Literature DB >> 24969153

Pre-study feasibility and identifying sensitivity analyses for protocol pre-specification in comparative effectiveness research.

Cynthia J Girman1, Douglas Faries, Patrick Ryan, Matt Rotelli, Mark Belger, Bruce Binkowitz, Robert O'Neill.   

Abstract

The use of healthcare databases for comparative effectiveness research (CER) is increasing exponentially despite its challenges. Researchers must understand their data source and whether outcomes, exposures and confounding factors are captured sufficiently to address the research question. They must also assess whether bias and confounding can be adequately minimized. Many study design characteristics may impact on the results; however, minimal if any sensitivity analyses are typically conducted, and those performed are post hoc. We propose pre-study steps for CER feasibility assessment and to identify sensitivity analyses that might be most important to pre-specify to help ensure that CER produces valid interpretable results.

Keywords:  comparative effectiveness; empirical equipoise; feasibility; observational study; pharmacoepidemiology; sensitivity analysis

Mesh:

Year:  2014        PMID: 24969153     DOI: 10.2217/cer.14.16

Source DB:  PubMed          Journal:  J Comp Eff Res        ISSN: 2042-6305            Impact factor:   1.744


  5 in total

1.  A tool for empirical equipoise assessment in multigroup comparative effectiveness research.

Authors:  Kazuki Yoshida; Daniel H Solomon; Sebastien Haneuse; Seoyoung C Kim; Elisabetta Patorno; Sara K Tedeschi; Houchen Lyu; Sonia Hernández-Díaz; Robert J Glynn
Journal:  Pharmacoepidemiol Drug Saf       Date:  2019-05-27       Impact factor: 2.890

2.  Multinomial Extension of Propensity Score Trimming Methods: A Simulation Study.

Authors:  Kazuki Yoshida; Daniel H Solomon; Sebastien Haneuse; Seoyoung C Kim; Elisabetta Patorno; Sara K Tedeschi; Houchen Lyu; Jessica M Franklin; Til Stürmer; Sonia Hernández-Díaz; Robert J Glynn
Journal:  Am J Epidemiol       Date:  2019-03-01       Impact factor: 4.897

3.  Persistence of oral antidiabetic treatment for type 2 diabetes characterized by drug class, patient characteristics and severity of renal impairment: A Japanese database analysis.

Authors:  Takashi Kadowaki; Nobuaki Sarai; Takeshi Hirakawa; Kentaro Taki; Kosuke Iwasaki; Hisashi Urushihara
Journal:  Diabetes Obes Metab       Date:  2018-08-02       Impact factor: 6.577

4.  Considerations in characterizing real-world data relevance and quality for regulatory purposes: A commentary.

Authors:  Cynthia J Girman; Mary E Ritchey; Wei Zhou; Nancy A Dreyer
Journal:  Pharmacoepidemiol Drug Saf       Date:  2018-12-05       Impact factor: 2.890

5.  Real-world data: Assessing electronic health records and medical claims data to support regulatory decision-making for drug and biological products.

Authors:  Cynthia J Girman; Mary E Ritchey; Vincent Lo Re
Journal:  Pharmacoepidemiol Drug Saf       Date:  2022-05-03       Impact factor: 2.732

  5 in total

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