Re: W for time varying covariates

Dear Bernard, The user guide for scm seems to indicate that for the functionality of time-varying covariates, you need to estimate the standard deviation of the residual error as THETAS(s) (with SIGMAS(s) fixed to 1): https://github.com/UUPharmacometrics/PsN/releases/download/4.7.0/scm_userguide.pdf (see option “time_varying") One more thing, before you switch to estimating the magnitude of the residual error as a fixed effect: notice that W should represent the standard deviation of the residual error (IRES in my example code below), and SIGMA(1,1) would generally represent the variance. Below, I provide the code that I would use for the additive error with SIGMA estimated. (the code with SIGMA fixed is provided in the scm user guide, under time_varying). Best wishes Jakob PS. For other users, it should be noted that for covariates that are mostly varying between individuals, and just slightly over time (e.g. WT in adults), the option -time_varying in scm will have little or no impact. But in this case, if another covariates is mostly varying over time, one may as well include WT among the ones listed as time varying (assuming WT was measured multiple times). DS. Example code for estimating additive error model with sigma estimated. IPRED = [Model specific equation, or F] IRES = DV-IPRED ADD = SQRT(SIGMA(1,1)) SD = ADD IWRES = IRES/SD Y = IPRED + EPS(1) Jakob Ribbing, Ph.D. Senior Consultant, Pharmetheus AB Cell/Mobile: +46 (0)70 514 33 77 [email protected] 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.