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Items: 1 to 20 of 42

1.

Systematic differences in treatment effect estimates between propensity score methods and logistic regression.

Martens EP, Pestman WR, de Boer A, Belitser SV, Klungel OH.

Int J Epidemiol. 2008 Oct;37(5):1142-7. doi: 10.1093/ije/dyn079.

PMID:
18453634
2.

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. Review.

PMID:
15878468
3.

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
4.
5.

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
6.

Improving participant selection in disease management programmes: insights gained from propensity score stratification.

Linden A, Adams JL.

J Eval Clin Pract. 2008 Oct;14(5):914-8. doi: 10.1111/j.1365-2753.2008.01091.x. Review.

PMID:
19018926
7.
9.

Correction of confounding bias in non-randomized studies by appropriate weighting.

Schmoor C, Gall C, Stampf S, Graf E.

Biom J. 2011 Mar;53(2):369-87. doi: 10.1002/bimj.201000154. Review.

PMID:
21308726
10.

[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. Review. Spanish.

11.

A comparison between traditional methods and multilevel regression for the analysis of multicenter intervention studies.

Moerbeek M, van Breukelen GJ, Berger MP.

J Clin Epidemiol. 2003 Apr;56(4):341-50. Review.

PMID:
12767411
12.

Overwhelming heterogeneity in systematic reviews of observational anti-epileptic studies.

Maguire MJ, Hemming K, Hutton JL, Marson AG.

Epilepsy Res. 2008 Aug;80(2-3):201-12. doi: 10.1016/j.eplepsyres.2008.03.024. Review.

PMID:
18515046
13.

Differences between marginal structural models and conventional models in their exposure effect estimates: a systematic review.

Suarez D, Borràs R, Basagaña X.

Epidemiology. 2011 Jul;22(4):586-8. doi: 10.1097/EDE.0b013e31821d0507. Review.

PMID:
21540744
14.

Quantitative assessment of unobserved confounding is mandatory in nonrandomized intervention studies.

Groenwold RH, Hak E, Hoes AW.

J Clin Epidemiol. 2009 Jan;62(1):22-8. doi: 10.1016/j.jclinepi.2008.02.011. Review.

PMID:
18619797
15.

Marginal structural models might overcome confounding when analyzing multiple treatment effects in observational studies.

Suarez D, Haro JM, Novick D, Ochoa S.

J Clin Epidemiol. 2008 Jun;61(6):525-30. doi: 10.1016/j.jclinepi.2007.11.007. Review.

PMID:
18471655
16.

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. Review.

PMID:
21616172
17.

An introduction to statistical methods used in binary outcome modeling.

Nathanson BH, Higgins TL.

Semin Cardiothorac Vasc Anesth. 2008 Sep;12(3):153-66. doi: 10.1177/1089253208323415. Review.

PMID:
18805850
18.

Should we adjust for covariates in nonlinear regression analyses of randomized trials?

Hauck WW, Anderson S, Marcus SM.

Control Clin Trials. 1998 Jun;19(3):249-56. Review.

PMID:
9620808
19.

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

Heinze G, Jüni P.

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

PMID:
21362706
20.

Clinical trials: how to assess confounding and why so.

Cleophas TJ, Zwinderman AH.

Curr Clin Pharmacol. 2007 May;2(2):129-33. Review.

PMID:
18690860
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