The evidence of Bayesian A/B testing in the contrast of clinical events by COVID-19
The clinical investigations reported in this journal employ the standard framework of frequentist statistics based on signifi-cance assumptions (p < 0.05). This method leads to a dicho-tomization of the results as “significant” or “non-significant” requiring the evaluation of statistical hypotheses1. Therefore, the use of the Bayesian approach is important as an improved way of drawing statistical conclusions from clinical data since it facilitates the answer to the question, what is the proba-bility that the effect is conclusive based on the data, which provides greater validity to the significant conclusions. One of the best known methods is the Bayes factor (FB), which esti-mates the probability of one hypothesis relative to the other given the data (e.g., null hypothesis vs alternate hypothesis)1,2, this allows estimation of the weight of evidence (10 times the decimal logarithm value of the FB)3,4, useful for decision ma-king of significant findings, where results with evidence values greater than 20 are optimal for clinical decision making..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:26 |
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| Enthalten in: |
Infectio - 26(2021), 1, Seite 99-100 |
| Sprache: |
Englisch ; Spanisch |
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| Beteiligte Personen: |
Cristian Ramos-Vera [VerfasserIn] |
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| Links: |
doi.org [kostenfrei] |
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| Themen: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
DOAJ023501774 |
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| 520 | |a The clinical investigations reported in this journal employ the standard framework of frequentist statistics based on signifi-cance assumptions (p < 0.05). This method leads to a dicho-tomization of the results as “significant” or “non-significant” requiring the evaluation of statistical hypotheses1. Therefore, the use of the Bayesian approach is important as an improved way of drawing statistical conclusions from clinical data since it facilitates the answer to the question, what is the proba-bility that the effect is conclusive based on the data, which provides greater validity to the significant conclusions. One of the best known methods is the Bayes factor (FB), which esti-mates the probability of one hypothesis relative to the other given the data (e.g., null hypothesis vs alternate hypothesis)1,2, this allows estimation of the weight of evidence (10 times the decimal logarithm value of the FB)3,4, useful for decision ma-king of significant findings, where results with evidence values greater than 20 are optimal for clinical decision making. | ||
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