Table 2.
c1 | s1 |
r = 1.0 |
r = 1.5 |
r = 2.0 |
|||
RD | 95% BCIb | RD | 95% BCI | RD | 95% BCI | ||
0.00 | 0.00 | 0.20 | 0.12, 0.30 | 0.20 | 0.12, 0.30 | 0.20 | 0.12, 0.30 |
0.05 | 0.18 | 0.10, 0.28 | 0.18 | 0.10, 0.28 | 0.17 | 0.09, 0.28 | |
0.10 | 0.16 | 0.08, 0.27 | 0.15 | 0.07, 0.26 | 0.14 | 0.06, 0.25 | |
0.20 | 0.12 | 0.05, 0.23 | 0.10 | 0.03, 0.22 | 0.08 | 0.01, 0.20 | |
0.30 | 0.08 | 0.01, 0.20 | 0.05 | −0.02, 0.18 | 0.02 | −0.05, 0.15 | |
0.05 | 0.00 | 0.16 | 0.08, 0.27 | 0.15 | 0.07, 0.26 | 0.14 | 0.07, 0.25 |
0.05 | 0.14 | 0.06, 0.25 | 0.13 | 0.05, 0.24 | 0.11 | 0.04, 0.22 | |
0.15 | 0.10 | 0.03, 0.22 | 0.08 | 0.00, 0.20 | 0.05 | −0.02, 0.17 | |
0.25 | 0.06 | −0.01, 0.18 | 0.03 | −0.05, 0.15 | −0.01 | −0.08, 0.12 | |
0.10 | 0.00 | 0.12 | 0.05, 0.23 | 0.10 | 0.03, 0.22 | 0.08 | 0.01, 0.20 |
0.10 | 0.08 | 0.01, 0.20 | 0.05 | −0.02, 0.17 | 0.02 | −0.05, 0.15 | |
0.20 | 0.04 | −0.03, 0.16 | 0.00 | −0.07, 0.13 | −0.04 | −0.11, 0.10 | |
0.20 | 0.00 | 0.04 | −0.03, 0.16 | 0.00 | −0.07, 0.13 | −0.04 | −0.11, 0.09 |
0.10 | 0.00 | −0.07, 0.13 | −0.05 | −0.12, 0.09 | −0.10 | −0.17, 0.04 | |
0.30 | 0.00 | −0.05 | −0.10, 0.10 | −0.10 | −0.16, 0.05 | −0.16 | −0.23, −0.01 |
Abbreviations: BCI, bootstrap confidence interval; RD, risk difference.
The authors assumed that the sensitivity functions followed linear structures, such that c(z = 1, e) = c1 + s1e and c(z = 0, e) = r × (z = 1, e).
The 95% bootstrap confidence intervals were obtained using the 2.5% and 97.5% percentiles among the 1,000 bootstrap replications.