Dataset Information

Explaining computation of predictive values: 2 x 2 table versus frequency tree. A randomized controlled trial [ISRCTN74278823].

ABSTRACT:

Background

Involving patients in decision making on diagnostic procedures requires a basic level of statistical thinking. However, innumeracy is prevalent even among physicians. In medical teaching the 2 x 2 table is widely used as a visual help for computations whereas in psychology the frequency tree is favoured. We assumed that the 2 x 2 table is more suitable to support computations of predictive values.

Methods

184 students without prior statistical training were randomised either to a step-by-step self-learning tutorial using the 2 x 2 table (n = 94) or the frequency tree (n = 90). During the training session students were instructed by two sample tasks and a total of five positive predictive values had to be computed. During a follow-up session 4 weeks later participants had to compute 5 different tasks of comparable degree of difficulty without having the tutorial instructions at their disposal. The primary outcome was the correct solution of the tasks.

Results

There were no statistically significant differences between the two groups. About 58% achieved correct solutions in 4-5 tasks following the training session and 26% in the follow-up examination.

Conclusions

These findings do not support the hypothesis that the 2 x 2 table is more valuable to facilitate the calculation of positive predictive values than the frequency tree.

SUBMITTER: Steckelberg A

PROVIDER: S-EPMC514564 | biostudies-literature | 2004 Aug

REPOSITORIES: biostudies-literature

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Publications

Explaining computation of predictive values: 2 x 2 table versus frequency tree. A randomized controlled trial [ISRCTN74278823].

Steckelberg Anke A Balgenorth Andrea A Berger Jürgen J Mühlhauser Ingrid I

BMC medical education 20040810

<h4>Background</h4>Involving patients in decision making on diagnostic procedures requires a basic level of statistical thinking. However, innumeracy is prevalent even among physicians. In medical teaching the 2 x 2 table is widely used as a visual help for computations whereas in psychology the frequency tree is favoured. We assumed that the 2 x 2 table is more suitable to support computations of predictive values.<h4>Methods</h4>184 students without prior statistical training were randomised e ...[more]

PMID: 15301689

Similar Datasets

Project description:BackgroundMachine learning (ML) can be an effective tool to extract information from attribute-rich molecular datasets for the generation of molecular diagnostic tests. However, the way in which the resulting scores or classifications are produced from the input data may not be transparent. Algorithmic explainability or interpretability has become a focus of ML research. Shapley values, first introduced in game theory, can provide explanations of the result generated from a specific set of input data by a complex ML algorithm.MethodsFor a multivariate molecular diagnostic test in clinical use (the VeriStrat® test), we calculate and discuss the interpretation of exact Shapley values. We also employ some standard approximation techniques for Shapley value computation (local interpretable model-agnostic explanation (LIME) and Shapley Additive Explanations (SHAP) based methods) and compare the results with exact Shapley values.ResultsExact Shapley values calculated for data collected from a cohort of 256 patients showed that the relative importance of attributes for test classification varied by sample. While all eight features used in the VeriStrat® test contributed equally to classification for some samples, other samples showed more complex patterns of attribute importance for classification generation. Exact Shapley values and Shapley-based interaction metrics were able to provide interpretable classification explanations at the sample or patient level, while patient subgroups could be defined by comparing Shapley value profiles between patients. LIME and SHAP approximation approaches, even those seeking to include correlations between attributes, produced results that were quantitatively and, in some cases qualitatively, different from the exact Shapley values.ConclusionsShapley values can be used to determine the relative importance of input attributes to the result generated by a multivariate molecular diagnostic test for an individual sample or patient. Patient subgroups defined by Shapley value profiles may motivate translational research. However, correlations inherent in molecular data and the typically small ML training sets available for molecular diagnostic test development may cause some approximation methods to produce approximate Shapley values that differ both qualitatively and quantitatively from exact Shapley values. Hence, caution is advised when using approximate methods to evaluate Shapley explanations of the results of molecular diagnostic tests.

Dataset Information

Explaining computation of predictive values: 2 x 2 table versus frequency tree. A randomized controlled trial [ISRCTN74278823].

Background

Methods

Results

Conclusions

Publications

Explaining computation of predictive values: 2 x 2 table versus frequency tree. A randomized controlled trial [ISRCTN74278823].

Similar Datasets

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