Document Detail


Data mining and statistically guided clinical review of adverse event data in clinical trials.
MedLine Citation:
PMID:  20183445     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Some approaches to the analysis of adverse event data arising from clinical trials are presented. These include (a) an inside-out data mining method where the adverse events are used as explanatory variables, classifying the treatment allocation, (b) a support method where we fit separate regression models to each adverse event with and without a treatment effect, and (c) a three-level hierarchical Bayesian mixture model for analysis of adverse event counts. The problem of understanding treatment-emergence of the adverse events is formulated as one of data mining rather than hypothesis testing. Our approaches provide an ordering of the adverse events by the strength of evidence of a treatment effect, rather than p values for prespecified hypotheses. The three methods produce intuitive graphical summaries showing the treatment effect on adverse event incidence. These graphs can be readily linked to relevant supportive information such as reports summarizing predicted risks for (demographic) subpopulations of interest and patient-level data such as laboratory information, concomitant medications, and medical history. This results in a statistically guided and thorough review of drug safety in the clinical trial.
Authors:
H Southworth; M O'Connell
Related Documents :
11297895 - Role of modelling and simulation in phase i drug development.
14578245 - Archimedes: a trial-validated model of diabetes.
7997715 - A unified method for monitoring and analysing controlled trials.
7913675 - Interim monitoring of bivariate responses using repeated confidence intervals.
18562125 - Using the timeline followback to determine time windows representative of annual alcoho...
12150595 - Probabilistic analysis of cost-effectiveness models: choosing between treatment strateg...
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Journal of biopharmaceutical statistics     Volume:  19     ISSN:  1520-5711     ISO Abbreviation:  J Biopharm Stat     Publication Date:  2009 Sep 
Date Detail:
Created Date:  2010-02-25     Completed Date:  2010-05-20     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9200436     Medline TA:  J Biopharm Stat     Country:  England    
Other Details:
Languages:  eng     Pagination:  803-17     Citation Subset:  IM    
Affiliation:
Astra Zeneca. Harry.Southworth@astrazeneca.com
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Bayes Theorem
Computer Graphics
Data Interpretation, Statistical
Data Mining / statistics & numerical data*
Drug Therapy / adverse effects*
Evidence-Based Medicine / statistics & numerical data
Humans
Likelihood Functions
Models, Statistical*
Randomized Controlled Trials as Topic / statistics & numerical data*
Regression Analysis
Risk Assessment
Risk Factors
Treatment Outcome

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


Previous Document:  Estimation of Multiple Response Rates in Phase II Clinical Trials with Missing Observations.
Next Document:  Equivalence testing for parallelism in the four-parameter logistic model.