Bayesian interpretation of p values in clinical trials
© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ..
Commonly accepted statistical advice dictates that large-sample size and highly powered clinical trials generate more reliable evidence than trials with smaller sample sizes. This advice is generally sound: treatment effect estimates from larger trials tend to be more accurate, as witnessed by tighter confidence intervals in addition to reduced publication biases. Consider then two clinical trials testing the same treatment which result in the same p values, the trials being identical apart from differences in sample size. Assuming statistical significance, one might at first suspect that the larger trial offers stronger evidence that the treatment in question is truly effective. Yet, often precisely the opposite will be true. Here, we illustrate and explain this somewhat counterintuitive result and suggest some ramifications regarding interpretation and analysis of clinical trial results.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:27 |
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| Enthalten in: |
BMJ evidence-based medicine - 27(2022), 5 vom: 23. Okt., Seite 313-316 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ferguson, John [VerfasserIn] |
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| Anmerkungen: |
Date Completed 26.09.2022 Date Revised 12.10.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1136/bmjebm-2020-111603 |
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| funding: |
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| 520 | |a © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ. | ||
| 520 | |a Commonly accepted statistical advice dictates that large-sample size and highly powered clinical trials generate more reliable evidence than trials with smaller sample sizes. This advice is generally sound: treatment effect estimates from larger trials tend to be more accurate, as witnessed by tighter confidence intervals in addition to reduced publication biases. Consider then two clinical trials testing the same treatment which result in the same p values, the trials being identical apart from differences in sample size. Assuming statistical significance, one might at first suspect that the larger trial offers stronger evidence that the treatment in question is truly effective. Yet, often precisely the opposite will be true. Here, we illustrate and explain this somewhat counterintuitive result and suggest some ramifications regarding interpretation and analysis of clinical trial results | ||
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