preprint) , 1982 .

[30]  R. Wolfe,et al.  Shared Frailty Models for Recurrent Events and a Terminal Event , 2004, Biometrics.

[31]  W. Hörl,et al.  Effect of radio contrast media on residual renal function in peritoneal dialysis patients--a prospective study. , 2006, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[32]  L. J. Wei,et al.  The Robust Inference for the Cox Proportional Hazards Model , 1989 .