An Analysis of Paediatric Cd4 Counts for Acquired Immune Deficiency Syndrome Using Flexible Random Curves

SUMMARY In this paper we analyse CD4 counts from infants born to mothers who are infected with the human immunodeficiency virus. A random effects model with linear or low order polynomials in time is unsatisfactory for these longitudinal data We develop an alternative approach based on a flexible family of models for which both the fixed and the random effects are linear combinations of B-splines. The fixed and random parts are smooth functions of time and the covariance structure is parsimonious. The procedure allows estimates of each individual's smooth trajectory over time to be exhibited. Model selection, estimation and computation are discussed. Centile curves are presented that take into account the longitudinal nature of the data. We emphasize a graphical approach to the presentation of results.

[1]  C. Giaquinto,et al.  Age-related standards for T lymphocyte subsets based on uninfected children born to human immunodeficiency virus 1-infected women. The European Collaborative Study. , 1992, The Pediatric infectious disease journal.

[2]  M. Wulfsohn,et al.  Modeling the Relationship of Survival to Longitudinal Data Measured with Error. Applications to Survival and CD4 Counts in Patients with AIDS , 1995 .

[3]  R. Jennrich,et al.  Unbalanced repeated-measures models with structured covariance matrices. , 1986, Biometrics.

[4]  S. E. Hills,et al.  Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling , 1990 .

[5]  C. Kelly,et al.  Monotone smoothing with application to dose-response curves and the assessment of synergism. , 1990, Biometrics.

[6]  T J Cole,et al.  Smoothing reference centile curves: the LMS method and penalized likelihood. , 1992, Statistics in medicine.

[7]  A. Fiocchi,et al.  Prognostic significance of immunologic changes in 675 infants perinatally exposed to human immunodeficiency virus. The Italian Register for Human Immunodeficiency Virus Infection in Children. , 1991, The Journal of pediatrics.

[8]  N. Laird,et al.  Maximum likelihood computations with repeated measures: application of the EM algorithm , 1987 .

[9]  B. Silverman,et al.  Estimating the mean and covariance structure nonparametrically when the data are curves , 1991 .

[10]  Jeremy M. G. Taylor,et al.  A Stochastic Model for Analysis of Longitudinal AIDS Data , 1994 .

[11]  Carl de Boor,et al.  A Practical Guide to Splines , 1978, Applied Mathematical Sciences.

[12]  J M Taylor,et al.  Inference for smooth curves in longitudinal data with application to an AIDS clinical trial. , 1995, Statistics in medicine.

[13]  Richard A. Becker,et al.  The New S Language , 1989 .

[14]  M. Thompson,et al.  Maximum likelihood estimation of reference centiles. , 1990, Statistics in medicine.

[15]  R. Weiss,et al.  Residual plots for repeated measures. , 1992, Statistics in medicine.

[16]  J. Chambers,et al.  The New S Language , 1989 .

[17]  P. Diggle,et al.  Semiparametric models for longitudinal data with application to CD4 cell numbers in HIV seroconverters. , 1994, Biometrics.

[18]  P. Diggle An approach to the analysis of repeated measurements. , 1988, Biometrics.

[19]  J. Ware,et al.  Random-effects models for longitudinal data. , 1982, Biometrics.