Literature DB >> 16401267

The evaluation of treatment when center-specific selection criteria vary with respect to patient risk.

Elizabeth R DeLong1, Laura P Coombs, T Bruce Ferguson, Eric D Peterson.   

Abstract

Many standards of medical care are based on the demonstrated effects of various treatment strategies or processes. Unlike pharmacological treatments, these strategies or processes are not necessarily subjected to rigorous clinical trials and their benefit is frequently assessed from observational data. For evaluating the influence of such medical processes on patient outcomes, not only is risk adjustment an issue, but also the "center effect" represents an important, often overlooked consideration. Both the quality of care and the tendency to use certain treatments or processes vary from one center to another. The induced similarity in outcomes within center, as well as the potential for confounding by center, needs to be addressed within the context of risk adjustment. In addition, center-specific selection criteria for a treatment strategy can vary with respect to patient risk. Because of these considerations, it is important to adequately separate the within-center effects of the treatment or strategy from the across-center effects, which relate more to center performance. The primary objective of this article is to explore and extend current methods of dealing with center confounding for dichotomous outcomes, primarily for the situation where selection on the basis of patient risk can vary from center to center. A simulation study compares results from several different analytic methods and provides evidence for the importance of considering confounding due to both risk and center when evaluating the effectiveness of a process. An example that examines the effect of early extubation after bypass surgery is also presented.

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Year:  2005        PMID: 16401267     DOI: 10.1111/j.1541-0420.2005.00358.x

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  3 in total

1.  Comments on "Intermediate and advanced topics in multilevel logistic regression analysis".

Authors:  Lei Li; Matthew A Rysavy; Abhik Das
Journal:  Stat Med       Date:  2018-07-16       Impact factor: 2.373

2.  Design effect in multicenter studies: gain or loss of power?

Authors:  Emilie Vierron; Bruno Giraudeau
Journal:  BMC Med Res Methodol       Date:  2009-06-18       Impact factor: 4.615

Review 3.  Centre selection for clinical trials and the generalisability of results: a mixed methods study.

Authors:  Adrian Gheorghe; Tracy E Roberts; Jonathan C Ives; Benjamin R Fletcher; Melanie Calvert
Journal:  PLoS One       Date:  2013-02-22       Impact factor: 3.240

  3 in total

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