Set up: I have a epidemiological study with a dose-response curve with a series of relative risk estimates (risk ratio of mortality risk exposed compared to mortality risk unexposed) along a curve. The curve only has a few points, but I am using it to model a continuum of points in a health impact assessment using a piecewise linear regression between each pair of points. For each slope, I have two sets of confidence intervals (point a and point b) and I need to combine them to make a unified 95% CI lower and upper bound slope. To do this, I am trying to run a monte carlo analysis with 1000+ iterations to model the distribution of slopes and then select the mean, lower, and upper bound slopes to apply to my analysis.
Issue: in the epi study, I only have the central, upper and lower bound RR estimate for a given exposure level but I need to calculate the SD to run the monte carlo analysis. I do not know how many observations were included in the study for a given point on the RR curve.
Question: is it possible to calculate the SD of RR for a point on the curve using only the mean and 95% CI? All the answers to this question have recommended using the approach found here, with requires an input for N. https://handbook-5-1.cochrane.org/chapter_7/7_7_3_2_obtaining_standard_deviations_from_standard_errors_and.htm#:~:text=The%20standard%20deviation%20for%20each,should%20be%20replaced%20by%205.15.
Just to make a concrete example, let's just say there are two points on the relative risk curve and I want to monte carlo model a distribution of slopes between these two points based on the mean and CIs. If that's the data I have, how can I estimate the SD?
At exposure = 10, 0.90 with a symmetrical 95% CI of (0.87, 0.94).
At exposure = 20; 0.95 with a symmetrical 95% CI of (0.90, 1.00).
Thank you in advance!