Björn Holzhauer

18 Sept

Observations on driving around 800 km (and back) with our electric estate (station wagon) for the summer holidays (mostly through France) The good: 1. Young kids need more breaks than the battery. 2. Charging time is about right for a food and playground break.

18 Sept

3. Only had to wait for charger once (kind of foreseeable: gap in chargers after that, so everyone wanted to stop there). The bad: 4. Charger user interfaces are terrible: confusing menus, unclear instructions, screens hard to see in sunlight. 5. Called support 2x to detach (at least that worked).

18 Sept

I hope/expect all of that will just get even better with time. Disclaimers: Didn't go on most popular travel day, so there's probably days in the year where chargers are busier. Of course, two adult drivers without kids taking turns would have fewer/shorter breaks with a petrol car.

opensource.nibr.comApplied Modelling in Drug Development

We’ve just added new materials to Applied Modelling in Drug Development, our collection of drug-development case studies exploring what you can do with the brms R package: opensource.nibr.com/bamdd

opensource.nibr.com19 Robust borrowing in the meta-analytic combined approach – Applied Modelling in Drug Development

I wrote up how to do the robust meta-analytic combined borrowing approach from my PhD. Back then I wrote custom Stan code, but brms makes this super quick. opensource.nibr.com/bamdd/src/02...

opensource.nibr.com10 Dose finding – Applied Modelling in Drug Development

We also updated the dose finding chapter with the priors for the sigmoid Emax model proposed by Pfizer researchers informed by historical data. FDA classified their dosing approach to be a fit-for-purpose drug development tool. opensource.nibr.com/bamdd/src/02...

opensource.nibr.comApplied Modelling in Drug Development

We’ve just added new materials to Applied Modelling in Drug Development, our collection of drug-development case studies exploring what you can do with the brms R package: opensource.nibr.com/bamdd

opensource.nibr.com15 Bayesian Mixed effects Model for Repeated Measures – Applied Modelling in Drug Development

My colleague Sebastian Weber expanded the Bayesian MMRM article with a section on turning forward differences into a random walk over visits. Kind of budget-GPs. opensource.nibr.com/bamdd/src/02...

github.comGitHub - seantalts/stanli: stanli, the Stan Language Interpreter: op-graph executor over precompiled stan-math kernels. Compile and sample Stan models with no C++ toolchain.stanli, the Stan Language Interpreter: op-graph executor over precompiled stan-math kernels. Compile and sample Stan models with no C++ toolchain. - seantalts/stanli

29 Aug

I was stunned when a colleague showed me stanli. One thing that regularly slows down my Bayesian work: waiting longer for C++ compilation than for the sampling when fitting a simple one-off brms/Stan model on a small dataset. github.com/seantalts/st...

github.comGitHub - andrjohns/brms at stanr-stanlibrms R package for Bayesian generalized multivariate non-linear multilevel models using Stan - andrjohns/brms

29 Aug

stanli precompiles the vocabulary of operations one needs to create a model. I’ve tried the draft brms integration branch (github.com/andrjohns/br...) & it is amazing, so far. Basically your MCMC sampling starts right away with the same speed and quality you're used to from Stan.

How much does covariate adjustment usually help in cardiovascular outcome trials (CVOTs)? On a bunch of past trials adjustment for existing risk scores/known prognostic factors did not have much of an effect. It’s great to see this work finally out. doi.org/10.1007/s434...

doi.orgClient Challenge

doi.orgCovariate-adjusted log-rank test: guaranteed efficiency gain and universal applicabilitySummary. Nonparametric covariate adjustment is considered for log-rank-type tests of the treatment effect with right-censored time-to-event data from clini

With the marginal estimator of Ye et al. 2024 (using the RobinCar2 R package) the hazard ratios stayed on average the same as the covariates get more prognostic, but the estimates become more variable while the standard errors got narrower (just as expected). doi.org/10.1093/biom...

doi.orgThe type I error and power of non‐parametric logrank and Wilcoxon tests with adjustment for covariates—A simulation studyTime-to-event outcomes are common for oncology clinical trials. Conventional methods of analysis for these endpoints include logrank or Wilcoxon tests for treatment group comparisons, Kaplan–Meier su...

