Literature DB >> 7326940

On the detection of outlier clinics in medical and surgical trials: I. Practical considerations.

P L Canner, Y B Huang, C L Meinert.   

Abstract

In the conduct of multicenter clinical trials, comparisons among the clinical centers are often made with respect to such measures as incidence of diagnosis of various events, operative mortality (in the case of a surgical trial), and patient adherence of the treatment regimen. If one or two clinics seem to stand out from all the other clinics with respect to any such measures, this may indicate - among other things - differences in skill levels or possible deficiencies in understanding and following the protocol among these clinics. On the other hand, when clinics are ranked on any measure, one clinic must of necessity be first and one last; thus, it is necessary to determine whether the extreme values represent true differences from the values of the other clinics or whether they can be attributed to random variation alone. A statistical test is described for detecting clinics that are outliers - i.e., truly different from all the rest of the clinics in the trial. Critical values for this test are given for various numbers of clinics, numbers of patients enrolled per clinic, and probabilities of a patient incurring a given event under the null hypothesis of no differences among the clinics. Use of this test in detecting outlier clinics with respect to incidence of various endpoints is illustrated for the case of a large, multicenter drug trial.

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Year:  1981        PMID: 7326940     DOI: 10.1016/0197-2456(81)90013-1

Source DB:  PubMed          Journal:  Control Clin Trials        ISSN: 0197-2456


  3 in total

1.  Clinical trial performance of community- vs university-based practices in the submacular surgery trials (SST): SST report no. 2.

Authors:  Neil M Bressler; Barbara S Hawkins; Susan B Bressler; Päivi H Miskala; Marta J Marsh
Journal:  Arch Ophthalmol       Date:  2004-06

2.  Bayesian methods to determine performance differences and to quantify variability among centers in multi-center trials: the IHAST trial.

Authors:  Emine O Bayman; Kathryn M Chaloner; Bradley J Hindman; Michael M Todd
Journal:  BMC Med Res Methodol       Date:  2013-01-16       Impact factor: 4.615

3.  Addressing the challenge of assessing physician-level screening performance: mammography as an example.

Authors:  Elizabeth S Burnside; Yunzhi Lin; Alejandro Munoz del Rio; Perry J Pickhardt; Yirong Wu; Roberta M Strigel; Mai A Elezaby; Eve A Kerr; Diana L Miglioretti
Journal:  PLoS One       Date:  2014-02-21       Impact factor: 3.240

  3 in total

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