Autoregressive models for describing non-linear changes in biological parameters fitted using BUGS.
Many biological processes give outcome data which show a curvilinear association with time which tends to an asymptote. We show how autoregressive models can be used to describe this association within individual subjects. We also present a Bayesian approach implemented using statistical software, BUGS, to fit these models in a multi-level (hierarchical) setting that describes variation in the association between subjects. Peak expiratory flow data from a clinical trial involving subjects with asthma are used to illustrate the methods.
Mander
A P
AP
MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge, CB2 2SR, U.K.
Hughes
M D
MD
Sharp
S J
SJ
Lamm
C J
CJ
D000893:Anti-Inflammatory Agents / Q000627:therapeutic use; D001249:Asthma / Q000188:drug therapy; D001499:Bayes Theorem; D019819:Budesonide / Q000627:therapeutic use; D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D008954:Models, Biological; D009716:Numerical Analysis, Computer-Assisted; D010366:Peak Expiratory Flow Rate; D012984:Software; D013997:Time Factors; D016896:Treatment Outcome
D000893:Anti-Inflammatory Agents; D019819:Budesonide