Hi Tingjie, 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. Best wishes Jakob Jakob Ribbing, Ph.D. Senior Consultant, Pharmetheus AB Cell/Mobile: +46 (0)70 514 33 77 [email protected] www.pharmetheus.com <http://www.pharmetheus.com/> 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 person.