That’s interesting, because Ye et al.'s method is also robust to mis-specified covariate relationships (but not to censoring depending on prognostic covariates). In contrast, conditional Cox reg. has problems at least in the pretty extreme scenarios of Jiang et al. 2008. doi.org/10.1002/sim....

doi.orgClient Challenge

Using the idea of the 2023 super-covariates paper, we also trained our own tailored prognostic models, but it didn’t make a huge difference. doi.org/10.1002/pst....

On average, more favourable hazard ratios outweigh the wider standard error in conditional Cox regression, but with simulations we illustrated that you’d need much stronger prognostic covariates to get consistently better results.

doi.orgClient Challenge

Looking into the why, it’s because these prognostic covariates were not very strongly associated with the CV outcomes + only a low proportion of patients experience an event (yes, that has an effect). That’s often the case for CVOTs.

I just had my first top 1% finish (24/2639) in a Kaggle (tabular playground series) competition. Accuracy is such a noisy low information competition metric (even with 10,000s observations) that you have to be very careful to not overfit the out-of-fold observations of your cross-validation.

My selected solution was a simple average of multiple CatBoost, LightGBM using target encoding for categories, logistic regressions & seed averaged fastai NNs with embeddings of dim 1-4 for all features (numeric ones had low cardinality) and 2 small hidden layers (10 & 5) trained with focal loss.

I find these tabular competitions a useful learning tool (tabular data is what I'm dealing with most of the time, just usually much smaller). E.g. I learnt a lot on the internals of CatBoost. I also should write up my thoughts on tuning GBDTs (e.g. don't tune the learning rate, lower is better).

I think it's generally a good idea to not take the performance of early stopping per CV fold, but to rather take the best number of iterations (or epochs) averaging across folds. It's particularly so with such a noisy low information metric, so that was an important part of my solution.

Without naming the company, this is disappointing. Company's webpage:"We are committed to disclosing the results of clinical trials on all applicable registries in accordance with applicable law." + "This clinical trial is now complete. When available, results will be posted on ClinicalTrials.gov."

accessdata.fda.govDrugs@FDA: FDA-Approved Drugs

Maybe the results are just not available, yet? Thanks to Drugs@FDA (www.accessdata.fda.gov/scripts/cder...), I finally found the results in the clinical pharmacology review for the original drug approval (10+ years ago)...

I mean, sure, this clinical trial was conducted long enough ago, that the company is not legally required to report the results. Still, it feels disappointing that it's so hard to find the outcomes for a large(ish) trial on a widely used drug.

Meanwhile on clinicaltrials.gov the trial still doesn't have results 19 years after being completed. As far as I can tell no results from it have been published in a medical journal, either.

Is there something more clever than what the directlabels R package offers? The style of plot like in this hypothetical example (where geom_dl(method="smart.grid") worked well) is so useful. Yet, all too often it struggles to place labels well & I end up using geom_text manually.

graphicsprinciples.github.iographics principles - WelcomeThis is the home page for effective visual communication and good graphical principles for quantitative scientists.

Annotating on the plot rather than making people go looking forth and back to a legend is such a nice way of making your graphics easier to read. In combination with some of the other graphics principles on graphicsprinciples.github.io like well chosen colors (vs. different dashed lines), ...

doi.orgHow can we make better graphs? An initiative to increase the graphical expertise and productivity of quantitative scientistsGraphics are at the core of exploring and understanding data, communicating results and conclusions, and supporting decision-making. Increasing our graphical expertise can significantly strengthen ou...

... minimal clutter/white background, and large enough font sizes already help so much. (see also also the article by some of my colleagues: doi.org/10.1002/pst....).