Counterfactual Links to the Proportion of Treatment Effect Explained by a Surrogate Marker
dc.contributor.author | Taylor, Jeremy M. G. | en_US |
dc.contributor.author | Wang, Yue | en_US |
dc.contributor.author | Thiébaut, Rodolphe | en_US |
dc.date.accessioned | 2010-04-01T14:53:54Z | |
dc.date.available | 2010-04-01T14:53:54Z | |
dc.date.issued | 2005-12 | en_US |
dc.identifier.citation | Taylor, Jeremy M. G.; Wang, Yue; ThiÉbaut, Rodolphe (2005). "Counterfactual Links to the Proportion of Treatment Effect Explained by a Surrogate Marker." Biometrics 61(4): 1102-1111. <http://hdl.handle.net/2027.42/65353> | en_US |
dc.identifier.issn | 0006-341X | en_US |
dc.identifier.issn | 1541-0420 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/65353 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=16401284&dopt=citation | en_US |
dc.description.abstract | In a randomized clinical trial, a statistic that measures the proportion of treatment effect on the primary clinical outcome that is explained by the treatment effect on a surrogate outcome is a useful concept. We investigate whether a statistic proposed to estimate this proportion can be given a causal interpretation as defined by models of counterfactual variables. For the situation of binary surrogate and outcome variables, two counterfactual models are considered, both of which include the concept of the proportion of the treatment effect, which acts through the surrogate. In general, the statistic does not equal either of the two proportions from the counterfactual models, and can be substantially different. Conditions are given for which the statistic does equal the counterfactual model proportions. A randomized clinical trial with potential surrogate endpoints is undertaken in a scientific context; this context will naturally place constraints on the parameters of the counterfactual model. We conducted a simulation experiment to investigate what impact these constraints had on the relationship between the proportion explained (PE) statistic and the counterfactual model proportions. We found that observable constraints had very little impact on the agreement between the statistic and the counterfactual model proportions, whereas unobservable constraints could lead to more agreement. | en_US |
dc.format.extent | 178707 bytes | |
dc.format.extent | 3110 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | Blackwell Publishing | en_US |
dc.rights | The International Biometric Society, 2005 | en_US |
dc.subject.other | Causal Effects | en_US |
dc.subject.other | Clinical Trial | en_US |
dc.subject.other | Counterfactual Model | en_US |
dc.subject.other | Direct Effect | en_US |
dc.subject.other | Indirect Effect | en_US |
dc.subject.other | Surrogate Marker | en_US |
dc.title | Counterfactual Links to the Proportion of Treatment Effect Explained by a Surrogate Marker | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A. | en_US |
dc.contributor.affiliationother | Research Laboratory, Merck & Co., West Point, Pennsylvania 19486, U.S.A. | en_US |
dc.contributor.affiliationother | INSERM E0338 Biostatistics, ISPED, Bordeaux 2 University, Bordeaux 33076, France | en_US |
dc.identifier.pmid | 16401284 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/65353/1/j.1541-0420.2005.00380.x.pdf | |
dc.identifier.doi | 10.1111/j.1541-0420.2005.00380.x | en_US |
dc.identifier.source | Biometrics | en_US |
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dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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