Literature DB >> 16579822

Strengthening the case for disease management effectiveness: un-hiding the hidden bias.

Ariel Linden1, John L Adams, Nancy Roberts.   

Abstract

As is the case with most health care program evaluations, disease management (DM) programs typically follow an observational study design, indicating that randomization to treatment or control was not performed. The foremost limitation of observational studies, compared to randomized studies, is that the only biases that can be controlled for are those associated with observed variables. Hidden bias refers to all those unobserved covariates that may distort the conclusions of the study. This paper introduces a sensitivity analysis that is used to determine the magnitude of hidden bias necessary to alter the conclusion that a DM program intervention was indeed effective.

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Year:  2006        PMID: 16579822     DOI: 10.1111/j.1365-2753.2005.00612.x

Source DB:  PubMed          Journal:  J Eval Clin Pract        ISSN: 1356-1294            Impact factor:   2.431


  3 in total

1.  Who gets disease management?

Authors:  Melinda Beeuwkes Buntin; Arvind K Jain; Soeren Mattke; Nicole Lurie
Journal:  J Gen Intern Med       Date:  2009-03-24       Impact factor: 5.128

2.  Comparing the Health of Populations: Methods to Evaluate and Tailor Population Management Initiatives in the Netherlands.

Authors:  Roy J P Hendrikx; Hanneke W Drewes; Marieke Spreeuwenberg; Dirk Ruwaard; Caroline A Baan
Journal:  Popul Health Manag       Date:  2017-11-01       Impact factor: 2.459

3.  Estimating Measurement Error of the Patient Activation Measure for Respondents with Partially Missing Data.

Authors:  Ariel Linden
Journal:  Biomed Res Int       Date:  2015-11-17       Impact factor: 3.411

  3 in total

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