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Accounting for selection bias due to death in estimating the effect of wealth shock on cognition for the Health and Retirement Study
dc.contributor.authorTan, Yaoyuan Vincent
dc.contributor.authorFlannagan, Carol A. C.
dc.contributor.authorPool, Lindsay R.
dc.contributor.authorElliott, Michael R.
dc.date.accessioned2021-06-02T21:08:53Z
dc.date.available2022-06-02 17:08:52en
dc.date.available2021-06-02T21:08:53Z
dc.date.issued2021-05-20
dc.identifier.citationTan, Yaoyuan Vincent; Flannagan, Carol A. C.; Pool, Lindsay R.; Elliott, Michael R. (2021). "Accounting for selection bias due to death in estimating the effect of wealth shock on cognition for the Health and Retirement Study." Statistics in Medicine 40(11): 2613-2625.
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.urihttps://hdl.handle.net/2027.42/167825
dc.publisherCambridge University Press
dc.publisherWiley Periodicals, Inc.
dc.subject.othermissing data
dc.subject.othertime‐dependent confounding
dc.subject.othercausal inference
dc.subject.otherlongitudinal study
dc.subject.otherpenalized spline of propensity methods in treatment comparisons
dc.subject.otherBayesian additive regression trees
dc.titleAccounting for selection bias due to death in estimating the effect of wealth shock on cognition for the Health and Retirement Study
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelPublic Health
dc.subject.hlbsecondlevelStatistics and Numeric Data
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelHealth Sciences
dc.subject.hlbtoplevelSocial Sciences
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/167825/1/sim8921.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/167825/2/SIM_8921_appendix.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/167825/3/sim8921_am.pdf
dc.identifier.doi10.1002/sim.8921
dc.identifier.sourceStatistics in Medicine
dc.identifier.citedreferenceFrangakis CE, Rubin DB. Principal stratification in causal inference. Biometrics. 2002; 58 ( 1 ): 21 ‐ 29.
dc.identifier.citedreferenceLee Y, Back JH, Kim J, et al. Systematic review of health behavioral risks and cognitive health in older adults. Int Psychogeriatr. 2010; 22 ( 2 ): 174 ‐ 187.
dc.identifier.citedreferencePlassman BL, Williams JW Jr, Burke JR, Holsinger T, Benjamin S. Systematic review: factors associated with risk for and possible prevention of cognitive decline in later life. Ann Intern Med. 2010; 153 ( 3 ): 182 ‐ 193.
dc.identifier.citedreferenceKrieger N. Theories for social epidemiology in the 21st century: an eco‐social perspective. Int J Epidemiol. 2001; 30 ( 4 ): 668 ‐ 677.
dc.identifier.citedreferenceButrica BA, Smith KE, Toder EJ. What the 2008 stock market crash means for retirement security. J Aging Soc Policy. 2010; 22 ( 4 ): 339 ‐ 359.
dc.identifier.citedreferenceShrira A, Palgi Y, Ben‐Ezra M, Spalter T, Kavé G, Shmotkin D. For better and for worse: the relationship between future expectations and functioning in the second half of life. J Gerontol B Psychol Sci Soc Sci. 2011; 66 ( 2 ): 195 ‐ 203.
dc.identifier.citedreferenceSonnega A, Faul JD, Ofstedal MB, Langa KM, Phillips JW, Weir DR. Cohort profile: the health and retirement study (HRS). Int J Epidemiol. 2014; 43 ( 2 ): 576 ‐ 585.
dc.identifier.citedreferenceRosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983; 70 ( 1 ): 41 ‐ 55.
dc.identifier.citedreferenceImbens GW, Rubin DB. Causal Inference for Statistical, Social, and Biomedical Sciences: An Introduction. New York, NY: Cambridge University Press; 2015.
dc.identifier.citedreferenceRobins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000; 11 ( 5 ): 550 ‐ 560.
dc.identifier.citedreferenceZhou T, Elliott MR, Little RJA. Penalized spline of propensity methods for treatment comparison. J Am Stat Assoc. 2019; 114 ( 525 ): 1 ‐ 19.
dc.identifier.citedreferenceWeuve J, Tchetgen Tchetgen EJ, Glymour MM, et al. Accounting for bias due to selective attrition: the example of smoking and cognitive decline. Epidemiology. 2012; 23: 119 ‐ 128.
dc.identifier.citedreferenceChaix B, Evans D, Merlo J, Suzuki E. Commentary: weighing up the dead and missing: reflections on inverse probability weighting and principal stratification to address truncation by death. Epidemiology. 2012; 23 ( 1 ): 129 ‐ 131.
dc.identifier.citedreferenceNeyman J. On the two different aspects of the representative method: the method of stratified sampling and the method of purposive selection. J R Stat Soc. 1934; 97 ( 4 ): 558 ‐ 625.
dc.identifier.citedreferenceRubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol. 1974; 66 ( 5 ): 688 ‐ 701.
dc.identifier.citedreferenceElliott MR, Joffe MM, Chen Z. A potential outcomes approach to developmental toxicity analyses. Biometrics. 2006; 62 ( 2 ): 352 ‐ 360.
dc.identifier.citedreferencePool LR, Burgard SA, Needham BL, Elliott MR, Langa KM, Leon CFM. Association of a negative wealth shock with all‐cause mortality in middle‐aged and older adults in the united states. J Am Med Assoc. 2018; 319 ( 13 ): 1341 ‐ 1350.
dc.identifier.citedreferenceZhang G, Little RJA. Extensions of the penalized spline of propensity prediction method of imputation. Biometrics. 2009; 65: 911 ‐ 918.
dc.identifier.citedreferenceChipman HA, George EI, McCulloch RE. BART: Bayesian additive regression trees. Ann Appl Stat. 2010; 4 ( 1 ): 266 ‐ 298.
dc.identifier.citedreferenceTan YV, Flannagan CAC, Elliott MR. “ Robust‐squared” imputation models using BART. Journal of Survey Statistics and Methodology. 2019; 7 ( 4 ): 465 ‐ 497.
dc.identifier.citedreferenceHeitjan D, Little RJA. Multiple imputation for the fatal accident reporting system. Appl Stat. 1991; 40: 13 ‐ 29.
dc.identifier.citedreferenceJuster FT, Smith JP. Improving the quality of economic data: lessons from the HRS and AHEAD. J Am Stat Assoc. 1997; 92 ( 440 ): 1268 ‐ 1278.
dc.identifier.citedreferencePool LR, Needham BL, Burgard SA, Elliott MR, Leon CFM. Negative wealth shock and short‐term changes in depressive symptoms and medication adherence among late middle‐aged adults. J Epidemiol Community Health. 2017; 71: 758 ‐ 763.
dc.identifier.citedreferenceCrimmins EM, Kim JK, Langa KM, Weir DR. Assessment of cognition using surveys and neuropsychological assessment: the health and retirement study and the aging, demographics and memory study. J Gerontol Ser B Psychol Sci Soc Sci. 2011; 66 ( Suppl 1 ): 162 ‐ 171.
dc.identifier.citedreferenceKorn EL, Graubard BI. Examples of differing weighted and unweighted estimates from a sample survey. Am Stat. 1995; 49 ( 3 ): 291 ‐ 295.
dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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