Safety surveillance and the estimation of risk in select populations: Flexible methods to control for confounding while targeting marginal comparisons via standardization
dc.contributor.author | Shi, Xu | |
dc.contributor.author | Wellman, Robert | |
dc.contributor.author | Heagerty, Patrick J. | |
dc.contributor.author | Nelson, Jennifer C. | |
dc.contributor.author | Cook, Andrea J. | |
dc.date.accessioned | 2020-01-13T15:04:56Z | |
dc.date.available | WITHHELD_14_MONTHS | |
dc.date.available | 2020-01-13T15:04:56Z | |
dc.date.issued | 2020-02-20 | |
dc.identifier.citation | Shi, Xu; Wellman, Robert; Heagerty, Patrick J.; Nelson, Jennifer C.; Cook, Andrea J. (2020). "Safety surveillance and the estimation of risk in select populations: Flexible methods to control for confounding while targeting marginal comparisons via standardization." Statistics in Medicine 39(4): 369-386. | |
dc.identifier.issn | 0277-6715 | |
dc.identifier.issn | 1097-0258 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/152565 | |
dc.publisher | Wiley Periodicals, Inc. | |
dc.publisher | Cambridge University Press | |
dc.subject.other | electronic health records | |
dc.subject.other | rare adverse event | |
dc.subject.other | pharmacosurveillance | |
dc.subject.other | causal inference | |
dc.subject.other | propensity score | |
dc.title | Safety surveillance and the estimation of risk in select populations: Flexible methods to control for confounding while targeting marginal comparisons via standardization | |
dc.type | Article | |
dc.rights.robots | IndexNoFollow | |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | |
dc.subject.hlbsecondlevel | Public Health | |
dc.subject.hlbsecondlevel | Medicine (General) | |
dc.subject.hlbtoplevel | Health Sciences | |
dc.subject.hlbtoplevel | Science | |
dc.subject.hlbtoplevel | Social Sciences | |
dc.description.peerreviewed | Peer Reviewed | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/152565/1/sim8410_am.pdf | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/152565/2/sim8410.pdf | |
dc.identifier.doi | 10.1002/sim.8410 | |
dc.identifier.source | Statistics in Medicine | |
dc.identifier.citedreference | Hahn J, Ridder G. Asymptotic variance of semiparametric estimators with generated regressors. Econometrica. 2013; 81 ( 1 ): 315 ‐ 340. | |
dc.identifier.citedreference | De Boor C. A practical guide to splines. In: Applied Mathematical Sciences. Vol 27. New York, NY: Springer; 1978. | |
dc.identifier.citedreference | De Boor C. On calculating with B‐splines. J Approx Theory. 1972; 6: 50 ‐ 62. | |
dc.identifier.citedreference | Hansen BE. The integrated mean squared error of series regression and a Rosenthal Hilbert‐space inequality. Econometric Theory. 2015; 31 ( 2 ): 337 ‐ 361. | |
dc.identifier.citedreference | Localio AR, Margolis DJ, Berlin JA. Relative risks and confidence intervals were easily computed indirectly from multivariable logistic regression. J Clin Epidemiol. 2007; 60 ( 9 ): 874 ‐ 882. | |
dc.identifier.citedreference | Greenland S. Introduction to Regression Modelling. Chapter 21. In: Rothman KJ, Greenland S, Lash TL, eds. Introduction to Regression Modelling. Philadelphia, PA: Lippincott Williams & Wilkins; 2008. | |
dc.identifier.citedreference | Gruber S, van der Laan MJ. TMLE: an R package for targeted maximum likelihood estimation. Journal of Statistical Software. 2012; 51 ( 13 ). | |
dc.identifier.citedreference | Van der Vaart AW. On differentiable functionals. Ann Stat. 1991; 19: 178 ‐ 204. | |
dc.identifier.citedreference | Newey WK. The asymptotic variance of semiparametric estimators. Econometrica. 1994; 62 ( 6 ): 1349 ‐ 1382. | |
