Show simple item record

Incorporating baseline covariates to validate surrogate endpoints with a constant biomarker under control arm

dc.contributor.authorRoberts, Emily K.
dc.contributor.authorElliott, Michael R.
dc.contributor.authorTaylor, Jeremy M. G.
dc.date.accessioned2021-12-02T02:32:05Z
dc.date.available2023-01-01 21:32:03en
dc.date.available2021-12-02T02:32:05Z
dc.date.issued2021-12-20
dc.identifier.citationRoberts, Emily K.; Elliott, Michael R.; Taylor, Jeremy M. G. (2021). "Incorporating baseline covariates to validate surrogate endpoints with a constant biomarker under control arm." Statistics in Medicine 40(29): 6605-6618.
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.urihttps://hdl.handle.net/2027.42/171046
dc.publisherWiley Periodicals, Inc.
dc.subject.othersurrogate endpoints
dc.subject.othersubgroup effects
dc.subject.otherprincipal stratification
dc.subject.otherBayesian methods
dc.titleIncorporating baseline covariates to validate surrogate endpoints with a constant biomarker under control arm
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbsecondlevelStatistics and Numeric Data
dc.subject.hlbsecondlevelPublic Health
dc.subject.hlbtoplevelHealth Sciences
dc.subject.hlbtoplevelScience
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/171046/1/sim9201_am.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/171046/2/sim9201-sup-0001-supinfo.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/171046/3/sim9201.pdf
dc.identifier.doi10.1002/sim.9201
dc.identifier.sourceStatistics in Medicine
dc.identifier.citedreferenceConlon AS, Taylor JM, Elliott MR. Surrogacy assessment using principal stratification when surrogate and outcome measures are multivariate normal. Biostatistics. 2014; 15 ( 2 ): 266 ‐ 283.
dc.identifier.citedreferencePréziosi MP, Halloran ME. Effects of pertussis vaccination on disease: vaccine efficacy in reducing clinical severity. Clin Infect Dis. 2003; 37 ( 6 ): 772 ‐ 779.
dc.identifier.citedreferenceHudgens MG, Halloran ME. Causal vaccine effects on binary postinfection outcomes. J Am Stat Assoc. 2006; 101 ( 473 ): 51 ‐ 64.
dc.identifier.citedreferenceFollmann D. Augmented designs to assess immune response in vaccine trials. Biometrics. 2006; 62 ( 4 ): 1161 ‐ 1169.
dc.identifier.citedreferenceGabriel EE, Gilbert PB. Evaluating principal surrogate endpoints with time‐to‐event data accounting for time‐varying treatment efficacy. Biostatistics. 2014; 15 ( 2 ): 251 ‐ 265.
dc.identifier.citedreferenceGabriel EE, Follmann D. Augmented trial designs for evaluation of principal surrogates. Biostatistics. 2016; 17 ( 3 ): 453 ‐ 467.
dc.identifier.citedreferenceGilbert PB, Qin L, Self SG. Evaluating a surrogate endpoint at three levels, with application to vaccine development. Stat Med. 2008; 62 ( 4 ): 4758 ‐ 4778.
dc.identifier.citedreferenceWolfson J, Gilbert P. Statistical identifiability and the surrogate endpoint problem, with application to vaccine trials. Biometrics. 2010; 66 ( 4 ): 1153 ‐ 1161.
dc.identifier.citedreferenceHuang Y, Gilbert PB, Wolfson J. Design and estimation for evaluating principal surrogate markers in vaccine trials. Biometrics. 2013; 69 ( 2 ): 301 ‐ 309.
dc.identifier.citedreferenceZhuang Y, Huang Y, Gilbert PB. Simultaneous inference of treatment effect modification by intermediate response endpoint principal strata with application to vaccine trials. Int J Biostat. 2019; 16 ( 1 ): 20180058.
dc.identifier.citedreferenceGilbert PB, Huang Y. Predicting overall vaccine efficacy in a new setting by re‐calibrating baseline covariate and intermediate response endpoint effect modifiers of type‐specific vaccine efficacy. Epidemiol Methods. 2016; 5 ( 1 ): 93 ‐ 112.
dc.identifier.citedreferenceShepherd BE, Gilbert PB, Jemiai Y, Rotnitzky A. Sensitivity analyses comparing outcomes only existing in a subset selected post‐randomization, conditional on covariates, with application to HIV vaccine trials. Biometrics. 2006; 62 ( 2 ): 332 ‐ 342.
dc.identifier.citedreferenceZigler CM, Belin TR. A Bayesian approach to improved estimation of causal effect predictiveness for a principal surrogate endpoint. Biometrics. 2012; 68 ( 3 ): 922 ‐ 932.
