Semiparametric models for longitudinal data with application to CD4 cell numbers in HIV seroconverters

Scott Zeger, P. J. Diggle

Research output: Contribution to journalArticle

Abstract

The paper describes a semiparametric model for longitudinal data which is illustrated by its application to data on the time evolution of CD4 cell numbers in HIV seroconverters. The essential ingredients of the model are a parametric linear model for covariate adjustment, a nonparametric estimation of a smooth time trend, serial correlation between measurements on an individual subject, and random measurement error. A back-fitting algorithm is used in conjunction with a cross-validation prescription to fit the model. A notable feature in the application is that the onset of HIV infection is associated with a sudden drop in CD4 cells followed by a longer-term slower decay. The model is also used to estimate an individual's curve by combining his data with the population curve. Shrinkage toward the population mean trajectory is controlled in a natural way by the estimated covariance structure of the data.

Original languageEnglish (US)
Pages (from-to)689-699
Number of pages11
JournalBiometrics
Volume50
Issue number3
DOIs
StatePublished - 1994

Fingerprint

Semiparametric Model
Longitudinal Data
Cell Count
HIV
Cell
Backfitting Algorithm
Population
HIV Infections
Prescriptions
Linear Models
HIV Infection
Serial Correlation
Curve
Random Error
Covariance Structure
cells
Nonparametric Estimation
Shrinkage
Parametric Model
Cross-validation

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Public Health, Environmental and Occupational Health
  • Agricultural and Biological Sciences (miscellaneous)
  • Applied Mathematics
  • Statistics and Probability

Cite this

Semiparametric models for longitudinal data with application to CD4 cell numbers in HIV seroconverters. / Zeger, Scott; Diggle, P. J.

In: Biometrics, Vol. 50, No. 3, 1994, p. 689-699.

Research output: Contribution to journalArticle

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