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Results: 1 to 20 of 25

Related Articles by Review for PubMed (Select 15347546)

1.

Advanced statistics: the propensity score--a method for estimating treatment effect in observational research.

Newgard CD, Hedges JR, Arthur M, Mullins RJ.

Acad Emerg Med. 2004 Sep;11(9):953-61.

PMID:
15347546
2.

Estimating effects of nursing intervention via propensity score analysis.

Qin R, Titler MG, Shever LL, Kim T.

Nurs Res. 2008 Nov-Dec;57(6):444-52. doi: 10.1097/NNR.0b013e31818c66f6. Review.

3.

Methodological aspects in the assessment of treatment effects in observational health outcomes studies.

Haro JM, Kontodimas S, Negrin MA, Ratcliffe M, Suarez D, Windmeijer F.

Appl Health Econ Health Policy. 2006;5(1):11-25. Review.

PMID:
16774289
4.

An overview of the objectives of and the approaches to propensity score analyses.

Heinze G, J√ľni P.

Eur Heart J. 2011 Jul;32(14):1704-8. doi: 10.1093/eurheartj/ehr031. Epub 2011 Feb 28. Review.

5.

Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review.

Shah BR, Laupacis A, Hux JE, Austin PC.

J Clin Epidemiol. 2005 Jun;58(6):550-9. Epub 2005 Apr 19. Review.

PMID:
15878468
6.
7.

Methods to assess intended effects of drug treatment in observational studies are reviewed.

Klungel OH, Martens EP, Psaty BM, Grobbee DE, Sullivan SD, Stricker BH, Leufkens HG, de Boer A.

J Clin Epidemiol. 2004 Dec;57(12):1223-31. Review.

PMID:
15617947
8.

[Propensity score methods for creating covariate balance in observational studies].

Pattanayak CW, Rubin DB, Zell ER.

Rev Esp Cardiol. 2011 Oct;64(10):897-903. doi: 10.1016/j.recesp.2011.06.008. Epub 2011 Aug 27. Review. Spanish.

9.

Review of guidelines for good practice in decision-analytic modelling in health technology assessment.

Philips Z, Ginnelly L, Sculpher M, Claxton K, Golder S, Riemsma R, Woolacoot N, Glanville J.

Health Technol Assess. 2004 Sep;8(36):iii-iv, ix-xi, 1-158. Review.

10.

Generalisability in economic evaluation studies in healthcare: a review and case studies.

Sculpher MJ, Pang FS, Manca A, Drummond MF, Golder S, Urdahl H, Davies LM, Eastwood A.

Health Technol Assess. 2004 Dec;8(49):iii-iv, 1-192. Review.

11.

Principles for modeling propensity scores in medical research: a systematic literature review.

Weitzen S, Lapane KL, Toledano AY, Hume AL, Mor V.

Pharmacoepidemiol Drug Saf. 2004 Dec;13(12):841-53. Review.

PMID:
15386709
12.

Cost-effectiveness of using prognostic information to select women with breast cancer for adjuvant systemic therapy.

Williams C, Brunskill S, Altman D, Briggs A, Campbell H, Clarke M, Glanville J, Gray A, Harris A, Johnston K, Lodge M.

Health Technol Assess. 2006 Sep;10(34):iii-iv, ix-xi, 1-204. Review.

14.

Invited commentary: propensity scores.

Joffe MM, Rosenbaum PR.

Am J Epidemiol. 1999 Aug 15;150(4):327-33. Review.

15.

[Bias in observational research: 'confounding'].

Groenwold RH.

Ned Tijdschr Geneeskd. 2012;156(13):A4221. Review. Dutch.

PMID:
22456291
16.

Estimating causal effects from large data sets using propensity scores.

Rubin DB.

Ann Intern Med. 1997 Oct 15;127(8 Pt 2):757-63. Review.

PMID:
9382394
17.
18.

Are propensity scores really superior to standard multivariable analysis?

Biondi-Zoccai G, Romagnoli E, Agostoni P, Capodanno D, Castagno D, D'Ascenzo F, Sangiorgi G, Modena MG.

Contemp Clin Trials. 2011 Sep;32(5):731-40. doi: 10.1016/j.cct.2011.05.006. Epub 2011 May 16. Review.

PMID:
21616172
19.

[Propensity scores in observational research].

Groenwold RH.

Ned Tijdschr Geneeskd. 2013;157(29):A6179. Review. Dutch.

PMID:
23859107
20.

Evaluating the evidence about therapies. What the clinician needs to know about statistics.

Kennedy KA, Frankowski RF.

Clin Perinatol. 2003 Jun;30(2):205-15. Review.

PMID:
12875351
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