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Epidemiol Infect. 2018 Sep;146(12):1556-1564. doi: 10.1017/S0950268818001723. Epub 2018 Jun 27.

Different latent class models were used and evaluated for assessing the accuracy of campylobacter diagnostic tests: overcoming imperfect reference standards?

Author information

1
Bordeaux University Hospital,Public Health Department, Clinical Epidemiology Unit,F-33076 Bordeaux,France.
2
French National Reference Center for Campylobacter and Helicobacter,F-33076 Bordeaux,France.

Abstract

In the absence of perfect reference standard, classical techniques result in biased diagnostic accuracy and prevalence estimates. By statistically defining the true disease status, latent class models (LCM) constitute a promising alternative. However, LCM is a complex method which relies on parametric assumptions, including usually a conditional independence between tests and might suffer from data sparseness. We carefully applied LCMs to assess new campylobacter infection detection tests for which bacteriological culture is an imperfect reference standard. Five diagnostic tests (culture, polymerase chain reaction and three immunoenzymatic tests) of campylobacter infection were collected in 623 patients from Bordeaux and Lyon Hospitals, France. Their diagnostic accuracy were estimated with standard and extended LCMs with a thorough examination of models goodness-of-fit. The model including a residual dependence specific to the immunoenzymatic tests best complied with LCM assumptions. Asymptotic results of goodness-of-fit statistics were substantially impaired by data sparseness and empirical distributions were preferred. Results confirmed moderate sensitivity of the culture and high performances of immunoenzymatic tests. LCMs can be used to estimate diagnostic tests accuracy in the absence of perfect reference standard. However, their implementation and assessment require specific attention due to data sparseness and limitations of existing software.

KEYWORDS:

Campylobacter; diagnostic accuracy; imperfect gold standard; latent class model; sparseness

PMID:
29945689
PMCID:
PMC6090718
DOI:
10.1017/S0950268818001723
[Indexed for MEDLINE]
Free PMC Article

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