RXR-2394: support estimation of lagtime #51
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roninsightrx
commented
Jul 8, 2025
| t_init = 0, | ||
| calc_ofv, | ||
| regimen, | ||
| lagtime = c(0), |
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This is the default for PKPDsim. It needs to be non-NULL. If we pass a NULL value it will also be converted to c(0) in PKPDsim.
| @@ -0,0 +1,10 @@ | |||
| mod_1cmt_oral_lagtime <- PKPDsim::new_ode_model( | |||
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moved this out of the single test, so it can be re-used.
| ## check that fit works for vector of lagtimes | ||
| lagtimes <- c(0.25, 0.5, 0.75, 1.0, 2.0) | ||
| est <- c() | ||
| for(i in seq(lagtimes)) { |
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loop over a range of lagtime estimates (true value is 0.83). Should give estimate for all values. This in contrast to the default optimization method (BGFS) which will error for values lower than ~0.85.
jasmineirx
approved these changes
Jul 8, 2025
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Previously, it was not possible to estimate lagtime as a parameter, since it was incorporated in the static event tables that were input to PKPDsim. We have now pulled this out of the event table and it is now handled dynamically within PKPDsim's core function. In principle, it requires just a few minor updates to PKPDmap to support estimation of the parameter: just passing the lagtime vector as argument.
However, it does introduce a second problem we need to solve, in that lagtimes are step-functions and hence introduce inconcistencies that make estimation of the parameter difficult / impossible with gradient-based optimization methods like BFGS. In practice, if the initial estimate of lagtime is lower than the data suggests, it will never be able to recover because the initial gradients are zero for TLAG. The issue can however be solved fairly easily by switching to a non-gradient-based optimization method like Nelder-Mead. I've chosen not to make this a forced or automatic switch, but to leave it to the user. I have left some info in documentation and code comments.
Related PR in PKPDsim
CI WILL ONLY PASS AFTER THE PKPDsim PR HAS BEEN MERGED