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15 in total

1. Minimizing bias due to confounding by indication in comparative effectiveness research: the importance of restriction.

Authors: Bruce M Psaty; David S Siscovick
Journal: JAMA Date: 2010-08-25 Impact factor: 56.272

2. Variable selection for propensity score models.

Authors: M Alan Brookhart; Sebastian Schneeweiss; Kenneth J Rothman; Robert J Glynn; Jerry Avorn; Til Stürmer
Journal: Am J Epidemiol Date: 2006-04-19 Impact factor: 4.897

Review 3. A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods.

Authors: Til Stürmer; Manisha Joshi; Robert J Glynn; Jerry Avorn; Kenneth J Rothman; Sebastian Schneeweiss
Journal: J Clin Epidemiol Date: 2005-10-13 Impact factor: 6.437

4. Impact of mis-specification of the treatment model on estimates from a marginal structural model.

Authors: Geneviève Lefebvre; Joseph A C Delaney; Robert W Platt
Journal: Stat Med Date: 2008-08-15 Impact factor: 2.373

5. The implications of propensity score variable selection strategies in pharmacoepidemiology: an empirical illustration.

Authors: Amanda R Patrick; Sebastian Schneeweiss; M Alan Brookhart; Robert J Glynn; Kenneth J Rothman; Jerry Avorn; Til Stürmer
Journal: Pharmacoepidemiol Drug Saf Date: 2011-03-10 Impact factor: 2.890

6. Treatment effects in the presence of unmeasured confounding: dealing with observations in the tails of the propensity score distribution--a simulation study.

Authors: Til Stürmer; Kenneth J Rothman; Jerry Avorn; Robert J Glynn
Journal: Am J Epidemiol Date: 2010-08-17 Impact factor: 4.897

7. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.

Authors: Sebastian Schneeweiss; Jeremy A Rassen; Robert J Glynn; Jerry Avorn; Helen Mogun; M Alan Brookhart
Journal: Epidemiology Date: 2009-07 Impact factor: 4.822

8. Assessing the impact of propensity score estimation and implementation on covariate balance and confounding control within and across important subgroups in comparative effectiveness research.

Authors: Cynthia J Girman; Mugdha Gokhale; Tzuyung Doug Kou; Kimberly G Brodovicz; Richard Wyss; Til Stürmer
Journal: Med Care Date: 2014-03 Impact factor: 2.983

9. Cardiovascular and mortality risk in elderly Medicare beneficiaries treated with olmesartan versus other angiotensin receptor blockers.

Authors: David J Graham; Esther H Zhou; Stephen McKean; Mark Levenson; Katlyn Calia; Kate Gelperin; Xiao Ding; Thomas E MaCurdy; Chris Worrall; Jeffrey A Kelman
Journal: Pharmacoepidemiol Drug Saf Date: 2013-11-26 Impact factor: 2.890

Review 10. Angiotensin-converting enzyme inhibitors reduce mortality in hypertension: a meta-analysis of randomized clinical trials of renin-angiotensin-aldosterone system inhibitors involving 158,998 patients.

Authors: Laura C van Vark; Michel Bertrand; K Martijn Akkerhuis; Jasper J Brugts; Kim Fox; Jean-Jacques Mourad; Eric Boersma
Journal: Eur Heart J Date: 2012-04-17 Impact factor: 29.983

1 in total

Review 1. Active-comparator design and new-user design in observational studies.

Authors: Kazuki Yoshida; Daniel H Solomon; Seoyoung C Kim
Journal: Nat Rev Rheumatol Date: 2015-03-24 Impact factor: 20.543

1 in total