Table 1 Bisection procedure to determine intercept of a logistic regression model to produce an outcome with a given prevalence (target prevalence: 0.10)
Iteration | Target outcome prevalence | \(\beta_{0}^{{{\text{midpoint}}}}\) | Empirical outcome prevalence |
|---|---|---|---|
1 | 0.1 | 0 | 0.729943 |
2 | 0.1 | -5 | 0.062318 |
3 | 0.1 | -2.5 | 0.313826 |
4 | 0.1 | -3.75 | 0.153365 |
5 | 0.1 | -4.375 | 0.099513 |
6 | 0.1 | -4.0625 | 0.124133 |
7 | 0.1 | -4.21875 | 0.111236 |
8 | 0.1 | -4.29688 | 0.105887 |
9 | 0.1 | -4.33594 | 0.102487 |
10 | 0.1 | -4.35547 | 0.100863 |
11 | 0.1 | -4.36523 | 0.100523 |
12 | 0.1 | -4.37012 | 0.099575 |
13 | 0.1 | -4.36768 | 0.100366 |
14 | 0.1 | -4.3689 | 0.099923 |