Combining Super Learner with high-dimensional propensity score to improve confounding adjustment: A real-world application in chronic lymphocytic leukemia.
Super Learner
bleeding
chronic lymphocytic leukemia
confounding
high-dimensional propensity score
observational study
pharmacoepidemiology
Journal
Pharmacoepidemiology and drug safety
ISSN: 1099-1557
Titre abrégé: Pharmacoepidemiol Drug Saf
Pays: England
ID NLM: 9208369
Informations de publication
Date de publication:
23 Aug 2023
23 Aug 2023
Historique:
revised:
28
07
2023
received:
15
04
2023
accepted:
02
08
2023
medline:
23
8
2023
pubmed:
23
8
2023
entrez:
23
8
2023
Statut:
aheadofprint
Résumé
High-dimensional propensity score (hdPS) is a semiautomated method that leverages a vast number of covariates available in healthcare databases to improve confounding adjustment. A novel combined Super Learner (SL)-hdPS approach was proposed to assist with selecting the number of covariates for propensity score inclusion, and was found in plasmode simulation studies to improve bias reduction and precision compared to hdPS alone. However, the approach has not been examined in the applied setting. We compared SL-hdPS's performance with that of several hdPS models, each with prespecified covariates and a different number of empirically-identified covariates, using a cohort study comparing real-world bleeding rates between ibrutinib- and bendamustine-rituximab (BR)-treated individuals with chronic lymphocytic leukemia in Optum's de-identified Clinformatics® Data Mart commercial claims database (2013-2020). We used inverse probability of treatment weighting for confounding adjustment and Cox proportional hazards regression to estimate hazard ratios (HRs) for bleeding outcomes. Parameters of interest included prespecified and empirically-identified covariate balance (absolute standardized difference [ASD] thresholds of <0.10 and <0.05) and outcome HR precision (95% confidence intervals). We identified 2423 ibrutinib- and 1102 BR-treated individuals. Including >200 empirically-identified covariates in the hdPS model compromised covariate balance at both ASD thresholds. SL-hdPS balanced more covariates than all individual hdPS models at both ASD thresholds. The bleeding HR 95% confidence intervals were generally narrower with SL-hdPS than with individual hdPS models. In a real-world application, hdPS was sensitive to the number of covariates included, while use of SL for covariate selection resulted in improved covariate balance and possibly improved precision.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NIGMS NIH HHS
ID : T32 GM075766
Pays : United States
Organisme : NHLBI NIH HHS
Pays : United States
Organisme : NIA NIH HHS
Pays : United States
Informations de copyright
© 2023 John Wiley & Sons Ltd.
Références
Jick H. The discovery of drug-induced illness. N Engl J Med. 1977;296(9):481-485. doi:10.1056/NEJM197703032960904
Centers for E, Research on Therapeutics Risk Assessment W. Risk assessment of drugs, biologics and therapeutic devices: present and future issues. Pharmacoepidemiol Drug Saf. 2003;12(8):653-662. doi:10.1002/pds.859
Walker AM. Confounding by indication. Epidemiology. 1996;7(4):335-336.
Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399-424. doi:10.1080/00273171.2011.568786
Glynn RJ, Schneeweiss S, Wang PS, Levin R, Avorn J. Selective prescribing led to overestimation of the benefits of lipid-lowering drugs. J Clin Epidemiol. 2006;59(8):819-828. doi:10.1016/j.jclinepi.2005.12.012
Glynn RJ, Schneeweiss S, Sturmer T. Indications for propensity scores and review of their use in pharmacoepidemiology. Basic Clin Pharmacol Toxicol. 2006;98(3):253-259. doi:10.1111/j.1742-7843.2006.pto_293.x
Austin PC, Mamdani MM. A comparison of propensity score methods: a case-study estimating the effectiveness of post-AMI statin use. Stat Med. 2006;25(12):2084-2106. doi:10.1002/sim.2328
Schneeweiss S, Rassen JA, Glynn RJ, Avorn J, Mogun H, Brookhart MA. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology. 2009;20(4):512-522. doi:10.1097/EDE.0b013e3181a663cc
Guertin JR, Rahme E, LeLorier J. Performance of the high-dimensional propensity score in adjusting for unmeasured confounders. Eur J Clin Pharmacol. 2016;72(12):1497-1505. doi:10.1007/s00228-016-2118-x
Patorno E, Glynn RJ, Hernandez-Diaz S, Liu J, Schneeweiss S. Studies with many covariates and few outcomes: selecting covariates and implementing propensity-score-based confounding adjustments. Epidemiology. 2014;25(2):268-278. doi:10.1097/EDE.0000000000000069
Rassen JA, Glynn RJ, Brookhart MA, Schneeweiss S. Covariate selection in high-dimensional propensity score analyses of treatment effects in small samples. Am J Epidemiol. 2011;173(12):1404-1413. doi:10.1093/aje/kwr001
Wyss R, Schneeweiss S, van der Laan M, Lendle SD, Ju C, Franklin JM. Using super learner prediction modeling to improve high-dimensional propensity score estimation. Epidemiology. 2018;29(1):96-106. doi:10.1097/EDE.0000000000000762
van der Laan MJ, Polley EC, Hubbard AE. Super learner. Stat Appl Genet Mol Biol. 2007;6:25. doi:10.2202/1544-6115.1309
Caron F, Leong DP, Hillis C, Fraser G, Siegal D. Current understanding of bleeding with ibrutinib use: a systematic review and meta-analysis. Blood Adv. 2017;1(12):772-778. doi:10.1182/bloodadvances.2016001883
Shatzel JJ, Olson SR, Tao DL, McCarty OJT, Danilov AV, DeLoughery TG. Ibrutinib-associated bleeding: pathogenesis, management and risk reduction strategies. J Thromb Haemost. 2017;15(5):835-847. doi:10.1111/jth.13651
Kim DH, Schneeweiss S, Glynn RJ, Lipsitz LA, Rockwood K, Avorn J. Measuring frailty in Medicare data: development and validation of a claims-based frailty index. J Gerontol A Biol Sci Med Sci. 2018;73(7):980-987. doi:10.1093/gerona/glx229
Cunningham A, Stein CM, Chung CP, Daugherty JR, Smalley WE, Ray WA. An automated database case definition for serious bleeding related to oral anticoagulant use. Pharmacoepidemiol Drug Saf. 2011;20(6):560-566. doi:10.1002/pds.2109
Dhopeshwarkar N, Yang W, Hennessy S, Rhodes JM, Cuker A, Leonard CE. Rate of major bleeding with ibrutinib versus bendamustine-rituximab in chronic lymphocytic leukemia: a population-based cohort study. Am J Hematol. 2022;97(9):E332. doi:10.1002/ajh.26632
Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Presented at: Proceedings of the 14th international joint conference on Artificial intelligence, 2, 1995, Montreal, Quebec, Canada.
Austin PC. Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research. Commun Stat Simul Comput. 2009;38(6):1228-1234. doi:10.1080/03610910902859574
Rose S. Mortality risk score prediction in an elderly population using machine learning. Am J Epidemiol. 2013;177(5):443-452. doi:10.1093/aje/kws241
Ju C, Bibaut A, van der Laan M. The relative performance of ensemble methods with deep convolutional neural networks for image classification. J Appl Stat. 2018;45(15):2800-2818. doi:10.1080/02664763.2018.1441383
Cole SR, Hernan MA. Constructing inverse probability weights for marginal structural models. Am J Epidemiol. 2008;168(6):656-664. doi:10.1093/aje/kwn164
Tian Y, Schuemie MJ, Suchard MA. Evaluating large-scale propensity score performance through real-world and synthetic data experiments. Int J Epidemiol. 2018;47(6):2005-2014. doi:10.1093/ije/dyy120
Karim ME, Pang M, Platt RW. Can we train machine learning methods to outperform the high-dimensional propensity score algorithm? Epidemiology. 2018;29(2):191-198. doi:10.1097/EDE.0000000000000787
Lee BK, Lessler J, Stuart EA. Improving propensity score weighting using machine learning. Stat Med. 2010;29(3):337-346. doi:10.1002/sim.3782
Westreich D, Lessler J, Funk MJ. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. J Clin Epidemiol. 2010;63(8):826-833. doi:10.1016/j.jclinepi.2009.11.020
Wyss R, Yanover C, El-Hay T, et al. Machine learning for improving high-dimensional proxy confounder adjustment in healthcare database studies: an overview of the current literature. Pharmacoepidemiol Drug Saf. 2022;31(9):932-943. doi:10.1002/pds.5500