Assuming (zero and) negative parameter values are not allowed, you =
could change from e.g. a linear model to a power model, which is as =
close as possible to the linear model, in the range of covariate values =
from the original publication.
If the publication lists e.g. median, mean and 95% CI of the covariate =
values (maybe this is hoping for too much?), then you can generate e.g. =
a normal or log-normal distribution of covariate values that reflect =
these statistics as closely as possible.
Then you can optimize the power model to resemble the linear model as =
closely as possible on these covariate-parameter data.
Jakob Ribbing, Ph.D.
Senior Consultant, Pharmetheus AB
Cell/Mobile: +46 (0)70 514 33 77
Phone, Office: +46 (0)18 513 328
Uppsala Science Park, Dag Hammarskjölds väg 52B
SE-752 37 Uppsala, Sweden
This communication is confidential and is only intended for the use of =
the individual or entity to which it is directed. It may contain =
information that is privileged and exempt from disclosure under =
applicable law. If you are not the intended recipient please notify us =
immediately. Please do not copy it or disclose its contents to any other =
Received on Fri Apr 13 2018 - 12:04:10 EDT