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Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies
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| Zeitschriftentitel: | JNCI: Journal of the National Cancer Institute |
|---|---|
| Personen und Körperschaften: | , , , , |
| In: | JNCI: Journal of the National Cancer Institute, 96, 2004, 6, S. 434-442 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
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| Schlagwörter: |
| author_facet |
Wacholder, Sholom Chanock, Stephen Garcia-Closas, Montserrat El ghormli, Laure Rothman, Nathaniel Wacholder, Sholom Chanock, Stephen Garcia-Closas, Montserrat El ghormli, Laure Rothman, Nathaniel |
|---|---|
| author |
Wacholder, Sholom Chanock, Stephen Garcia-Closas, Montserrat El ghormli, Laure Rothman, Nathaniel |
| spellingShingle |
Wacholder, Sholom Chanock, Stephen Garcia-Closas, Montserrat El ghormli, Laure Rothman, Nathaniel JNCI: Journal of the National Cancer Institute Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies Cancer Research Oncology |
| author_sort |
wacholder, sholom |
| spelling |
Wacholder, Sholom Chanock, Stephen Garcia-Closas, Montserrat El ghormli, Laure Rothman, Nathaniel 1460-2105 0027-8874 Oxford University Press (OUP) Cancer Research Oncology http://dx.doi.org/10.1093/jnci/djh075 <jats:title>Abstract</jats:title><jats:p>Too many reports of associations between genetic variants and common cancer sites and other complex diseases are false positives. A major reason for this unfortunate situation is the strategy of declaring statistical significance based on a P value alone, particularly, any P value below .05. The false positive report probability (FPRP), the probability of no true association between a genetic variant and disease given a statistically significant finding, depends not only on the observed P value but also on both the prior probability that the association between the genetic variant and the disease is real and the statistical power of the test. In this commentary, we show how to assess the FPRP and how to use it to decide whether a finding is deserving of attention or “noteworthy.” We show how this approach can lead to improvements in the design, analysis, and interpretation of molecular epidemiology studies. Our proposal can help investigators, editors, and readers of research articles to protect themselves from overinterpreting statistically significant findings that are not likely to signify a true association. An FPRP-based criterion for deciding whether to call a finding noteworthy formalizes the process already used informally by investigators—that is, tempering enthusiasm for remarkable study findings with considerations of plausibility.</jats:p> Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies JNCI: Journal of the National Cancer Institute |
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| title |
Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies |
| title_unstemmed |
Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies |
| title_full |
Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies |
| title_fullStr |
Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies |
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Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies |
| title_short |
Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies |
| title_sort |
assessing the probability that a positive report is false: an approach for molecular epidemiology studies |
| topic |
Cancer Research Oncology |
| url |
http://dx.doi.org/10.1093/jnci/djh075 |
| publishDate |
2004 |
| physical |
434-442 |
| description |
<jats:title>Abstract</jats:title><jats:p>Too many reports of associations between genetic variants and common cancer sites and other complex diseases are false positives. A major reason for this unfortunate situation is the strategy of declaring statistical significance based on a P value alone, particularly, any P value below .05. The false positive report probability (FPRP), the probability of no true association between a genetic variant and disease given a statistically significant finding, depends not only on the observed P value but also on both the prior probability that the association between the genetic variant and the disease is real and the statistical power of the test. In this commentary, we show how to assess the FPRP and how to use it to decide whether a finding is deserving of attention or “noteworthy.” We show how this approach can lead to improvements in the design, analysis, and interpretation of molecular epidemiology studies. Our proposal can help investigators, editors, and readers of research articles to protect themselves from overinterpreting statistically significant findings that are not likely to signify a true association. An FPRP-based criterion for deciding whether to call a finding noteworthy formalizes the process already used informally by investigators—that is, tempering enthusiasm for remarkable study findings with considerations of plausibility.</jats:p> |
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| author | Wacholder, Sholom, Chanock, Stephen, Garcia-Closas, Montserrat, El ghormli, Laure, Rothman, Nathaniel |
| author_facet | Wacholder, Sholom, Chanock, Stephen, Garcia-Closas, Montserrat, El ghormli, Laure, Rothman, Nathaniel, Wacholder, Sholom, Chanock, Stephen, Garcia-Closas, Montserrat, El ghormli, Laure, Rothman, Nathaniel |
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| description | <jats:title>Abstract</jats:title><jats:p>Too many reports of associations between genetic variants and common cancer sites and other complex diseases are false positives. A major reason for this unfortunate situation is the strategy of declaring statistical significance based on a P value alone, particularly, any P value below .05. The false positive report probability (FPRP), the probability of no true association between a genetic variant and disease given a statistically significant finding, depends not only on the observed P value but also on both the prior probability that the association between the genetic variant and the disease is real and the statistical power of the test. In this commentary, we show how to assess the FPRP and how to use it to decide whether a finding is deserving of attention or “noteworthy.” We show how this approach can lead to improvements in the design, analysis, and interpretation of molecular epidemiology studies. Our proposal can help investigators, editors, and readers of research articles to protect themselves from overinterpreting statistically significant findings that are not likely to signify a true association. An FPRP-based criterion for deciding whether to call a finding noteworthy formalizes the process already used informally by investigators—that is, tempering enthusiasm for remarkable study findings with considerations of plausibility.</jats:p> |
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| spelling | Wacholder, Sholom Chanock, Stephen Garcia-Closas, Montserrat El ghormli, Laure Rothman, Nathaniel 1460-2105 0027-8874 Oxford University Press (OUP) Cancer Research Oncology http://dx.doi.org/10.1093/jnci/djh075 <jats:title>Abstract</jats:title><jats:p>Too many reports of associations between genetic variants and common cancer sites and other complex diseases are false positives. A major reason for this unfortunate situation is the strategy of declaring statistical significance based on a P value alone, particularly, any P value below .05. The false positive report probability (FPRP), the probability of no true association between a genetic variant and disease given a statistically significant finding, depends not only on the observed P value but also on both the prior probability that the association between the genetic variant and the disease is real and the statistical power of the test. In this commentary, we show how to assess the FPRP and how to use it to decide whether a finding is deserving of attention or “noteworthy.” We show how this approach can lead to improvements in the design, analysis, and interpretation of molecular epidemiology studies. Our proposal can help investigators, editors, and readers of research articles to protect themselves from overinterpreting statistically significant findings that are not likely to signify a true association. An FPRP-based criterion for deciding whether to call a finding noteworthy formalizes the process already used informally by investigators—that is, tempering enthusiasm for remarkable study findings with considerations of plausibility.</jats:p> Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies JNCI: Journal of the National Cancer Institute |
| spellingShingle | Wacholder, Sholom, Chanock, Stephen, Garcia-Closas, Montserrat, El ghormli, Laure, Rothman, Nathaniel, JNCI: Journal of the National Cancer Institute, Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies, Cancer Research, Oncology |
| title | Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies |
| title_full | Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies |
| title_fullStr | Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies |
| title_full_unstemmed | Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies |
| title_short | Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies |
| title_sort | assessing the probability that a positive report is false: an approach for molecular epidemiology studies |
| title_unstemmed | Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies |
| topic | Cancer Research, Oncology |
| url | http://dx.doi.org/10.1093/jnci/djh075 |