Literature DB >> 31444309

Toward a Framework for Outcome-Based Analytical Performance Specifications: A Methodology Review of Indirect Methods for Evaluating the Impact of Measurement Uncertainty on Clinical Outcomes.

Alison F Smith1,2, Bethany Shinkins3,2,4, Peter S Hall5, Claire T Hulme3,6, Mike P Messenger2,4,7.   

Abstract

BACKGROUND: For medical tests that have a central role in clinical decision-making, current guidelines advocate outcome-based analytical performance specifications. Given that empirical (clinical trial-style) analyses are often impractical or unfeasible in this context, the ability to set such specifications is expected to rely on indirect studies to calculate the impact of test measurement uncertainty on downstream clinical, operational, and economic outcomes. Currently, however, a lack of awareness and guidance concerning available alternative indirect methods is limiting the production of outcome-based specifications. Therefore, our aim was to review available indirect methods and present an analytical framework to inform future outcome-based performance goals. CONTENT: A methodology review consisting of database searches and extensive citation tracking was conducted to identify studies using indirect methods to incorporate or evaluate the impact of test measurement uncertainty on downstream outcomes (including clinical accuracy, clinical utility, and/or costs). Eighty-two studies were identified, most of which evaluated the impact of imprecision and/or bias on clinical accuracy. A common analytical framework underpinning the various methods was identified, consisting of 3 key steps: (a) calculation of "true" test values; (b) calculation of measured test values (incorporating uncertainty); and (c) calculation of the impact of discrepancies between (a) and (b) on specified outcomes. A summary of the methods adopted is provided, and key considerations are discussed.
CONCLUSIONS: Various approaches are available for conducting indirect assessments to inform outcome-based performance specifications. This study provides an overview of methods and key considerations to inform future studies and research in this area.
© 2019 American Association for Clinical Chemistry.

Mesh:

Year:  2019        PMID: 31444309      PMCID: PMC7055686          DOI: 10.1373/clinchem.2018.300954

Source DB:  PubMed          Journal:  Clin Chem        ISSN: 0009-9147            Impact factor:   8.327


  88 in total

1.  The impact of bias in MoM values on patient risk and screening performance for Down syndrome.

Authors:  Barry Nix; Dave Wright; Amy Baker
Journal:  Prenat Diagn       Date:  2007-09       Impact factor: 3.050

2.  Decision-analytic modeling to evaluate benefits and harms of medical tests: uses and limitations.

Authors:  Thomas A Trikalinos; Uwe Siebert; Joseph Lau
Journal:  Med Decis Making       Date:  2009-09-04       Impact factor: 2.583

Review 3.  The influence of analytical bias on diagnostic misclassifications.

Authors:  P H Petersen; C H de Verdier; T Groth; C G Fraser; O Blaabjerg; M Hørder
Journal:  Clin Chim Acta       Date:  1997-04-25       Impact factor: 3.786

4.  The impact of measurement frequency on the domains of glycemic control in the critically ill--a Monte Carlo simulation.

Authors:  James S Krinsley; David E Bruns; James C Boyd
Journal:  J Diabetes Sci Technol       Date:  2015-01-06

5.  Studies on the required analytical quality of TSH measurements in screening for congenital hypothyroidism.

Authors:  P H Petersen; F Rosleff; J Rasmussen; N Hobolth
Journal:  Scand J Clin Lab Invest Suppl       Date:  1980

6.  Economic Value of Improved Accuracy for Self-Monitoring of Blood Glucose Devices for Type 1 and Type 2 Diabetes in England.

Authors:  Robert Brett McQueen; Marc D Breton; Joyce Craig; Hayden Holmes; Melanie D Whittington; Markus A Ott; Jonathan D Campbell
Journal:  J Diabetes Sci Technol       Date:  2018-04-21

7.  Impact of a reduced error range of SMBG in insulin-treated patients in Germany.

Authors:  Oliver Schnell; Michael Erbach
Journal:  J Diabetes Sci Technol       Date:  2014-02-05

8.  Higher accuracy of self-monitoring of blood glucose in insulin-treated patients in Germany: clinical and economical aspects.

Authors:  Oliver Schnell; Michael Erbach; Eva Wintergerst
Journal:  J Diabetes Sci Technol       Date:  2013-07-01

9.  Modeling of effect of glucose sensor errors on insulin dosage and glucose bolus computed by LOGIC-Insulin.

Authors:  Tom Van Herpe; Bart De Moor; Greet Van den Berghe; Dieter Mesotten
Journal:  Clin Chem       Date:  2014-08-26       Impact factor: 8.327

10.  Misclassification and discordance of measured blood pressure from patient's true blood pressure in current clinical practice: a clinical trial simulation case study.

Authors:  Yuyan Jin; Robert Bies; Marc R Gastonguay; Norman Stockbridge; Jogarao Gobburu; Rajanikanth Madabushi
Journal:  J Pharmacokinet Pharmacodyn       Date:  2012-05-09       Impact factor: 2.745

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  1 in total

1.  A Software Tool for Calculating the Uncertainty of Diagnostic Accuracy Measures.

Authors:  Theodora Chatzimichail; Aristides T Hatjimihail
Journal:  Diagnostics (Basel)       Date:  2021-02-27
  1 in total

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