Statistical Models for Longitudinal Studies of Health

Statistical Models for Longitudinal Studies of Health

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Description

Longitudinal studies of health outcomes and their risk factors are an increasingly important source of knowledge in epidemiology, public health and clinical medicine. However, many of the statistical procedures suited to the analysis of findings from these studies have emerged only recently. This volume addresses theoretical and practical issues, demonstrating techniques with empirical examples. The topics covered include the specification and estimation of time-related statistical models involving continuous and categorical variables, fixed and random effects, variable coefficients, unconditional and conditional (autoregressive) structures, missing data and errors attrition, non-sampling errors of measurement, latent variables, reciprocal effects, stochastic differentials, empirical Bayes, weighted least squares estimation and biologic processses. Methodological issues in the design of large longitudinal surveys are also discussed.show more

Product details

  • Hardback | 396 pages
  • 150 x 230 x 28mm | 845g
  • Oxford University Press Inc
  • New York, United States
  • English
  • line illustrations, tables
  • 0195054733
  • 9780195054736

Table of contents

Introduction to statistical models for longitudinal observation, James Dwyer and Manning Feinleib; Considerations in the design of longitudinal surveys of health, Lester Curtin and Manning Feinleib: PART I: MODELS FOR CONTINUOUS VARIABLES: Linear differential equation models for longitudinal data: Application: blood pressure and relative weight, James Dwyer; Unconditional linear models for longitudinal data: Application: lead exposure and cognitive development in infants, Christine Waternaux, Nan Laird and James Ware; Conditional linear models for longitudinal data: Application: cigarette smoking and respiratory function in adolescents, Bernard Rosner and Alvara Munoz; Linear structural equation modelling with non-normal continuous variables: Application: relations among social support, drug use and health in young adults, Peter Bentler and Michael Newcomb: PART II MODELS FOR CATEGORICAL DATA: Use of the logistic and related models in longitudinal studies of chronic disease risk: Application: coronary heart disease mortality in Framingham, Norman Breslow; Generalized linear models for longitudinal data: Application: xeropthalmia in Indonesia, Lawrence Moulton; Some aspects of weighted least-squares analysis for longitudinal categorical data: Application: clinical trial of treatment for skin disorder, Garry Koch, Julio Singer and Maura Stokes: PART III: SPECIAL PROBLEMS IN THE MODELLING OF LONGITUDINAL OBSERVATIONS: Non-random attrition in the Framingham Heart Study: Application: age trends in blood pressure, Manning Feinleib and Joan Pinsky.show more