dc.identifier.citedreference | Van der Vaart AW. Asymptotic Statistics. Vol 3. Cambridge, UK: Cambridge University Press; 1998. | |
dc.identifier.citedreference | Franklin JM, Eddings W, Austin PC, Stuart EA, Schneeweiss S. Comparing the performance of propensity score methods in healthcare database studies with rare outcomes. Statis Med. 2017; 36 ( 12 ): 1946 ‐ 1963. | |
dc.identifier.citedreference | Hampel FR. The influence curve and its role in robust estimation. J Am Stat Assoc. 1974; 69 ( 346 ): 383 ‐ 393. | |
dc.identifier.citedreference | Behrman RE, Benner JS, Brown JS, McClellan M, Woodcock J, Platt R. Developing the Sentinel System–a national resource for evidence development. N Engl J Med. 2011; 364 ( 6 ): 498 ‐ 499. | |
dc.identifier.citedreference | Härdle W. Applied Nonparametric Regression. Cambridge, UK: Cambridge University Press; 1990. | |
dc.identifier.citedreference | Rosenbaum PR, Rubin D. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983; 70 ( 1 ): 41 ‐ 55. | |
dc.identifier.citedreference | Robins J. A new approach to causal inference in mortality studies with a sustained exposure period–application to control of the healthy worker survivor effect. Mathematical Modelling. 1986; 7 ( 9‐12 ): 1393 ‐ 1512. | |
dc.identifier.citedreference | Snowden JM, Rose S, Mortimer KM. Implementation of G‐computation on a simulated data set: demonstration of a causal inference technique. Am J Epidemiol. 2011; 173 ( 7 ): 731 ‐ 738. | |
dc.identifier.citedreference | Vansteelandt S, Keiding N. Invited commentary: G‐computation–lost in translation? Am J Epidemiol. 2011; 173 ( 7 ): 739 ‐ 742. | |
dc.identifier.citedreference | Austin PC, Grootendorst P, Normand S‐LT, Anderson GM. Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study. Statis Med. 2007; 26 ( 4 ): 754 ‐ 768. | |
dc.identifier.citedreference | Austin PC. The performance of different propensity score methods for estimating marginal odds ratios. Statis Med. 2007; 26 ( 16 ): 3078 ‐ 3094. | |
dc.identifier.citedreference | Robins JM, Rotnitzky A. Comment on “Inference for semiparametric models: Some questions and an answer” by P.J. Bickel and J. Kwon. Statistica Sinica. 2001; 11: 920 ‐ 936. | |
dc.identifier.citedreference | Hade EM, Lu B. Bias associated with using the estimated propensity score as a regression covariate. Statis Med. 2014; 33 ( 1 ): 74 ‐ 87. | |
dc.identifier.citedreference | Vansteelandt S, Daniel RM. On regression adjustment for the propensity score. Statis Med. 2014; 33 ( 23 ): 4053 ‐ 4072. | |
dc.identifier.citedreference | Wan F, Mitra N. An evaluation of bias in propensity score‐adjusted non‐linear regression models. Stat Methods Med Res. 2016; 27 ( 3 ): 846 ‐ 862. | |
dc.identifier.citedreference | Little R, An H. Robust likelihood‐based analysis of multivariate data with missing values. Statistica Sinica. 2004; 14: 949 ‐ 968. | |
dc.identifier.citedreference | Gutman R, Rubin D. Estimation of causal effects of binary treatments in unconfounded studies. Statis Med. 2015; 34 ( 26 ): 3381 ‐ 3398. | |
dc.identifier.citedreference | Schafer JL, Kang J. Average causal effects from nonrandomized studies: a practical guide and simulated example. Psychological Methods. 2008; 13 ( 4 ): 279. | |
dc.identifier.citedreference | Myers JA, Louis TA. Comparing treatments via the propensity score: stratification or modeling? Health Serv Outcomes Res Methodol. 2012; 12 ( 1 ): 29 ‐ 43. | |
dc.identifier.citedreference | Tsiatis A. Semiparametric Theory and Missing Data. New York, NY: Springer Science+Business Media; 2007. | |
