Document Detail

Sequential causal inference: application to randomized trials of adaptive treatment strategies.
MedLine Citation:
PMID:  17914714     Owner:  NLM     Status:  MEDLINE    
Clinical trials that randomize subjects to decision algorithms, which adapt treatments over time according to individual response, have gained considerable interest as investigators seek designs that directly inform clinical decision making. We consider designs in which subjects are randomized sequentially at decision points, among adaptive treatment options under evaluation. We present a sequential method to estimate the comparative effects of the randomized adaptive treatments, which are formalized as adaptive treatment strategies. Our causal estimators are derived using Bayesian predictive inference. We use analytical and empirical calculations to compare the predictive estimators to (i) the 'standard' approach that allocates the sequentially obtained data to separate strategy-specific groups as would arise from randomizing subjects at baseline; (ii) the semi-parametric approach of marginal mean models that, under appropriate experimental conditions, provides the same sequential estimator of causal differences as the proposed approach. Simulation studies demonstrate that sequential causal inference offers substantial efficiency gains over the standard approach to comparing treatments, because the predictive estimators can take advantage of the monotone structure of shared data among adaptive strategies. We further demonstrate that the semi-parametric asymptotic variances, which are marginal 'one-step' estimators, may exhibit significant bias, in contrast to the predictive variances. We show that the conditions under which the sequential method is attractive relative to the other two approaches are those most likely to occur in real studies.
Ree Dawson; Philip W Lavori
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural    
Journal Detail:
Title:  Statistics in medicine     Volume:  27     ISSN:  0277-6715     ISO Abbreviation:  Stat Med     Publication Date:  2008 May 
Date Detail:
Created Date:  2008-04-09     Completed Date:  2008-09-03     Revised Date:  2013-06-06    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  England    
Other Details:
Languages:  eng     Pagination:  1626-45     Citation Subset:  IM    
Frontier Science & Technology Research Foundation, Boston, MA 02215, U.S.A.
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MeSH Terms
Bayes Theorem
Confounding Factors (Epidemiology)
Decision Support Techniques
Drug Administration Schedule
Models, Statistical*
Randomized Controlled Trials as Topic / methods*
Statistics, Nonparametric
Grant Support
R01 MH051481-10/MH/NIMH NIH HHS; R01-MH51481/MH/NIMH NIH HHS

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

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