Comments on the importance of visualizing the distribution of pain‐related data

Lötsch, Jörn; Ultsch, Alfred

doi:10.1002/ejp.2135

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…For example, in the analysis of right-skewed distributions observed in the analysis of social dynamics, the median can often be more misleading than the mean [36], and such scenarios are also not excluded in biomedical data. Above all, this simple example emphasizes that careful data exploration during preprocessing including the adequate visualization of raw data [37], as an essential part of the omics data analysis workflow, cannot be replaced by an unquestioned standard procedure rigidly applied to the data at hand.…”

Section: Discussionmentioning

confidence: 99%

“…The geometric mean is usually appropriate for lognormal data, while the median can be misleading, especially for right-skewed distributions [36]. This emphasizes the importance of careful data exploration during preprocessing, including adequate visualization of raw data [37], as an essential part of the omics data analysis workflow that cannot be replaced by unquestioned standard procedures.…”

Section: Discussionmentioning

confidence: 99%

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Revisiting Fold-Change Calculation: Preference for Median or Geometric Mean over Arithmetic Mean-Based Methods

Lötsch,

Kringel,

Ultsch

2024

Preprint

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Background: Fold change is widely used in biomedical research to quantify the magnitude of group differences in omics variables. However, the exact calculation method is often not reported, leading to inconsistent results. This study re-evaluates different fold-change calculation methods and provides a clear preference. Methods: Data scenarios with different distributions of treatment/test and reference data were created to challenge the assumption of interchangeable calculation methods. The main difference lies in the definition of the expected values of the groups used to calculate the log ratio, which is the basis of the fold change calculation. In addition, a multi-omics biomedical dataset was analyzed. Results: Using the arithmetic mean as the expected value for the treatment and reference groups resulted in incorrect fold change values more frequently than other methods, especially when the standard deviations between subgroups differed widely. Conclusions: The inferior arithmetic mean method is often perceived as the standard, although mathematically different equations are possible that differ mainly in the estimation of the expected value. Alternatives that define expectation by median, geometric mean, or paired fold change combinations are less susceptible to violations of equal variances or similar treatment/reference distributions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Revisiting Fold-Change Calculation: Preference for Median or Geometric Mean over Arithmetic Mean-Based Methods

Lötsch,

Kringel,

Ultsch

2024

Preprint

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Artificial intelligence and machine learning in clinical pharmacological research

Mayer,

Kringel,

Lötsch

2024

Expert Review of Clinical Pharmacology

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Diagnosed and subjectively perceived long-term effects of COVID-19 infection on olfactory function assessed by supervised machine learning

Lötsch,

Brosig,

Slobodova

et al. 2024

Chemical Senses

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Loss of olfactory function is a typical acute COVID-19 symptom, at least in early variants of SARS-CoV2. The time that has elapsed since the emergence of COVID-19 now allows assessing the long-term prognosis of its olfactory impact. Participants (n = 722) of whom n = 464 reported having had COVID-19 dating back with a mode of 174 days) were approached in a museum as a relatively unbiased environment. Olfactory function was diagnosed by assessing odor threshold and odor identification performance. Subjects also rated their actual olfactory function on an 11-point numerical scale [0,…10]. Neither the frequency of olfactory diagnostic categories nor olfactory test scores showed any COVID-19-related effects. Olfactory diagnostic categories (anosmia, hyposmia, or normosmia) were similarly distributed among former patients and controls (0.86%, 18.97%, and 80.17% for former patients, and 1.17%, 17.51%, and 81.32% for controls). Former COVID-19 patients, however, showed differences in their subjective perception of their own olfactory function. The impact of this effect was substantial enough that supervised machine learning algorithms detected past COVID-19 infections in new subjects, based on reduced self-awareness of olfactory performance and parosmia, while the diagnosed olfactory function did not contribute any relevant information in this context. Based on diagnosed olfactory function, results suggest a positive prognosis for COVID-19-related olfactory loss in the long term. Traces of former infection are found in self-perceptions of olfaction, highlighting the importance of investigating the long-term effects of COVID-19 using reliable and validated diagnostic measures in olfactory testing.

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Comments on the importance of visualizing the distribution of pain‐related data

Cited by 6 publications

References 12 publications

Revisiting Fold-Change Calculation: Preference for Median or Geometric Mean over Arithmetic Mean-Based Methods

Revisiting Fold-Change Calculation: Preference for Median or Geometric Mean over Arithmetic Mean-Based Methods

Artificial intelligence and machine learning in clinical pharmacological research

Diagnosed and subjectively perceived long-term effects of COVID-19 infection on olfactory function assessed by supervised machine learning

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