Document Detail


Attributable effects in case2-studies.
MedLine Citation:
PMID:  15737100     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In an effort to determine whether a particular treatment causes a particular outcome event, data are obtained from a database system that records events when they occur, and for such events, the system records exposure to the treatment. That is, the system records information about cases. The system provides no information about events that might have occurred but did not, that is, about units which are not cases. Roughly speaking, we know the number of successes for two proportions, treated and control, but not the numbers of trials or units for these proportions; indeed, the concept of a "trial" may be somewhat vague. With no further information, the situation is quite hopeless. However, an interesting strategy that is sometimes used entails identifying two types of cases whose origin is entirely different so that it is known the cases of the second type were definitely not affected by the treatment under study. This strategy--the case-case or case2-study--seems to have been reinvented independently many times, and has recently been offered as a general strategy for infectious disease epidemiology by McCarthy and Giesecke (1999, International Journal of Epidemiology 28, 764-768). Can this strategy permit estimation of the number of cases caused by the treatment? Using attributable effects in a new way, a method of exact inference is proposed, along with a large sample approximation. Two examples are discussed: one concerning the effects of daytime running lights (DRLs) on the risk of multivehicle accidents; the other concerning the origin of a Salmonella infection. A counterexample with superficially similar appearance is also discussed concerning suicide rates following the publication of Final Exit; here, the treatment may alter the outcome, or it may alter the type, and the attributable effect cannot be estimated.
Authors:
Paul R Rosenbaum
Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Biometrics     Volume:  61     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  2005 Mar 
Date Detail:
Created Date:  2005-03-01     Completed Date:  2005-06-30     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  246-53     Citation Subset:  IM    
Affiliation:
Statistics Department, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6340, USA. rosenbaum@stat.wharton.upenn.edu
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Accidents, Traffic / statistics & numerical data
Biometry
Causality*
Databases, Factual
Humans
Models, Statistical*
Salmonella Infections / etiology
Suicide / statistics & numerical data
Treatment Outcome*

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


Previous Document:  Bayesian monitoring of clinical trials with failure-time endpoints.
Next Document:  Comparison of maximum statistics for hypothesis testing when a nuisance parameter is present only un...