Table 3 |
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Change in prior probabilities of cafestol not affecting serum cholesterol to posterior probabilities using data of the present study and Bayesian analysis |
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| Prior probability |
Prior odds (Yes/No) |
Posterior odds |
Posterior probability |
|
|
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| 0.90 (very strong) |
0.9/(1-0.9) = 9 |
9x Bayes factor = 3.22 |
3.22/(1+3.22) = 0.76 |
| 0.75 (strong) |
0.75/(1-0.75) = 3 |
3x Bayes factor = 1.07 |
1.07/(1+1.07) = 0.52 |
| 0.50 (equivocal) |
0.50/(1-0.50)= 1 |
1x Bayes factor = 0.36 |
0.36/(1+1.07) = 0.26 |
| 0.25 (weak) |
0.25/(1-0.25) = 0.33 |
0.33x Bayes factor = 0.12 |
0.12/(1+0.12) = 0.11 |
| 0.10 (very weak) |
0.10/(1-0.10) = 0.11 |
0.11x Bayes factor = 0.04 |
0.04(1+0.04) = 0.04 |
|
|
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A priori probabilities were converted to a priori odds and multiplied by the minimum Bayes factor*. The obtained a postiori odds were converted to a postiori probabilities. *Bayes factor = e to the power -Z2/2, where Z is the Z-score corresponding to the P-value for obtaining an effect of 0.27 mmol/l under the null hypothesis. P-value = 0.15, Z-score = 1.43 the minimum Bayes factor = 0.36 |
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Boekschoten et al. Nutrition Journal 2004 3:7 doi:10.1186/1475-2891-3-7 |
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