Literature DB >> 23251125

Analysis of nonintervention studies: technical supplement.

Mikel Aickin1.   

Abstract

Methods for analyzing data in nonintervention clinical studies are substantially different from those that are appropriate for randomized clinical trials. Although the latter methods are well known, the former are not. A systematic approach for dealing with statistical confounding in nonintervention research has been developed over the past 30 to 40 years, and the essence of this theory constitutes the contents of this article. An accompanying, less technical article explains the implications of these results for clinical research.

Mesh:

Year:  2012        PMID: 23251125      PMCID: PMC3523943          DOI: 10.7812/tpp/12-046

Source DB:  PubMed          Journal:  Perm J        ISSN: 1552-5767


  12 in total

1.  Beyond randomization.

Authors:  Mikel Aickin
Journal:  J Altern Complement Med       Date:  2002-12       Impact factor: 2.579

2.  The intensity-score approach to adjusting for confounding.

Authors:  Babette Brumback; Sander Greenland; Mary Redman; Nancy Kiviat; Paula Diehr
Journal:  Biometrics       Date:  2003-06       Impact factor: 2.571

Review 3.  A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003.

Authors:  Peter C Austin
Journal:  Stat Med       Date:  2008-05-30       Impact factor: 2.373

4.  Developing practical recommendations for the use of propensity scores: discussion of 'A critical appraisal of propensity score matching in the medical literature between 1996 and 2003' by Peter Austin, Statistics in Medicine.

Authors:  Elizabeth A Stuart
Journal:  Stat Med       Date:  2008-05-30       Impact factor: 2.373

5.  The essential role of balance tests in propensity-matched observational studies: comments on 'A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003' by Peter Austin, Statistics in Medicine.

Authors:  B B Hansen
Journal:  Stat Med       Date:  2008-05-30       Impact factor: 2.373

6.  Discussion of research using propensity-score matching: comments on 'A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003' by Peter Austin, Statistics in Medicine.

Authors:  Jennifer Hill
Journal:  Stat Med       Date:  2008-05-30       Impact factor: 2.373

7.  Why we need observational studies to evaluate the effectiveness of health care.

Authors:  N Black
Journal:  BMJ       Date:  1996-05-11

Review 8.  Estimating causal effects from large data sets using propensity scores.

Authors:  D B Rubin
Journal:  Ann Intern Med       Date:  1997-10-15       Impact factor: 25.391

9.  The effectiveness of adjustment by subclassification in removing bias in observational studies.

Authors:  W G Cochran
Journal:  Biometrics       Date:  1968-06       Impact factor: 2.571

10.  From medical records to clinical science.

Authors:  Mikel Aickin; Charles Elder
Journal:  Perm J       Date:  2012
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  1 in total

1.  Revisiting Triple Antibiotic Irrigation of Breast Implant Pockets: A Placebo-controlled Single Practice Cohort Study.

Authors:  James J Drinane; Ronald S Bergman; Bryan L Folkers; Matthew J Kortes
Journal:  Plast Reconstr Surg Glob Open       Date:  2013-11-07
  1 in total

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