Document Detail


Correction for non-compliance in equivalence trials.
MedLine Citation:
PMID:  9493255     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In randomized trials comparing a new therapy to standard therapy, the sharp null hypothesis of equivalent therapeutic efficacy does not imply the intent-to-treat null hypothesis of equal outcome distributions in the two-treatment arm if non-compliance is present. As a consequence, the development of analytic methods that adjust for non-compliance is of particular importance in equivalence trials comparing a new therapy to standard therapy. This paper provides, in the context of equivalence trial, a unified overview of various analytic approaches to correct for non-compliance in randomized trials. The overview focuses on comparing and contrasting the plausibility, robustness, and strength of assumptions required by each method and their programming and computational burdens. In addition, several new structural (causal) models are introduced: the coarse structural nested models, the non-nested marginal structural models and the continuous-time structural nested models, and their properties are compared with those of previously proposed structural nested models. The fundamental assumption that allows us to correct for non-compliance is that the decision whether or not to continue to comply with assigned therapy at time t is random (that is, ignorable or explainable) conditional on the history up to t of measured pre- and time-dependent post-randomization prognostic factors. In the final sections of the paper, we consider how the consequences of violations of our assumption of conditionally ignorable non-compliance can be explored through a sensitivity analysis. Finally, the analytic methods described in this paper can also be used to estimate the causal effect of a time-varying treatment from observational data.
Authors:
J M Robins
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  Statistics in medicine     Volume:  17     ISSN:  0277-6715     ISO Abbreviation:  Stat Med     Publication Date:  1998 Feb 
Date Detail:
Created Date:  1998-04-13     Completed Date:  1998-04-13     Revised Date:  2007-11-15    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  ENGLAND    
Other Details:
Languages:  eng     Pagination:  269-302; discussion 387-9     Citation Subset:  IM    
Affiliation:
Epidemiology Department, Harvard School of Public Health, Boston, MA 02115, USA.
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Data Interpretation, Statistical*
Humans
Models, Statistical*
Models, Structural
Randomized Controlled Trials as Topic*
Therapeutic Equivalency*
Treatment Refusal*
Grant Support
ID/Acronym/Agency:
2P30 ES00002/ES/NIEHS NIH HHS; R01-A132475//PHS HHS; R01-ES03405/ES/NIEHS NIH HHS

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


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