dc.identifier.citedreference | Hahn J, Ridder G. The asymptotic variance of semi‐parametric estimators with generated regressors. Centre for Microdata Methods and Practice, Institute for Fiscal Studies; 2010. | |
dc.identifier.citedreference | Hahn J, Liao Z, Ridder G. Nonparametric two‐step sieve M estimation and inference. Econometric Theory. 2018; 34 ( 6 ): 1281 ‐ 1324. | |
dc.identifier.citedreference | Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000; 11 ( 5 ): 550 ‐ 560. | |
dc.identifier.citedreference | Potter FJ. A study of procedures to identify and trim extreme sampling weights. In: Proceedings of the American Statistical Association, Section on Survey Research Methods. Washington, DC: American Statistical Association; 1990: 225 ‐ 230. | |
dc.identifier.citedreference | Potter FJ. The effect of weight trimming on nonlinear survey estimates. In: Proceedings of the American Statistical Association, Section on Survey Research Methods. Washington, DC: American Statistical Association; 1993: 758 ‐ 763. | |
dc.identifier.citedreference | Robins JM, Rotnitzky A, Zhao LP. Estimation of regression coefficients when some regressors are not always observed. J Am Stat Assoc. 1994; 89 ( 427 ): 846 ‐ 866. | |
dc.identifier.citedreference | Bang H, Robins JM. Doubly robust estimation in missing data and causal inference models. Biometrics. 2005; 61 ( 4 ): 962 ‐ 973. | |
dc.identifier.citedreference | van der Laan MJ, Rubin D. Targeted maximum likelihood learning. Int J Biostat. 2006; 2 ( 1 ). | |
dc.identifier.citedreference | van der Laan MJ, Polley EC, Hubbard AE. Super learner. Stat Appl Genet Mol Biol. 2007; 6 ( 1 ). | |
dc.identifier.citedreference | Nelson JC, Boudreau D, Wellman R, et al. Improving sequential safety surveillance planning methods for routine assessments that use regression adjustment or weighting to control confounding. Mini‐Sentinel Methods Report; 2016. https://www.sentinelsystem.org/sentinel/methods/routine-prospective-safety-surveillance-new-drugs-vaccines-and-other-biologic/ | |
dc.identifier.citedreference | Franklin JM, Schneeweiss S, Polinski JM, Rassen JA. Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases. Comput Stat Data Anal. 2014; 72: 219 ‐ 226. | |
dc.identifier.citedreference | Cook AJ, Wellman R, Shoaibi A, et al. Safety signalling methods for survival outcomes to control for confounding in the mini‐sentinel distributed database. FDA’s Sentinel Initiative: Project Report; 2018. https://www.sentinelinitiative.org/sites/default/files/Methods/Mini-Sentinel_Methods_Survival_Outcomes_II_Final_Report.pdf | |
dc.identifier.citedreference | Roujeau JC, Stern RS. Severe adverse cutaneous reactions to drugs. N Engl J Med. 1994; 331 ( 19 ): 1272 ‐ 1285. | |
dc.identifier.citedreference | Imai K, Ratkovic M. Covariate balancing propensity score. J R Stat Soc Ser B Stat Methodol. 2014; 76 ( 1 ): 243 ‐ 263. | |
dc.identifier.citedreference | Hastie T, Tibshirani R. Varying‐coefficient models. J R Stat Soc Ser B Methodol. 1993; 55: 757 ‐ 796. | |
dc.identifier.citedreference | Härdle W, Liang H, Gao J. Partially Linear Models. Heidelberg, Germany: Springer Science+Business Media; 2012. | |
dc.identifier.citedreference | Hansen BE. Nonparametric sieve regression: Least squares, averaging least squares, and cross‐validation. In: Racine JS, Su L, Ullah A, eds. Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics. Oxford, UK: Oxford University Press; 2014. | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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