Impact of sex in stroke thrombolysis: A coarsened exact matching study

BMC Neurology

Volumn 15, Issue 1, 2015, Pages

  Hametner, Christian ^a   Kellert, Lars ^a,b   Ringleb, Arthur ^a  

^a UNIVERSITY OF HEIDELBERG   (Germany)

^b UNIVERSITY OF MUNICH   (Germany)

Author keywords

Acute ischemic stroke; Coarsened exact matching; Intracerebral hemorrhage; Intravenous thrombolysis; Sex

Indexed keywords

ANTICOAGULANT AGENT; STATIN (PROTEIN); FIBRINOLYTIC AGENT;

AGED; ANTICOAGULANT THERAPY; ARTICLE; BRAIN HEMORRHAGE; CEREBROVASCULAR ACCIDENT; CONTROLLED STUDY; DIABETES MELLITUS; DIASTOLIC BLOOD PRESSURE; FEMALE; FIBRINOLYTIC THERAPY; HEART ATRIUM FIBRILLATION; HUMAN; HYPERTENSION; ISCHEMIC HEART DISEASE; MAJOR CLINICAL STUDY; MALE; MULTIVARIATE ANALYSIS; OUTCOME ASSESSMENT; SEX DIFFERENCE; SMOKING; TIME TO TREATMENT; CHEMICALLY INDUCED; MORTALITY; PROSPECTIVE STUDY; STATISTICAL ANALYSIS; STROKE;

AGED; CEREBRAL HEMORRHAGE; FEMALE; FIBRINOLYTIC AGENTS; HUMANS; MALE; MATCHED-PAIR ANALYSIS; MULTIVARIATE ANALYSIS; PROSPECTIVE STUDIES; SEX FACTORS; STROKE; THROMBOLYTIC THERAPY;

EID: 84923866871     PISSN: None     EISSN: 14712377     Source Type: Journal    
DOI: 10.1186/s12883-015-0262-z     Document Type: Article

References (24)

1. Kent DM, Buchan AM, Hill MD. The gender effect in stroke thrombolysis: of CASES, controls, and treatment-effect modification. Neurology. 2008;71:1080-3.
2. Lorenzano S, Ahmed N, Falcou A, Mikulik R, Tatlisumak T, Roffe C, et al. Does sex influence the response to intravenous thrombolysis in ischemic stroke?: answers from safe implementation of treatments in stroke-international stroke thrombolysis register. Stroke. 2013;44:3401-6.
3. Kent DM, Price LL, Ringleb P, Hill MD, Selker HP. Sex-based differences in response to recombinant tissue plasminogen activator in acute ischemic stroke: a pooled analysis of randomized clinical trials. Stroke. 2005;36:62-5.
4. Elkind MS, Prabhakaran S, Pittman J, Koroshetz W, Jacoby M, Johnston KC, et al. Sex as a predictor of outcomes in patients treated with thrombolysis for acute stroke. Neurology. 2007;68:842-8.
5. Cochran WG. The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics. 1968;24:295-313.
6. Cochran WG. Analysis of covariance: its nature and uses. Biometrics. 1957;13:261-81.
7. Rubin DB. The use of matched sampling and regression adjustment to remove bias in observational studies. Biometrics. 1973;29:185-203.
8. Iacus SM, King G, Porro G. Causal Inference Without Balance Checking: Coarsened Exact Matching. Political Analysis first published online August 23, 2011 doi:10.1093/pan/mpr013
9. Rubin DB, Thomas N. Combining propensity score matching with additional adjustments for prognostic covariates. J Am Stat Assoc. 2000;95:573-85.
10. Ho D, Imai K, King G, Stuart E. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit Anal. 2007;15:199-236.
11. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61:344-9.
12. Saver JL, Yafeh B. Confirmation of tPA treatment effect by baseline severity-adjusted end point reanalysis of the NINDS-tPA stroke trials. Stroke. 2007;38:414-6.
13. Hacke W, Kaste M, Fieschi C, von Kummer R, Davalos A, Meier D, et al. Randomised double-blind placebo-controlled trial of thrombolytic therapy with intravenous alteplase in acute ischaemic stroke (ECASS II). Second European-Australasian acute stroke study investigators. Lancet. 1998;352:1245-51.
14. Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York: J. Wiley & Sons; 1987.
15. King G, Honaker J, Joseph A, Scheve K. Analyzing incomplete political science data: an alternative algorithm for multiple imputation. Am Polit Sci Rev. 2001;95:49-69.
16. Honaker J, King G, Blackwell M. Amelia II: a program for missing data. J Stat Software. 2011;45:1-47.
17. King G, Nielsen R, Coberley C, Pope J, and Wells A. Comparative Effectiveness of Matching Methods for Causal Inference. 2011. Copy at http://j.mp/jCpWmk
18. R Development Core Team. R: A language and environment for statistical computing. In: Book R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2013.
19. Imai K, King G, Lau O. "logit: Logistic Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau in "Zelig: Everyone's Statistical Software,". 2013. http://gking.harvard.edu/zelig
20. Ho D, Imai K, King G, Stuart EA. MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. J Stat Software. 2011;42:1-28.
21. Ernon L, Schrooten M, Thijs V. Body temperature and outcome after stroke thrombolysis. Acta Neurol Scand. 2006;114:23-8.
22. Fiorelli M, Bastianello S, von Kummer R, del Zoppo GJ, Larrue V, Lesaffre E, et al. Hemorrhagic transformation within 36 hours of a cerebral infarct: relationships with early clinical deterioration and 3-month outcome in the European Cooperative Acute Stroke Study I (ECASS I) cohort. Stroke. 1999;30:2280-4.
23. Larrue V, von Kummer RR, Muller A, Bluhmki E. Risk factors for severe hemorrhagic transformation in ischemic stroke patients treated with recombinant tissue plasminogen activator: a secondary analysis of the European-Australasian Acute Stroke Study (ECASS II). Stroke. 2001;32:438-41.
24. Di Legge S, Saposnik G, Nilanont Y, Hachinski V. Neglecting the difference: does right or left matter in stroke outcome after thrombolysis? Stroke. 2006;37:2066-9.