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In this paper, we discuss the Bile Acid Comparison and Harmonisation project, a sub-study of the Trial of URsodeoxycholic acid vs RIFampicin in early-onset severe Intrahepatic Cholestasis of pregnancy, giving an overview of the current state of affairs for total bile acid measurements. [ABSTRACT FROM AUTHOR]

In this computer simulation study, we examine four different statistical approaches of linearity assessment, including two variants of deviation from linearity (individual (IDL) and averaged (AD)), along with detection capabilities of residuals of linear regression (individual and averaged). From the results of the simulation, the following broad suggestions are provided to laboratory practitioners when performing linearity assessment. A high imprecision can challenge linearity investigations by producing a high false positive rate or low power of detection. Therefore, the imprecision of the measurement procedure should be considered when interpreting linearity assessment results. In the presence of high imprecision, the results of linearity assessment should be interpreted with caution. Different linearity assessment approaches examined in this study performed well under different analytical scenarios. For optimal outcomes, a considered and tailored study design should be implemented. With the exception of specific scenarios, both ADL and IDL methods were suboptimal for the assessment of linearity compared. When imprecision is low (3 %), averaged residual of linear regression with triplicate measurements and a non-linearity acceptance limit of 5 % produces <5 % false positive rates and a high power for detection of non-linearity of >70 % across different types and degrees of non-linearity. Detection of departures from linearity are difficult to identify in practice and enhanced methods of detection need development. [ABSTRACT FROM AUTHOR]

Reporting a measurement procedure and its analytical performance following method evaluation in a peer-reviewed journal is an important means for clinical laboratory practitioners to share their findings. It also represents an important source of evidence base to help others make informed decisions about their practice. At present, there are significant variations in the information reported in laboratory medicine journal publications describing the analytical performance of measurement procedures. These variations also challenge authors, readers, reviewers, and editors in deciding the quality of a submitted manuscript. The International Federation of Clinical Chemistry and Laboratory Medicine Working Group on Method Evaluation Protocols (IFCC WG-MEP) developed a checklist and recommends its adoption to enable a consistent approach to reporting method evaluation and analytical performance characteristics of measurement procedures in laboratory medicine journals. It is envisioned that the Laboratory Evaluation and Analytical Performance Characteristics (LEAP) checklist will improve the standardisation of journal publications describing method evaluation and analytical performance characteristics, improving the quality of the evidence base that is relied upon by practitioners. [ABSTRACT FROM AUTHOR]

Objectives An increase in analytical imprecision and/or the introduction of bias can affect the interpretation of quantitative laboratory results. In this study, we explore the impact of varying assay imprecision and bias introduction on the classification of patients based on fixed thresholds. Methods Simple spreadsheets (Microsoft Excel) were constructed to simulate conditions of assay deterioration, expressed as coefficient of variation and bias (in percentages). The impact on patient classification was explored based on fixed interpretative limits. A combined matrix of imprecision and bias of 0%, 2%, 4%, 6%, 8%, and 10% (tool 1) as well as 0%, 2%, 5%, 10%, 15%, and 20% (tool 2) was simulated, respectively. The percentage of patients who were reclassified following the addition of simulated imprecision and bias was summarized and presented in tables and graphs. Results The percentage of patients who were reclassified increased with increasing/decreasing magnitude of imprecision and bias. The impact of imprecision lessens with increasing bias such that at high biases, the bias becomes the dominant cause for reclassification. Conclusions The spreadsheet tools, available as Supplemental Material , allow laboratories to visualize the impact of additional analytical imprecision and bias on the classification of their patients when applied to locally extracted historical results. [ABSTRACT FROM AUTHOR]

This article is a reply to a letter that raised concerns about a previous study. The authors clarify that their study was not intended to derive analytical performance specifications based on indirect outcomes. Instead, their supplementary tool aimed to show the impact of changes in bias and imprecision on the interpretation of laboratory results. The authors also address concerns about the presentation and interpretation of simulation results, noting that different measurands, interpretation thresholds, and statistical assessments can lead to differing conclusions. They emphasize the importance of careful and objective review to avoid drawing misguided interpretations and conclusions. [Extracted from the article]