Document Detail


Using a serial marker to predict a repeated measures outcome in a cohort design--results of a simulation study.
MedLine Citation:
PMID:  15468767     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Consider the cohort design and suppose that the outcome of primary interest is a continuous random variable observed repeatedly over time. Suppose that there is a second variable of clinical relevance which is also observed repeatedly. We are interested in assessing whether the "serial marker" is in some sense predictive of the primary outcome. We would also like to predict the trend for the primary outcome assuming that the clinical marker follows a profile of specific clinical interest. In series of earlier papers, we have addressed these issues by applying a bivariate repeated measures model. One regression model was prescribed to relate the primary outcome to important explanatory variables, while a second regression model was prescribed for the serial marker. In this paper, we perform a series of simulation studies to investigate the empirical properties of this approach. Bivariate repeated measures data were generated at random, and basic study parameters including the sample size, the number of time points, the degree of serial correlation within the clinical marker, and type of association between the serial marker and the primary outcome were varied. The ability of the methodology to capture the underlying relationship between the two set of repeated measures was assessed. The ability to predicting the primary outcome corresponding to a known marker profile of specific interest was examined.
Authors:
James Rochon
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Journal of biopharmaceutical statistics     Volume:  14     ISSN:  1054-3406     ISO Abbreviation:  J Biopharm Stat     Publication Date:  2004 Aug 
Date Detail:
Created Date:  2004-10-07     Completed Date:  2005-01-26     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9200436     Medline TA:  J Biopharm Stat     Country:  United States    
Other Details:
Languages:  eng     Pagination:  817-33     Citation Subset:  IM    
Affiliation:
Department of Biostatistics & Bioinformatics and Duke Clinical Research Institute, Duke University, Durham, North Carolina 27715, USA. James.Rochon@Duke.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Cohort Studies*
Computer Simulation
Data Interpretation, Statistical
Humans
Likelihood Functions
Monte Carlo Method
Regression Analysis
Research Design / statistics & numerical data
Treatment Outcome*

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


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