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Statistical analyses form a fundamental part of causal inference in the experimental sciences. The statistical paradigm most commonly taught to science students around the world is that of frequentism, with a particular emphasis on the null hypothesis significance testing borne by the work of Neyman and Pearson in the early 20th century. This paradigm is often lauded as being the most objective of methods and remains commonplace in scientific journals. Despite its widespread use-and, indeed, requirement for publication in some journals-this paradigm has received substantial criticism in recent decades, and its impact on scientific publishing has been subjected to deeper scrutiny in response to the replication crisis in the psychological and medical sciences. It has been posited that the increasing use of the Bayesian statistical paradigm, made more accessible through technological advances in the last few decades, may have an important role to play in rendering research and statistical inference more robust, transparent, and reproducible. These methods can have a steep learning curve, and thus this paper seeks to introduce those working within clinical laboratories to the Bayesian paradigm of statistical analysis and provides worked examples of the Bayesian analysis of data commonly encountered in laboratory medicine using freely available, open source tools., (© American Association for Clinical Chemistry 2022.)

Background: Plasma amino acid (PAA) profiles are used in routine clinical practice for the diagnosis and monitoring of inherited disorders of amino acid metabolism, organic acidemias, and urea cycle defects. Interpretation of PAA profiles is complex and requires substantial training and expertise to perform. Given previous demonstrations of the ability of machine learning (ML) algorithms to interpret complex clinical biochemistry data, we sought to determine if ML-derived classifiers could interpret PAA profiles with high predictive performance., Methods: We collected PAA profiling data routinely performed within a clinical biochemistry laboratory (2084 profiles) and developed decision support classifiers with several ML algorithms. We tested the generalization performance of each classifier using a nested cross-validation (CV) procedure and examined the effect of various subsampling, feature selection, and ensemble learning strategies., Results: The classifiers demonstrated excellent predictive performance, with the 3 ML algorithms tested producing comparable results. The best-performing ensemble binary classifier achieved a mean precision-recall (PR) AUC of 0.957 (95% CI 0.952, 0.962) and the best-performing ensemble multiclass classifier achieved a mean F4 score of 0.788 (0.773, 0.803)., Conclusions: This work builds upon previous demonstrations of the utility of ML-derived decision support tools in clinical biochemistry laboratories. Our findings suggest that, pending additional validation studies, such tools could potentially be used in routine clinical practice to streamline and aid the interpretation of PAA profiles. This would be particularly useful in laboratories with limited resources and large workloads. We provide the necessary code for other laboratories to develop their own decision support tools., (© American Association for Clinical Chemistry 2020. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Urine steroid profiles are used in clinical practice for the diagnosis and monitoring of disorders of steroidogenesis and adrenal pathologies. Machine learning (ML) algorithms are powerful computational tools used extensively for the recognition of patterns in large data sets. Here, we investigated the utility of various ML algorithms for the automated biochemical interpretation of urine steroid profiles to support current clinical practices., Methods: Data from 4619 urine steroid profiles processed between June 2012 and October 2016 were retrospectively collected. Of these, 1314 profiles were used to train and test various ML classifiers' abilities to differentiate between "No significant abnormality" and "?Abnormal" profiles. Further classifiers were trained and tested for their ability to predict the specific biochemical interpretation of the profiles., Results: The best performing binary classifier could predict the interpretation of No significant abnormality and ?Abnormal profiles with a mean area under the ROC curve of 0.955 (95% CI, 0.949-0.961). In addition, the best performing multiclass classifier could predict the individual abnormal profile interpretation with a mean balanced accuracy of 0.873 (0.865-0.880)., Conclusions: Here we have described the application of ML algorithms to the automated interpretation of urine steroid profiles. This provides a proof-of-concept application of ML algorithms to complex clinical laboratory data that has the potential to improve laboratory efficiency in a setting of limited staff resources., (© 2018 American Association for Clinical Chemistry.)