dc.identifier.citedreferenceAlonso A, Van der Elst W, Meyvisch P. Assessing a surrogate predictive value: a causal inference approach. Stat Med. 2017; 36 ( 7 ): 1083 ‐ 1098.
dc.identifier.citedreferenceConlon A, Taylor J, Li Y, Diaz‐Ordaz K, Elliott M. Links between causal effects and causal association for surrogacy evaluation in a Gaussian setting. Stat Med. 2017; 36 ( 27 ): 4243 ‐ 4265.
dc.identifier.citedreferenceTaylor JM, Conlon AS, Elliott MR. Surrogacy assessment using principal stratification with multivariate normal and Gaussian copula models. Clin Trials. 2015; 12 ( 4 ): 317 ‐ 322.
dc.identifier.citedreferenceDing P, Feller A, Miratrix L. Randomization inference for treatment effect variation; 2014. arXiv preprint. arXiv:1412.5000.
dc.identifier.citedreferenceParast L, Cai T, Tian L. Nonparametric estimation of the proportion of treatment effect explained by a surrogate marker using censored data. Technical report. 2016.
dc.identifier.citedreferenceParast L, McDermott MM, Tian L. Robust estimation of the proportion of treatment effect explained by surrogate marker information. Stat Med. 2016; 35 ( 10 ): 1637 ‐ 1653.
dc.identifier.citedreferenceBarnard J, McCulloch R, Meng XL. Modeling covariance matrices in terms of standard deviations and correlations, with application to shrinkage. Stat Sin. 2000; 10 ( 4 ): 1281 ‐ 1311.
dc.identifier.citedreferenceGustafson P. What are the limits of posterior distributions arising from nonidentified models, and why should we care? J Am Stat Assoc. 2009; 104 ( 488 ): 1682 ‐ 1695.
dc.identifier.citedreferenceMuntoni F, Domingos J, Manzur AY, et al. Categorising trajectories and individual changes of the North Star Ambulatory Assessment in patients with Duchenne muscular dystrophy. PLoS One. 2019; 14 ( 9 ): e0221097.
dc.identifier.citedreferenceKim C, Daniels MJ, Marcus BH, Roy JA. A framework for Bayesian nonparametric inference for causal effects of mediation. Biometrics. 2017; 73 ( 2 ): 401 ‐ 409.
dc.identifier.citedreferenceMa Y, Roy J, Marcus B. Causal models for randomized trials with two active treatments and continuous compliance. Stat Med. 2011; 30 ( 19 ): 2349 ‐ 2362.
dc.identifier.citedreferenceDaniels MJ, Roy JA, Kim C, Hogan JW, Perri MG. Bayesian inference for the causal effect of mediation. Biometrics. 2012; 68 ( 4 ): 1028 ‐ 1036.
dc.identifier.citedreferencePrentice R. Surrogate endpoints in clinical trials: definition and operational criteria. Stat Med. 1989; 8 ( 4 ): 431 ‐ 440.
dc.identifier.citedreferenceVanderWeele TJ. Surrogate measures and consistent surrogates. Biometrics. 2013; 69 ( 3 ): 561 ‐ 565.
dc.identifier.citedreferenceFreedman LS, Graubard BI, Schatzkin A. Statistical validation of intermediate endpoints for chronic diseases. Stat Med. 1992; 11 ( 2 ): 167 ‐ 178.
dc.identifier.citedreferenceRubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol. 1974; 66 ( 5 ): 688 ‐ 701.
dc.identifier.citedreferenceLittle R, Rubin D. Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches. Annu Rev Public Health. 2000; 21 ( 1 ): 121 ‐ 145.
dc.identifier.citedreferenceFrangakis C, Rubin D. Principal stratification in causal inference. Biometrics. 2002; 58 ( 1 ): 21 ‐ 29.
dc.identifier.citedreferenceGilbert P, Hudgens M. Evaluating candidate principal surrogate endpoints. Biometrics. 2008; 64 ( 4 ): 1146 ‐ 1154.
dc.identifier.citedreferenceMendell JR, Sahenk Z, Lehman K, et al. Assessment of systemic delivery of rAAVrh74.MHCK7.micro‐dystrophin in children with duchenne muscular dystrophy: a nonrandomized controlled trial. JAMA Neurol. 2020; 77 ( 9 ): 1122 ‐ 1131.
dc.identifier.citedreferenceHalloran ME, Préziosi MP, Chu H. Estimating vaccine efficacy from secondary attack rates. J Am Stat Assoc. 2003; 98 ( 461 ): 38 ‐ 46.
dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.