Document Detail


A model for a proportional treatment effect on disease progression.
MedLine Citation:
PMID:  11414556     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Treatments intended to slow the progression of chronic diseases are often hypothesized to reduce the rate of further injury to a biological system without improving the current level of functioning. In this situation, the treatment effect may be negligible for patients whose disease would have been stable without the treatment but would be expected to be an increasing function of the progression rate in patients with worsening disease. This article considers a variation of the Laird Ware mixed effects model in which the effect of the treatment on the slope of a longitudinal outcome is assumed to be proportional to the progression rate for patients with progressive disease. Inference based on maximum likelihood and a generalized estimating equations procedure is considered. Under the proportional effect assumption, the precision of the estimated treatment effect can be increased by incorporating the functional relationship between the model parameters and the variance of the outcome variable, particularly when the magnitude of the mean slope of the outcome is small compared with the standard deviation of the slopes. An example from a study of chronic renal disease is used to illustrate insights provided by the proportional effect model that may be overlooked with models assuming additive treatment effects.
Authors:
T Greene
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Publication Detail:
Type:  Comparative Study; Journal Article    
Journal Detail:
Title:  Biometrics     Volume:  57     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  2001 Jun 
Date Detail:
Created Date:  2001-06-20     Completed Date:  2001-12-04     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  354-60     Citation Subset:  IM    
Affiliation:
Department of Biostatistics and Epidemiology, The Cleveland Clinic Foundation, Ohio 44195, USA. tgreene@bio.ri.ccf.org
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MeSH Terms
Descriptor/Qualifier:
Confidence Intervals
Diet, Protein-Restricted
Disease Progression*
Follow-Up Studies
Glomerular Filtration Rate
Humans
Kidney Diseases / diet therapy
Models, Statistical
Proportional Hazards Models
Time Factors
Treatment Outcome

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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