New release: mapbayr 0.10.0 is on CRAN! https://t.co/wrSTxRdDKt
Define your model in mrgsolve and make Maximum A Posteriori Bayesian estimation of individual parameters. This new version comes with useful features to help you in your Model-Informed Precision Dosing projects.
Researchers from @MSKCancerCenter & InsightRX collaborated on a recent study that proposed a novel hybrid #pharmacokinetic / #pharmacodynamic /machine learning approach, applied to predict absolute neutrophil counts during chemotherapy: https://t.co/yKMA9D81ej | @ASCPT_ClinPharm
The package mapbayr is developed on github where suggestions, issues and any input from the Pharmacometrics community are welcome. Feel free to ask any question about your project! https://t.co/lN4AhhN1jl
New release: mapbayr 0.10.0 is on CRAN! https://t.co/wrSTxRdDKt
Define your model in mrgsolve and make Maximum A Posteriori Bayesian estimation of individual parameters. This new version comes with useful features to help you in your Model-Informed Precision Dosing projects.
2) Prediction-corrected Visual Predictive Checks. Overlap the observations from your dataset with stochastic simulations from your model and identify if the latter is coded correctly and/or suitable for MIPD. Just call `mapbayr_vpc()`. More options in the documentation.
Corruption attempt on my person with Hex Stickers from @mattfidler at the #PAGEmeeting… Sorry but I think I will stick with my NONMEM / mrgsolve / mapbayr framework 🤓. Still, kudos to the @nlmixr team for their amazing job 👍
Really happy to have my presentation accepted for #positconf2023. I'll be talking about open-source in pharma and how things in clinical trial reporting have changed over the last 30 years...
Code a model and simulate in mrgsolve --> MAP-estimate with mapbayr (https://t.co/lN4AhhN1jl) --> correct the bias with ML infrastructure (i.e. xgboost/tidymodels).
Our colleagues from the @Woillard4 team (@CHULimoges) just published a nice paper in Pharmaceutical Research about combining Machine Learning and classical MAP Bayesian analyses in the context of dose individualization of isavuconazole.
https://t.co/GXytf0cIPg
Of note, they used mapbayr to obtain MAP estimates of PK parameters, from simulated data in mrgsolve. It is a nice example of streamlined analyses fully performed in R, and should be inspiring for future researches on improving model-informed precision dosing practices!
New publication in @BritJClinPharm! In this study @florent_puisset reports the impact of dose-banding of paclitaxel on clinical outcomes.
Cytotoxic drugs are often dosed in mg/m², but the relationship between body surface area and clearance is not so close.
The benefits are numerous: easer, cheaper and safer to prepare etc... And in our study, we show that there is not more toxicity in patients for whom the dose was rounded up, and same efficacy for for whom the dose was rounded down.👍
mapbayr 0.9.0 is now on CRAN! https://t.co/r2332MMl6A
Define your model in @mrgsolve and perform MAP-Bayesian estimations. Many nice new features to help you in your model-informed precision dosing projects! #rstats
5) Select the ETAs to estimate thanks to `mapbayest(select_eta = ...)`. Run times increase exponentially with the number of ETAs to estimate, so it is sometimes useful to ignore non-identifiable ETA or if your model have a lot of OMEGA due to inter-occasion.